Question: how does the coronavirus respond to changes in the weather

Title: On the global trends and spread of the COVID-19 outbreak: preliminary assessment of the potential relation between location-specific temperature and UV index Content: The novel coronavirus, since its first outbreak in December, has, up till now, affected approximately 114,542 people across 115 countries. Many international agencies are devoting efforts to enhance the understanding of the evolving COVID-19 outbreak on an international level, its influences, and preparedness. At present, COVID-19 appears to affect individuals through person-to-person means, like other commonly found cold or influenza viruses. It is widely known and acknowledged that viruses causing influenza peak during cold temperatures and gradually subside in the warmer temperature, owing to their seasonality. Thus, COVID-19, due to its regular flu-like symptoms, is also expected to show similar seasonality and subside as the global temperatures rise in the northern hemisphere with the onset of spring. Despite these speculations, however, the systematic analysis in the global perspective of the relation between COVID-19 spread and meteorological parameters is unavailable. Here, by analyzing the region- and city-specific affected global data and corresponding meteorological parameters, we show that there is an optimum range of temperature and UV index strongly affecting the spread and survival of the virus, whereas precipitation, relative humidity, cloud cover, etc. have no effect on the virus. Unavailability of pharmaceutical interventions would require greater preparedness and alert for the effective control of COVID-19. Under these conditions, the information provided here could be very helpful for the global community struggling to fight this global crisis. It is, however, important to note that the information presented here clearly lacks any physiological evidences, which may merit further investigation. Thus, any attempt for management, implementation, and evaluation strategies responding to the crisis arising due to the COVID-19 outbreak must not consider the evaluation presented here as the foremost factor.

Title: Air transportation, population density and temperature predict the spread of COVID-19 in Brazil Content: There is evidence that COVID-19, the disease caused by the betacoronavirus SARS-CoV-2, is sensitive to environmental conditions. However, such conditions often correlate with demographic and socioeconomic factors at larger spatial extents, which could confound this inference. We evaluated the effect of meteorological conditions (temperature, solar radiation, air humidity and precipitation) on 292 daily records of cumulative number of confirmed COVID-19 cases across the 27 Brazilian capital cities during the 1st month of the outbreak, while controlling for an indicator of the number of tests, the number of arriving flights, population density, proportion of elderly people and average income. Apart from increasing with time, the number of confirmed cases was mainly related to the number of arriving flights and population density, increasing with both factors. However, after accounting for these effects, the disease was shown to be temperature sensitive: there were more cases in colder cities and days, and cases accumulated faster at lower temperatures. Our best estimate indicates that a 1 °C increase in temperature has been associated with a decrease in confirmed cases of 8%. The quality of the data and unknowns limit the analysis, but the study reveals an urgent need to understand more about the environmental sensitivity of the disease to predict demands on health services in different regions and seasons.

Title: Seasonality and uncertainty in COVID-19 growth rates Content: The virus causing COVID-19 has spread rapidly worldwide and threatens millions of lives. It remains unknown if summer weather will reduce its continued spread, thereby alleviating strains on hospitals and providing time for vaccine development. Early insights from laboratory studies of related coronaviruses predicted that COVID-19 would decline at higher temperatures, humidity, and ultraviolet light. Using current, fine-scaled weather data and global reports of infection we developed a model that explained 36% of variation in early growth rates before intervention, with 17% based on weather or demography and 19% based on country-specific effects. We found that ultraviolet light was most strongly associated with lower COVID-19 growth rates. Projections suggest that, in the absence of intervention, COVID-19 will decrease temporarily during summer, rebound by autumn, and peak next winter. However, uncertainty remains high and the probability of a weekly doubling rate remained >20% throughout the summer in the absence of control. Consequently, aggressive policy interventions will likely be needed in spite of seasonal trends.

Title: Roles of meteorological conditions in COVID-19 transmission on a worldwide scale Content: The novel coronavirus (SARS-CoV-2/ 2019-nCoV) identified in Wuhan, China, in December 2019 has caused great damage to public health and economy worldwide with over 140,000 infected cases up to date. Previous research has suggested an involvement of meteorological conditions in the spread of droplet-mediated viral diseases, such as influenza. However, as for the recent novel coronavirus, few studies have discussed systematically about the role of daily weather in the epidemic transmission of the virus. Here, we examine the relationships of meteorological variables with the severity of the outbreak on a worldwide scale. The confirmed case counts, which indicates the severity of COVID-19 spread, and four meteorological variables, i.e., air temperature, relative humidity, wind speed, and visibility, were collected daily between January 20 and March 11 (52 days) for 430 cities and districts all over China, 21 cities/ provinces in Italy, 21 cities/ provinces in Japan, and 51 other countries around the world. Four different time delays of weather (on the day, 3 days ago, 7 days ago, and 14 days ago) as to the epidemic situation were taken for modeling and we finally chose the weather two weeks ago to model against the daily epidemic situation as its correlated with the outbreak best. Taken Chinese cities as a discovery dataset, it was suggested that temperature, wind speed, and relative humidity combined together could best predict the epidemic situation. The meteorological model could well predict the outbreak around the world with a high correlation (r2>0.6) with the real data. Using this model, we further predicted the possible epidemic situation in the future 12 days in several high-latitude cities with potential outbreak. This model could provide more information for government's future decisions on COVID-19 outbreak control.

Title: A spatio-temporal analysis for exploring the effect of temperature on COVID-19 early evolution in Spain Content: The new SARS-CoV-2 coronavirus, which causes the COVID-19 disease, was reported in Wuhan, China, in December 2019. This new pathogen has spread rapidly around more than 200 countries, in which Spain has one of the world's highest mortality rates so far. Previous studies have supported an epidemiological hypothesis that weather conditions may affect the survival and spread of droplet-mediated viral diseases. However, some contradictory studies have also been reported in the same research line. In addition, many of these studies have been performed considering only meteorological factors, which can limit the reliability of the results. Herein, we report a spatio-temporal analysis for exploring the effect of daily temperature (mean, minimum and maximum) on the accumulated number of COVID-19 cases in the provinces of Spain. Non-meteorological factors such as population density, population by age, number of travellers and number of companies have also been considered for the analysis. No evidence suggesting a reduction in COVID-19 cases at warmer mean, minimum and maximum temperatures has been found. Nevertheless, these results need to be interpreted cautiously given the existing uncertainty about COVID-19 data, and should not be extrapolated to temperature ranges other than those analysed here for the early evolution period.

Title: Effect of Temperature on the Transmission of COVID-19: A Machine Learning Case Study in Spain Content: The novel coronavirus (COVID-19) has already spread to almost every country in the world and has infected over 3 million people. To understand the transmission mechanism of this highly contagious virus, it is necessary to study the potential factors, including meteorological conditions. Here, we present a machine learning approach to study the effect of temperature, humidity and wind speed on the number of infected people in the three most populous autonomous communities in Spain. We find that there is a moderate inverse correlation between temperature and the daily number of infections. This correlation manifests for temperatures recorded up to 6 days before the onset, which corresponds well to the known mean incubation period of COVID-19. We also show that the correlation for humidity and wind speed is not significant.

Title: The role of absolute humidity on transmission rates of the COVID-19 outbreak Content: A novel coronavirus (COVID-19) was identified in Wuhan, Hubei Province, China, in December 2019 and has caused over 40,000 cases worldwide to date. Previous studies have supported an epidemiological hypothesis that cold and dry (low absolute humidity) environments facilitate the survival and spread of droplet-mediated viral diseases, and warm and humid (high absolute humidity) environments see attenuated viral transmission (i.e., influenza). However, the role of absolute humidity in transmission of COVID-19 has not yet been established. Here, we examine province-level variability of the basic reproductive numbers of COVID-19 across China and find that changes in weather alone (i.e., increase of temperature and humidity as spring and summer months arrive in the North Hemisphere) will not necessarily lead to declines in COVID-19 case counts without the implementation of extensive public health interventions.

Title: The Weather Impacts the Outbreak of COVID-19 in Mainland China Content: Recent literature has suggested that climate conditions have considerably significant influences on the transmission of coronavirus COVID-19. However, there is a lack of comprehensive study that investigates the relationships between multiple weather factors and the development of COVID-19 pandemic while excluding the impact of social factors. In this paper, we study the relationships between six main weather factors and the infection statistics of COVID-19 on 250 cities in Mainland China. Our correlation analysis using weather and infection statistics indicates that all the studied weather factors are correlated with the spread of COVID-19, where precipitation shows the strongest correlation. We also build a weather-aware predictive model that forecasts the number of infected cases should there be a second wave of the outbreak in Mainland China. Our predicted results show that cities located in different geographical areas are likely to be challenged with the second wave of COVID-19 at very different time periods and the severity of the outbreak varies to a large degree, in correspondence with the varying weather conditions.

Title: Meteorological impacts on the incidence of COVID-19 in the U.S. Content: Since the World Health Organization has declared the current outbreak of the novel coronavirus (COVID-19) a global pandemic, some have been anticipating that the mitigation could happen in the summer like seasonal influenza, while medical solutions are still in a slow progress. Experimental studies have revealed a few evidences that coronavirus decayed quickly under the exposure of heat and humidity. This study aims to carry out an epidemiological investigation to establish the association between meteorological factors and COVID-19 in high risk areas of the United States (U.S.). We analyzed daily new confirmed cases of COVID-19 and seven meteorological measures in top 50 U.S. counties with the most accumulative confirmed cases from March 22, 2020 to April 22, 2020. Our analyses indicate that each meteorological factor and COVID-19 more likely have a nonlinear association rather than a linear association over the wide ranges of temperature, relative humidity, and precipitation observed. Average temperature, minimum relative humidity, and precipitation were better predictors to address the meteorological impact on COVID-19. By including all the three meteorological factors in the same model with their lagged effects up to 3 days, the overall impact of the average temperature on COVID-19 was found to peak at 68.45 °F and decrease at higher degrees, though the overall relative risk percentage (RR %) reduction did not become significantly negative up to 85 °F. There was a generally downward trend of RR % with the increase of minimum relative humidity; nonetheless, the trend reversed when the minimum relative humidity exceeded 91.42%. The overall RR % of COVID-19 climbed to the highest level of 232.07% (95% confidence interval = 199.77, 267.85) with 1.60 inches of precipitation, and then started to decrease. When precipitation exceeded 1.85 inches, its impact on COVID-19 became significantly negative. Our findings alert people to better have self-protection during the pandemic rather than expecting that the natural environment can curb coronavirus for human beings.

Title: Impact of weather on COVID-19 pandemic in Turkey Content: The coronavirus pandemic, which has numerous global implications, has led people to believe that nothing will be the same as before. The present day is dominated by studies on determining the factors that affect, taking preventive actions, and trying to find an effective treatment on top priority. Meteorological parameters are among the crucial factors affecting infectious diseases. The present study examines the correlation between weather and coronavirus disease 2019 (COVID-19) by considering nine cities in Turkey. In this regard, temperature (°C), dew point (°C), humidity (%), and wind speed (mph) are considered as parameters of weather. Research states that the incubation period of COVID-19 varies from 1â¯day to 14â¯days. Therefore, the effects of each parameter within 1, 3, 7, and 14â¯days are examined. In addition, the population is included as an effective parameter for evaluation. The analyses are conducted based on Spearman's correlation coefficients. The results showed that the highest correlations were observed for population, wind speed 14â¯days ago, and temperature on the day, respectively. The study results may guide authorities and decision-makers on taking specific measures for the cities.

Title: Impact of weather on COVID-19 pandemic in Turkey Content: Abstract The coronavirus pandemic, which has numerous global implications, has led people to believe that nothing will be the same as before. The present day is dominated by studies on determining the factors that affect, taking preventive actions, and trying to find an effective treatment on top priority. Meteorological parameters are among the crucial factors affecting infectious diseases. The present study examines the correlation between weather and coronavirus disease 2019 (COVID-19) by considering nine cities in Turkey. In this regard, temperature (°C), dew point (°C), humidity (%), and wind speed (mph) are considered as parameters of weather. Research states that the incubation period of COVID-19 varies from 1 day to 14 days. Therefore, the effects of each parameter within 1, 3, 7, and 14 days are examined. In addition, the population is included as an effective parameter for evaluation. The analyses are conducted based on Spearman's correlation coefficients. The results showed that the highest correlations were observed for population, wind speed 14 days ago, and temperature on the day, respectively. The study results may guide authorities and decision-makers on taking specific measures for the cities.

Title: A close look at the biology of SARS-CoV-2, and the potential influence of weather conditions and seasons on COVID-19 case spread Content: BACKGROUND: There is sufficient epidemiological and biological evidence of increased human susceptibility to viral pathogens such as Middle East respiratory syndrome coronavirus, respiratory syncytial virus, human metapneumovirus and influenza virus, in cold weather. The pattern of outbreak of the coronavirus disease 2019 (COVID-19) in China during the flu season is further proof that meteorological conditions may potentially influence the susceptibility of human populations to coronaviruses, a situation that may become increasingly evident as the current global pandemic of COVID-19 unfolds. MAIN BODY: A very rapid spread and high mortality rates have characterized the COVID-19 pandemic in countries north of the equator where air temperatures have been seasonally low. It is unclear if the currently high rates of COVID-19 infections in countries of the northern hemisphere will wane during the summer months, or if fewer people overall will become infected with COVID-19 in countries south of the equator where warmer weather conditions prevail through most of the year. However, apart from the influence of seasons, evidence based on the structural biology and biochemical properties of many enveloped viruses similar to the novel severe acute respiratory syndrome coronavirus 2 or SARS-CoV-2 (aetiology of COVID-19), support the higher likelihood of the latter of the two outcomes. Other factors that may potentially impact the rate of virus spread include the effectiveness of infection control practices, individual and herd immunity, and emergency preparedness levels of countries. CONCLUSION: This report highlights the potential influence of weather conditions, seasons and non-climatological factors on the geographical spread of cases of COVID-19 across the globe.

Title: A spatio-temporal analysis for exploring the effect of temperature on COVID-19 early evolution in Spain Content: Abstract The new SARS-CoV-2 coronavirus, which causes the COVID-19 disease, was reported in Wuhan, China, in December 2019. This new pathogen has spread rapidly around more than 200 countries, in which Spain has one of the world's highest mortality rates so far. Previous studies have supported an epidemiological hypothesis that weather conditions may affect the survival and spread of droplet-mediated viral diseases. However, some contradictory studies have also been reported in the same research line. In addition, many of these studies have been performed considering only meteorological factors, which can limit the reliability of the results. Herein, we report a spatio-temporal analysis for exploring the effect of daily temperature (mean, minimum and maximum) on the accumulated number of COVID-19 cases in the provinces of Spain. Non-meteorological factors such as population density, population by age, number of travellers and number of companies have also been considered for the analysis. No evidence suggesting a reduction in COVID-19 cases at warmer mean, minimum and maximum temperatures has been found. Nevertheless, these results need to be interpreted cautiously given the existing uncertainty about COVID-19 data, and should not be extrapolated to temperature ranges other than those analysed here for the early evolution period.

Title: Will the COVID-19 pandemic slow down in the Northern hemisphere by the onset of summer? An epidemiological hypothesis Content: The COVID-19 pandemic has affected most countries of the world. As corona viruses are highly prevalent in the cold season, the question remains whether or not the pandemic will improve with increasing temperatures in the Northern hemisphere. We use data from a primary care registry of almost 15,000 patients over 20 years to retrieve information on viral respiratory infection outbreaks. Our analysis suggests that the severity of the pandemic will be softened by the seasonal change to summer.

Title: Does temperature and humidity influence the spread of Covid-19?: A preliminary report Content: INTRODUCTION: Climate change has been known to influence infectious diseases. The reason for this being the fact; disease agents and their vectors each have particular environments that are optimal for growth, survival, transport, and dissemination. MATERIALS AND METHODS: The WHO's website was accessed to look for the Novel Coronavirus (COVID-19) situation dashboard and comprehensively study and assess the report. An attempt was made to look for countries, areas or territories with maximum and minimum number of cases of lab confirmed COVID cases. Further, we entered the words “Climate“ in google for each of the aforementioned countries and searched for the results. A comparison was established by including countries from both the hemispheres (northern and southern). The preliminary analysis was based on the reports from countries with established testing facilities for Covid-19. RESULTS: The report suggests that countries with higher number of cases are the countries with cold weather. These are also the countries with low humidity which could be favoring the transmission and survival of the SARS-COV-2. CONCLUSIONS: The results though preliminary point to a pattern which favors the hypothesis that the extensive spread of Covid-19 maybe limited by temperature and humidity.

Title: Correlation of the global spread of coronavirus disease-19 with atmospheric air temperature Content: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an enveloped virus that may be sensitive to heat. We assessed whether the spread of coronavirus disease 2019 (COVID-19) correlates with air temperature. We also studied whether additional climate, geographical, and population variables were correlated. The total number of confirmed COVID-19 cases and mortality rates reported in each country between 1st Jan and 31st Mar 2020 were compared with the country's three-month average atmospheric air temperature, precipitation and latitude. Spearman's correlation coefficient (rs) was used to identify significant correlations. Our analysis included a total of 748,555 confirmed COVID-19 cases worldwide. The total number of patients with COVID-19 decreased with increasing atmospheric air temperature (rs = -0.54, 95%CI: [-0.64, -0.42]; P <0.001) and increased with an increasing latitude (rs =0.60, 95%CI: [0.48, 0.70]; P <0.001). Our findings justify further studies to examine the effect of air temperature on infectivity of SAR-CoV-2.

Title: Association between ambient temperature and COVID-19 infection in 122 cities from China Content: BACKGROUND: Coronavirus disease 2019 (COVID-19) has become a severe public health problem globally. Both epidemiological and laboratory studies have shown that ambient temperature could affect the transmission and survival of coronaviruses. This study aimed to determine whether the temperature is an essential factor in the infection caused by this novel coronavirus. METHODS: Daily confirmed cases and meteorological factors in 122 cities were collected between January 23, 2020, to February 29, 2020. A generalized additive model (GAM) was applied to explore the nonlinear relationship between mean temperature and COVID-19 confirmed cases. We also used a piecewise linear regression to determine the relationship in detail. RESULTS: The exposure-response curves suggested that the relationship between mean temperature and COVID-19 confirmed cases was approximately linear in the range of <3 °C and became flat above 3 °C. When mean temperature (lag0-14) was below 3 °C, each 1 °C rise was associated with a 4.861% (95% CI: 3.209-6.513) increase in the daily number of COVID-19 confirmed cases. These findings were robust in our sensitivity analyses. CONCLUSIONS: Our results indicate that mean temperature has a positive linear relationship with the number of COVID-19 cases with a threshold of 3 °C. There is no evidence supporting that case counts of COVID-19 could decline when the weather becomes warmer, which provides useful implications for policymakers and the public.

Title: Temperature Decreases Spread Parameters of the New Covid-19 Case Dynamics Content: (1) Background: The virulence of coronavirus diseases due to viruses like SARS-CoV or MERS-CoV decreases in humid and hot weather. The putative temperature dependence of infectivity by the new coronavirus SARS-CoV-2 or covid-19 has a high predictive medical interest. (2) Methods: External temperature and new covid-19 cases in 21 countries and in the French administrative regions were collected from public data. Associations between epidemiological parameters of the new case dynamics and temperature were examined using an ARIMA model. (3) Results: We show that, in the first stages of the epidemic, the velocity of contagion decreases with country- or region-wise temperature. (4) Conclusions: Results indicate that high temperatures diminish initial contagion rates, but seasonal temperature effects at later stages of the epidemy remain questionable. Confinement policies and other eviction rules should account for climatological heterogeneities, in order to adapt the public health decisions to possible geographic or seasonal gradients.

Title: Substantial Changes in Nitrate Oxide and Ozone after Excluding Meteorological Impacts during the COVID-19 Outbreak in Mainland China Content: [Image: see text] The COVID-19 outbreak in China led to dramatic changes in human activities resulting from the sudden infection prevention and control measures. Here, we use ground-level observations and model simulations to examine the nationwide spatial–temporal variations of six air pollutants before and after the initiation of First-Level Public Health Emergency Response. The level of ambient NO(2) declined significantly, and in most cities, the decline was dominated by reduced emissions. Meanwhile, the level of O(3) increased significantly during this period, and the nonmeteorological factors explained the increase. For the other air pollutants (PM(2.5), SO(2), and CO), the observed declines on the national scale were obviously affected by the meteorological conditions. In Wuhan, significant declines were found for air pollutants except O(3) and emissions dominated the changes, while in Beijing during the same period, only the level of NO(2) significantly declined. This study clearly shows that the meteorological changes contributed substantially to the observed changes in most air pollutants, and this must be considered in evaluating the impacts of pollutant source changes on air quality during the specific event and in assessing source-oriented risks.

Title: Effects of temperature and humidity on the daily new cases and new deaths of COVID-19 in 166 countries Content: The coronavirus disease 2019 (COVID-19) pandemic is the defining global health crisis of our time and the greatest challenge facing the world. Meteorological parameters are reportedly crucial factors affecting respiratory infectious disease epidemics; however, the effect of meteorological parameters on COVID-19 remains controversial. This study investigated the effects of temperature and relative humidity on daily new cases and daily new deaths of COVID-19, which has useful implications for policymakers and the public. Daily data on meteorological conditions, new cases and new deaths of COVID-19 were collected for 166 countries (excluding China) as of March 27, 2020. Log-linear generalized additive model was used to analyze the effects of temperature and relative humidity on daily new cases and daily new deaths of COVID-19, with potential confounders controlled for, including wind speed, median age of the national population, Global Health Security Index, Human Development Index and population density. Our findings revealed that temperature and relative humidity were both negatively related to daily new cases and deaths. A 1 °C increase in temperature was associated with a 3.08% (95% CI: 1.53%, 4.63%) reduction in daily new cases and a 1.19% (95% CI: 0.44%, 1.95%) reduction in daily new deaths, whereas a 1% increase in relative humidity was associated with a 0.85% (95% CI: 0.51%, 1.19%) reduction in daily new cases and a 0.51% (95% CI: 0.34%, 0.67%) reduction in daily new deaths. The results remained robust when different lag structures and the sensitivity analysis were used. These findings provide preliminary evidence that the COVID-19 pandemic may be partially suppressed with temperature and humidity increases. However, active measures must be taken to control the source of infection, block transmission and prevent further spread of COVID-19.

Title: Effects of meteorological conditions and air pollution on COVID-19 transmission: Evidence from 219 Chinese cities. Content: The spatial distribution of the COVID-19 infection in China cannot be explained solely by geographical distance and regulatory stringency. In this research we investigate how meteorological conditions and air pollution, as concurring factors, impact COVID-19 transmission, using data on new confirmed cases from 219 prefecture cities from January 24 to February 29, 2020. Results revealed a kind of nonlinear dose-response relationship between temperature and coronavirus transmission. We also found that air pollution indicators are positively correlated with new confirmed cases, and the coronavirus further spreads by 5-7% as the AQI increases by 10 units. Further analysis based on regional divisions revealed that in northern China the negative effects of rising temperature on COVID-19 is counteracted by aggravated air pollution. In the southern cities, the ambient temperature and air pollution have a negative interactive effect on COVID-19 transmission, implying that rising temperature restrains the facilitating effects of air pollution and that they jointly lead to a decrease in new confirmed cases. These results provide implications for the control and prevention of this disease and for the anticipation of another possible pandemic.

Title: Effects of meteorological conditions and air pollution on COVID-19 transmission: Evidence from 219 Chinese cities Content: The spatial distribution of the COVID-19 infection in China cannot be explained solely by geographical distance and regulatory stringency. In this research we investigate how meteorological conditions and air pollution, as concurring factors, impact COVID-19 transmission, using data on new confirmed cases from 219 prefecture cities from January 24 to February 29, 2020. Results revealed a kind of nonlinear dose-response relationship between temperature and coronavirus transmission. We also found that air pollution indicators are positively correlated with new confirmed cases, and the coronavirus further spreads by 5-7% as the AQI increases by 10 units. Further analysis based on regional divisions revealed that in northern China the negative effects of rising temperature on COVID-19 is counteracted by aggravated air pollution. In the southern cities, the ambient temperature and air pollution have a negative interactive effect on COVID-19 transmission, implying that rising temperature restrains the facilitating effects of air pollution and that they jointly lead to a decrease in new confirmed cases. These results provide implications for the control and prevention of this disease and for the anticipation of another possible pandemic.

Title: Weathering the pandemic: How the Caribbean Basin can use viral and environmental patterns to predict, prepare, and respond to COVID-19 Content: The 2020 coronavirus pandemic is developing at different paces throughout the world. Some areas, like the Caribbean Basin, have yet to see the virus strike at full force. When it does, there is reasonable evidence to suggest the consequent COVID-19 outbreaks will overwhelm healthcare systems and economies. This is particularly concerning in the Caribbean as pandemics can have disproportionately higher mortality impacts on lower and middle-income countries. Preliminary observations from our team and others suggest that temperature and climatological factors could influence the spread of this novel coronavirus, making spatiotemporal predictions of its infectiousness possible. This review studies geographic and time-based distribution of known respiratory viruses in the Caribbean Basin in an attempt to foresee how the pandemic will develop in this region. This review is meant to aid in planning short- and long-term interventions to manage outbreaks at the international, national, and subnational levels in the region.

Title: Eco-epidemiological assessment of the COVID-19 epidemic in China, January–February 2020 Content: Background: The outbreak of COVID-19 in China in early 2020 provides a rich data source for exploring the ecological determinants of this new infection, which may be of relevance as the pandemic develops. Objectives: Assessing the spread of the COVID-19 across China, in relation to associations between cases and ecological factors including population density, temperature, solar radiation and precipitation. Methods: Open-access COVID-19 case data include 18,069 geo-located cases in China during January and February 2020, which were mapped onto a 0.25° latitude/longitude grid together with population and weather data (temperature, solar radiation and precipitation). Of 15,539 grid cells, 559 (3.6%) contained at least one case, and these were used to construct a Poisson regression model of cell-weeks. Weather parameters were taken for the preceding week given the established 5–7 day incubation period for COVID-19. The dependent variable in the Poisson model was incident cases per cell-week and exposure was cell population, allowing for clustering of cells over weeks, to give incidence rate ratios. Results: The overall COVID-19 incidence rate in cells with confirmed cases was 0.12 per 1,000. There was a single confirmed case in 113/559 (20.2%) of cells, while two grid cells recorded over 1,000 confirmed cases. Weekly means of maximum daily temperature varied from −28.0°C to 30.1°C, minimum daily temperature from −42.4°C to 23.0°C, maximum solar radiation from 0.04 to 2.74 MJm(−2) and total precipitation from 0 to 72.6 mm. Adjusted incidence rate ratios suggested brighter, warmer and drier conditions were associated with lower incidence. Conclusion: Though not demonstrating cause and effect, there were appreciable associations between weather and COVID-19 incidence during the epidemic in China. This does not mean the pandemic will go away with summer weather but demonstrates the importance of using weather conditions in understanding and forecasting the spread of COVID-19.

Title: Eco-epidemiological assessment of the COVID-19 epidemic in China, January-February 2020 Content: Background: The outbreak of COVID-19 in China in early 2020 provides a rich data source for exploring the ecological determinants of this new infection, which may be of relevance as the pandemic develops.Objectives: Assessing the spread of the COVID-19 across China, in relation to associations between cases and ecological factors including population density, temperature, solar radiation and precipitation.Methods: Open-access COVID-19 case data include 18,069 geo-located cases in China during January and February 2020, which were mapped onto a 0.25° latitude/longitude grid together with population and weather data (temperature, solar radiation and precipitation). Of 15,539 grid cells, 559 (3.6%) contained at least one case, and these were used to construct a Poisson regression model of cell-weeks. Weather parameters were taken for the preceding week given the established 5-7 day incubation period for COVID-19. The dependent variable in the Poisson model was incident cases per cell-week and exposure was cell population, allowing for clustering of cells over weeks, to give incidence rate ratios.Results: The overall COVID-19 incidence rate in cells with confirmed cases was 0.12 per 1,000. There was a single confirmed case in 113/559 (20.2%) of cells, while two grid cells recorded over 1,000 confirmed cases. Weekly means of maximum daily temperature varied from -28.0°C to 30.1°C, minimum daily temperature from -42.4°C to 23.0°C, maximum solar radiation from 0.04 to 2.74 MJm-2 and total precipitation from 0 to 72.6 mm. Adjusted incidence rate ratios suggested brighter, warmer and drier conditions were associated with lower incidence.Conclusion: Though not demonstrating cause and effect, there were appreciable associations between weather and COVID-19 incidence during the epidemic in China. This does not mean the pandemic will go away with summer weather but demonstrates the importance of using weather conditions in understanding and forecasting the spread of COVID-19.

Title: The Challenge of Using Epidemiological Case Count Data: The Example of Confirmed COVID-19 Cases and the Weather Content: The publicly available data on COVID-19 cases provides an opportunity to better understand this new disease. However, strong attention needs to be paid to the limitations of the data to avoid making inaccurate conclusions. This article, which focuses on the relationship between the weather and COVID-19, raises the concern that the same factors influencing the spread of the disease might also affect the number of tests performed and who gets tested. For example, weather conditions impact the prevalence of respiratory diseases with symptoms similar to COVID-19, and this will likely influence the number of tests performed. This general limitation could severely undermine any similar analysis using existing COVID-19 data or similar epidemiological data, which could, therefore, mislead decision-makers on questions of great policy relevance.

Title: Impact of weather indicators on the COVID-19 outbreak: A multi-state study in India Content: The present study examines the impact of weather indicators on the COVID-19 outbreak in the majorly affected states of India. In this study, we hypothesize that the weather indicators could significantly influence the impact of the corona virus. The Kendall and Spearman rank correlation tests were chosen to conduct the statistical analysis. In this regard, we compiled a daily dataset including confirmed case counts, Recovered case counts, Deceased cases, Average Temperature, Maximum Relative Humidity, Maximum Wind Speed for six most affected states of India during the period of March 25, 2020 to April 24, 2020. We investigated that the average Humidity and Average Temperature seven days ago play a significant role in the recovery of coronavirus cases. The rise in average temperature will improve the recovery rate in the days to come. The cities with very high humidity levels or dry weather conditions have high probabilities of recovery from COVID-19. The findings of this research will help the policymakers to identify risky geographic areas and enforce timely preventive measures.

Title: COVID-19: Effects of weather conditions on the propagation of respiratory droplets Content: As the number of confirmed cases of Coronavirus disease 2019 (COVID-19) continues to increase, there has been a rising concern regarding the effect of weather conditions, especially over the upcoming summer, on the transmission of this disease. In this study, we assess the transmission of COVID-19 under different weather conditions by investigating the propagation of infectious respiratory droplets. A comprehensive mathematical model is established to explore their evaporation, heat transfer and kinematics under different temperature, humidity and ventilation conditions. The transmitting pathway of COVID-19 through respiratory droplets is divided into short-range droplet contacts and long-range aerosol exposure. We show that the effect of weather conditions is not monotonic: low temperature and high humidity facilitate droplet contact transmission, while high temperature and low humidity promote the formation of aerosol particles and accumulation of particles with a diameter of 2.5 m or less (PM2.5). Our model suggests that the 6 ft of social distance recommended by the Center for Disease Control and Prevention (CDC) may be insufficient in certain environmental conditions, as the droplet spreading distance can be as long as 6 m (19.7 ft) in cold and humid weather. The results of this study suggest that the current pandemic may not ebb in the summer of the northern hemisphere without proper intervention, as there is an increasing chance of aerosol transmission. We also emphasize that the meticulous design of building ventilation systems is critical in containing both the droplet contact infections and aerosol exposures.

Title: Effects of temperature and humidity on the daily new cases and new deaths of COVID-19 in 166 countries Content: Abstract The coronavirus disease 2019 (COVID-19) pandemic is the defining global health crisis of our time and the greatest challenge facing the world. Meteorological parameters are reportedly crucial factors affecting respiratory infectious disease epidemics; however, the effect of meteorological parameters on COVID-19 remains controversial. This study investigated the effects of temperature and relative humidity on daily new cases and daily new deaths of COVID-19, which has useful implications for policymakers and the public. Daily data on meteorological conditions, new cases and new deaths of COVID-19 were collected for 166 countries (excluding China) as of March 27, 2020. Log-linear generalized additive model was used to analyze the effects of temperature and relative humidity on daily new cases and daily new deaths of COVID-19, with potential confounders controlled for, including wind speed, median age of the national population, Global Health Security Index, Human Development Index and population density. Our findings revealed that temperature and relative humidity were both negatively related to daily new cases and deaths. A 1 °C increase in temperature was associated with a 3.08% (95% CI: 1.53%, 4.63%) reduction in daily new cases and a 1.19% (95% CI: 0.44%, 1.95%) reduction in daily new deaths, whereas a 1% increase in relative humidity was associated with a 0.85% (95% CI: 0.51%, 1.19%) reduction in daily new cases and a 0.51% (95% CI: 0.34%, 0.67%) reduction in daily new deaths. The results remained robust when different lag structures and the sensitivity analysis were used. These findings provide preliminary evidence that the COVID-19 pandemic may be partially suppressed with temperature and humidity increases. However, active measures must be taken to control the source of infection, block transmission and prevent further spread of COVID-19.

Title: Eco-epidemiological assessment of the COVID-19 epidemic in China, January-February 2020 Content: Background: The outbreak of COVID-19 in China in early 2020 provides a rich data source for exploring the ecological determinants of this new infection, which may be of relevance elsewhere. Objectives: Assessing the spread of the COVID-19 across China, in relation to associations between cases and ecological factors including population density, temperature, solar radiation and precipitation. Methods: Open-access COVID-19 case data include 18,069 geo-located cases in China during January and February 2020, which were mapped onto a 0.25° latitude/longitude grid together with population and weather data (temperature, solar radiation and precipitation). Of 15,539 grid cells, 559 (3.6%) contained at least one case, and these were used to construct a Poisson regression model of cell-weeks. Weather parameters were taken for the preceding week given the established 5-7 day incubation period for COVID-19. The dependent variable in the Poisson model was incident cases per cell-week and exposure was cell population, allowing for clustering of cells over weeks, to give incidence rate ratios. Results: The overall COVID-19 incidence rate in cells with confirmed cases was 0.12 per 1,000. There was a single case in 113/559 (20.2%) of cells, while two grid cells recorded over 1,000 cases. Weekly means of maximum daily temperature varied from -28.0 to 30.1 °C, minimum daily temperature from -42.4 to 23.0 °C, maximum solar radiation from 0.04 to 2.74 MJm-2 and total precipitation from 0 to 72.6 mm. Adjusted incidence rate ratios suggested brighter, warmer and drier conditions were associated with lower incidence. Conclusion: Though not demonstrating cause and effect, there were appreciable associations between weather and COVID-19 incidence during the epidemic in China. This does not mean the pandemic will go away with summer weather but demonstrates the importance of using weather conditions in understanding and forecasting the spread of COVID-19.

Title: Effects of temperature variation and humidity on the death of COVID-19 in Wuhan, China Content: Meteorological parameters are the important factors influencing the infectious diseases such as severe acute respiratory syndrome (SARS) and influenza. This study aims to explore the association between Corona Virus Disease 2019 (COVID-19) deaths and weather parameters. In this study, we collected the daily death numbers of COVID-19, meteorological parameters and air pollutant data from 20 January 2020 to 29 February 2020 in Wuhan, China. Generalized additive model was applied to explore the effect of temperature, humidity and diurnal temperature range on the daily death counts of COVID-19. There were 2299 COVID-19 death counts in Wuhan during the study period. A positive association with COVID-19 daily death counts was observed for diurnal temperature range (r = 0.44), but negative association for relative humidity (r = -0.32). In addition, one unit increase in diurnal temperature range was only associated with a 2.92% (95% CI: 0.61%, 5.28%) increase in COVID-19 deaths in lag 3. However, both 1 unit increase of temperature and absolute humidity were related to the decreased COVID-19 death in lag 3 and lag 5, with the greatest decrease both in lag 3 [-7.50% (95% CI: -10.99%, -3.88%) and -11.41% (95% CI: -19.68%, -2.29%)]. In summary, this study suggests the temperature variation and humidity may also be important factors affecting the COVID-19 mortality.

Title: Weathering the pandemic: How the Caribbean Basin can use viral and environmental patterns to predict, prepare and respond to COVID‐19 Content: The 2020 coronavirus pandemic is developing at different paces throughout the world. Some areas, like the Caribbean Basin, have yet to see the virus strike at full force. When it does, there is reasonable evidence to suggest the consequent COVID‐19 outbreaks will overwhelm healthcare systems and economies. This is particularly concerning in the Caribbean as pandemics can have disproportionately higher mortality impacts on lower and middle income countries. Preliminary observations from our team and others suggest that temperature and climatological factors could influence the spread of this novel coronavirus, making spatiotemporal predictions of its infectiousness possible. This review studies geographic and time‐based distribution of known respiratory viruses in the Caribbean Basin in an attempt to foresee how the pandemic will develop in this region. This review is meant to aid in planning short‐ and long‐term interventions to manage outbreaks at the international, national and sub‐national levels in the region. This article is protected by copyright. All rights reserved.

Title: Effects of weather and policy intervention on COVID-19 infection in Ghana Content: Even though laboratory and epidemiological studies have demonstrated the effects of ambient temperature on the transmission and survival of coronaviruses, not much has been done on the effects of weather on the spread of COVID-19. This study investigates the effects of temperature, humidity, precipitation, wind speed and the specific government policy intervention of partial lockdown on the new cases of COVID-19 infection in Ghana. Daily data on confirmed cases of COVID-19 from March 13, 2020 to April 21, 2020 were obtained from the official website of Our World in Data (OWID) dedicated to COVID-19 while satellite climate data for the same period was obtained from the official website of NASA's Prediction of Worldwide Energy Resources (POWER) project. Considering the nature of the data and the objectives of the study, a time series generalized linear model which allows for regressing on past observations of the response variable and covariates was used for model fitting. The results indicate significant effects of maximum temperature, relative humidity and precipitation in predicting new cases of the disease. Also, results of the intervention analysis indicate that the null hypothesis of no significant effect of the specific policy intervention of partial lockdown should be rejected (p-value=0.0164) at a 5\% level of significance. These findings provide useful insights for policymakers and the public.

Title: Short-Term Effects of Ambient Ozone, PM(2.5,) and Meteorological Factors on COVID-19 Confirmed Cases and Deaths in Queens, New York Content: The outbreak of coronavirus disease 2019 (COVID-19), caused by the virus SARS-CoV-2, has been rapidly increasing in the United States. Boroughs of New York City, including Queens county, turn out to be the epicenters of this infection. According to the data provided by the New York State Department of Health, most of the cases of new COVID-19 infections in New York City have been found in the Queens county where 42,023 people have tested positive, and 3221 people have died as of 20 April 2020. Person-to-person transmission and travels were implicated in the initial spread of the outbreaks, but factors related to the late phase of rapidly spreading outbreaks in March and April are still uncertain. A few previous studies have explored the links between air pollution and COVID-19 infections, but more data is needed to understand the effects of short-term exposures of air pollutants and meteorological factors on the spread of COVID-19 infections, particularly in the U.S. disease epicenters. In this study, we have focused on ozone and PM(2.5), two major air pollutants in New York City, which were previously found to be associated with respiratory viral infections. The aim of our regression modeling was to explore the associations among ozone, PM(2.5), daily meteorological variables (wind speed, temperature, relative humidity, absolute humidity, cloud percentages, and precipitation levels), and COVID-19 confirmed new cases and new deaths in Queens county, New York during March and April 2020. The results from these analyses showed that daily average temperature, daily maximum eight-hour ozone concentration, average relative humidity, and cloud percentages were significantly and positively associated with new confirmed cases related to COVID-19; none of these variables showed significant associations with new deaths related to COVID-19. The findings indicate that short-term exposures to ozone and other meteorological factors can influence COVID-19 transmission and initiation of the disease, but disease aggravation and mortality depend on other factors.

Title: Will COVID-19 pandemic diminish by summer-monsoon in India? Lesson from the first lockdown Content: The novel Coronavirus (2019-nCoV) was identified in Wuhan, Hubei Province, China, in December 2019 and has created a medical emergency worldwide. It has spread rapidly to multiple countries and has been declared a pandemic by the World Health Organization. In India, it is already reported more than 18 thousand cases and more than 600 deaths due to Coronavirus disease 2019 (COVID-19) till April 20, 2020. Previous studies on various viral infections like influenza have supported an epidemiological hypothesis that the cold and dry (low absolute humidity) environments favor the survival and spread of droplet-mediated viral diseases. These viral transmissions found attenuated in warm and humid (high absolute humidity) environments. However, the role of temperature, humidity, and absolute humidity in the transmission of COVID-19 has not yet been well established. Therefore the study to investigate the meteorological condition for incidence and spread of COVID-19 infection, to predict the epidemiology of the infectious disease, and to provide a scientific basis for prevention and control measures against the new disease is required for India. In this work, we analyze the local weather patterns of the Indian region affected by the COVID-19 virus for March and April months, 2020. We have investigated the effect of meteorological parameters like Temperature, relative humidity, and absolute humidity on the rate of spread of COVID-19 using daily confirm cases in India. We have used daily averaged meteorological data for the last three years (2017-2019) for March and April month and the same for the year 2020 for March 1 to April 15. We found a positive association (Pearsons r=0.56) between temperature and daily COVID-19 cases over India. We found a negative association of humidity (RH and AH) with daily COVID-19 Cases (Persons r=-0.62, -0.37). We have also investigated the role of aerosol in spreading the pandemic across India because its possible airborne nature. For this, we have investigated the association of aerosols (AOD) and other pollutions (NO2) with COVID-19 cases during the study period and also during the first lockdown period (25 March-15 April) in India. We found a negative association in March when there were few cases, but in April, it shows positive association when the number of cases is more (for AOD it was r=-0.41 and r=0.28 respectively). During the lockdown period, aerosols (AOD) and other pollutants (NO2; an indicator of PM2.5) reduced sharply with a percentage drop of about 36 and 37, respectively. This reduction may have reduced the risk for COVID-19 through air transmission due to the unavailability of aerosol particles as a base. HYSPLIT forward trajectory model also shows that surface aerosols may travel up to 4 km according to wind and direction within three h of its generation. If coronavirus becomes airborne as suggested by many studies, then it may have a higher risk of transmission by aerosols particles. So relaxing in the lockdown and environmental rules in terms of pollutant emissions from power plants, factories, and other facilities would be a wrong choice and could result in more COVID-19 incidences and deaths in India. Therefore the current study, although limited, suggests that it is doubtful that the spread of COVID-19 would slow down in India due to meteorological factors, like high temperature and high humidity. Because a large number of cases have already been reported in the range of high Tem, high Relative, and high absolute humidity regions of India. Thus our results in no way suggest that COVID-19 would not spread in warm, humid regions or during summer/monsoon. So effective public health interventions should be implemented across India to slow down the transmission of COVID-19. If COVID-19 is indeed sensitive to environmental factors, it could be tested in the coming summer-monsoon for India. So the only summer is not going to help India until monsoon is coming. Only government mitigations strategies would be helpful, whether its lockdown, aggressive and strategic testing, medical facilities, imposing social distancing, encouraging to use face mask or monitoring by a mobile application (Aarogya Setu).

Title: Weather Conditions and COVID-19 Transmission: Estimates and Projections Content: Background: Understanding and projecting the spread of COVID-19 requires reliable estimates of how weather components are associated with the transmission of the virus. Prior research on this topic has been inconclusive. Identifying key challenges to reliable estimation of weather impact on transmission we study this question using one of the largest assembled databases of COVID-19 infections and weather. Methods: We assemble a dataset that includes virus transmission and weather data across 3,739 locations from December 12, 2019 to April 22, 2020. Using simulation, we identify key challenges to reliable estimation of weather impacts on transmission, design a statistical method to overcome these challenges, and validate it in a blinded simulation study. Using this method and controlling for location-specific response trends we estimate how different weather variables are associated with the reproduction number for COVID-19. We then use the estimates to project the relative weather-related risk of COVID-19 transmission across the world and in large cities. Results: We show that the delay between exposure and detection of infection complicates the estimation of weather impact on COVID-19 transmission, potentially explaining significant variability in results to-date. Correcting for that distributed delay and offering conservative estimates, we find a negative relationship between temperatures above 25 degrees Celsius and estimated reproduction number ([R]), with each degree Celsius associated with a 3.1% (95% CI, 1.5% to 4.8%) reduction in [R]. Higher levels of relative humidity strengthen the negative effect of temperature above 25 degrees. Moreover, one millibar of additional pressure increases [R] by approximately 0.8 percent (95% CI, 0.6% to 1%) at the median pressure (1016 millibars) in our sample. We also find significant positive effects for wind speed, precipitation, and diurnal temperature on [R]. Sensitivity analysis and simulations show that results are robust to multiple assumptions. Despite conservative estimates, weather effects are associated with a 43% change in [R] between the 5th and 95th percentile of weather conditions in our sample. Conclusions: These results provide evidence for the relationship between several weather variables and the spread of COVID-19. However, the (conservatively) estimated relationships are not strong enough to seasonally control the epidemic in most locations.

Title: Short-term effects of specific humidity and temperature on COVID-19 morbidity in select US cities Content: Little is known about the environmental conditions that drive the spatiotemporal patterns of SARS-CoV-2. Preliminary research suggests an association with meteorological parameters. However, the relationship with temperature and humidity is not yet apparent for COVID-19 cases in US cities first impacted. The objective of this study is to evaluate the association between COVID-19 cases and meteorological parameters in select US cities. A case-crossover design with a distributed lag nonlinear model was used to evaluate the contribution of ambient temperature and specific humidity on COVID-19 cases in select US cities. The case-crossover examines each COVID case as its own control at different time periods (before and after transmission occurred). We modeled the effect of temperature and humidity on COVID-19 transmission using a lag period of 7 days. A subset of 8 cities were evaluated for the relationship with meteorological parameters and 5 cities were evaluated in detail. Short-term exposure to humidity was positively associated with COVID-19 transmission in 4 cities. The associations were small with 3 out of 4 cities exhibiting higher COVID19 transmission with specific humidity that ranged from 6 to 9 g/kg. Our results suggest that weather should be considered in infectious disease modeling efforts. Future work is needed over a longer time period and across different locations to clearly establish the weather-COVID19 relationship.

Title: The Seasonal End of Human Coronavirus Hospital Admissions with Implications for SARS-CoV-2 Content: The seasonality of influenza viruses and endemic human coronaviruses was tracked over an 8-year period to assess key epidemiologic reduction points in disease incidence for an urban area in the northeast United States. Patients admitted to a pediatric hospital with worsening respiratory symptoms were tested using a multiplex PCR assay from nasopharyngeal swabs. The additive seasonal effects of outdoor temperatures and indoor relative humidity (RH) were evaluated. The 8-year average peak activity of human coronaviruses occurred in the first week of January, when droplet and contact transmission was enabled by the low indoor RH of 20-30%. Previous studies have shown that an increase in RH to 50% has been associated with markedly reduced viability and transmission of influenza virus and animal coronaviruses. As disease incidence was reduced by 50% in early March, to 75% in early April, to greater than 99% at the end of April, a relationship was observed from colder temperatures in January with a low indoor RH to a gradual increase in outdoor temperatures in April with an indoor RH of 45-50%. As a lipid-bound, enveloped virus with similar size characteristics to endemic human coronaviruses, SARS-CoV-2 should be subject to the same dynamics of reduced viability and transmission with increased humidity. In addition to the major role of social distancing, the transition from lower to higher indoor RH with increasing outdoor temperatures could have an additive effect on the decrease in SARS-CoV-2 cases in May. Over the 8-year period of this study, human coronavirus activity was either zero or >99% reduction in the months of June through September, and the implication would be that SARS-Cov-2 may follow a similar pattern.

Title: Role of temperature and humidity in the modulation of the doubling time of COVID-19 cases Content: COVID-19 is having a great impact on public health, mortality and economy worldwide, in spite of the efforts to prevent its epidemy. The SARS-CoV-2 genome is different from that of MERS-CoV and SARS-CoV, although also expected to spread differently according to meteorological conditions. Our main goal is to investigate the role of some meteorological variables on the expansion of this outbreak. In this study, an exponential model relating the number of accumulated confirmed cases and time was considered. The rate of COVID-19 spread, using as criterion the doubling time of the number of confirmed cases, was used as dependent variable in a linear model that took four independent meteorological variables: temperature, humidity, precipitation and wind speed. Only China cases were considered, to control both cultural aspects and containment policies. Confirmed cases and the 4 meteorological variables were gathered between January 23 and March 1 (39 days) for the 31 provinces of Mainland China. Several periods of time were sampled for each province, obtaining more than one value for the rate of disease progression. Two different periods of time were tested, of 12 and 15 days, along with 3 and 5 different starting points in time, randomly chosen. The median value for each meteorological variable was computed, using the same time period; models with adjusted R square above 0.75 were selected. The rate of progression and doubling time were computed and used to fit a linear regression model. Models were evaluated using alpha=0.05. Results indicate that the doubling time correlates positively with temperature and inversely with humidity, suggesting that a decrease in the rate of progression of COVID-19 with the arrival of spring and summer in the north hemisphere. A 20oC increase is expected to delay the doubling time in 1.8 days. Those variables explain 18% of the variation in disease doubling time; the remaining 82% may be related to containment measures, general health policies, population density, transportation or cultural aspects.

Title: Multiple drivers of the COVID-19 spread: role of climate, international mobility, and region-specific conditions Content: The novel Coronavirus Disease 2019 (COVID-19) has spread quickly across the globe. Here, we evaluated the role of climate (temperature and precipitation), region-specific susceptibility (BCG vaccination, malaria infection, and elderly population) and international traveller population (human mobility) in shaping the geographical patterns of COVID-19 cases across 1,055 countries/regions, and examined the sequential shift of multiple drivers of the accumulated cases from December, 2019 to April 12, 2020. The accumulated numbers of COVID-19 cases (per 1 million population) were well explained by a simple regression model. The explanatory power (R2) of the model increased up to > 70% in April 2020 as the COVID-19 spread progressed. Climate, host mobility, and host susceptibility largely explained the variance of the COVID-19 cases (per 1 million population), and their explanatory power improved as the pandemic progressed; the relative importance of host mobility and host susceptibility have been greater than that of climate. The number of days from outbreak onset showed greater explanatory power in the earlier stages of COVID-19 spread but rapidly lost its influence. Our findings demonstrate that the COVID-19 pandemic is deterministically driven by climate suitability, cross-border human mobility, and region-specific susceptibility. The present distribution of COVID-19 cases has not reached an equilibrium and is changing daily, especially in the Southern Hemisphere. Nevertheless, the present results, based on mapping the spread of COVID-19 and identifying multiple drivers of this outbreak trajectory, may contribute to a better understanding of the COVID-19 disease transmission risk and the measures against long-term epidemic.

Title: Determinants of the infection rate of the COVID-19 in the U.S. using ANFIS and virus optimization algorithm (VOA) Content: Recently, anovel coronavirus virus disease (COVID-19) has become a serious concern for global public health hazards. Infectious disease outbreaks such as COVID-19can also significantly affect the sustainable development of urban areas. Several factors such as population density and climatology parameters could potentially affect the spread of the COVID-19. In this study, a combination of the virus optimization algorithm (VOA) and adaptive network-based fuzzy inference system (ANFIS) to investigate the effects of various climate-related factors and population density on the spread of the COVID-19. For this purpose, data on the climate-related factors and the confirmed infected cases by the COVID-19across the U.S counties was used.The results show that the variable defined for the population density had the most significant impact on the performance of the developed models, which is an indication of the importance social distancing in reducing the infection rate and spread rate of the COVID-19. Among the climatology parameters, an increase in the maximum temperature was found to reduce the infection rate. Average temperature, minimum temperature, precipitation, and average wind speed were not found to significantly affect the spread of the COVID-19 while an increase in the relative humidity was found to slightly increase the infection rate. The findings of this research show that it could be expected to have reduced infection rate over the summer season. However, it should be noted that the models developed in this study were based on limited one-month data. Future investigation can benefit from using more comprehensive data covering a wider range for the input variables.

Title: Effect of weather on COVID-19 spread in the US: A prediction model for India in 2020 Content: The effect of weather on COVID-19 spread is poorly understood. Recently, few studies have claimed that warm weather can possibly slowdown the global pandemic, which has already affected over 1.6 million people worldwide. Clarification of such relationships in the worst affected country, the US, can be immensely beneficial to understand the role of weather in transmission of the disease in the highly populated countries, such as India. We collected the daily data of new cases in 50 US states between Jan 1-Apr 9, 2020 and also the corresponding weather information (i.e., temperature (T) and absolute humidity (AH)). Distribution modeling of new cases across AH and T, helped identify the narrow and vulnerable AH range. We validated the results for 10-day intervals against monthly observations, and also worldwide trends. The results were used to predict Indian regions which would be vulnerable to weather based spread in upcoming months of 2020. COVID-19 spread in the US is significant for states with 4 < AH < 6 g/m3 and number of new cases > 10,000, irrespective of the chosen time intervals for study parameters. These trends are consistent with worldwide observations, but do not correlate well with India so far possibly due the total cases reported per interval < 10,000. The results clarify the relationship between weather parameters and COVID-19 spread. The vulnerable weather parameters will help classify the risky geographic areas in different countries. Specifically, with further reporting of new cases in India, prediction of states with high risk of weather based spread will be apparent.

Title: Temperature, Humidity, and Latitude Analysis to Estimate Potential Spread and Seasonality of Coronavirus Disease 2019 (COVID-19) Content: Importance: Coronavirus disease 2019 (COVID-19) infection has resulted in a global crisis. Investigating the potential association of climate and seasonality with the spread of this infection could aid in preventive and surveillance strategies. Objective: To examine the association of climate with the spread of COVID-19 infection. Design, Setting, and Participants: This cohort study examined climate data from 50 cities worldwide with and without substantial community spread of COVID-19. Eight cities with substantial spread of COVID-19 (Wuhan, China; Tokyo, Japan; Daegu, South Korea; Qom, Iran; Milan, Italy; Paris, France; Seattle, US; and Madrid, Spain) were compared with 42 cities that have not been affected or did not have substantial community spread. Data were collected from January to March 10, 2020. Main Outcomes and Measures: Substantial community transmission was defined as at least 10 reported deaths in a country as of March 10, 2020. Climate data (latitude, mean 2-m temperature, mean specific humidity, and mean relative humidity) were obtained from ERA-5 reanalysis. Results: The 8 cities with substantial community spread as of March 10, 2020, were located on a narrow band, roughly on the 30° N to 50° N corridor. They had consistently similar weather patterns, consisting of mean temperatures of between 5 and 11 °C, combined with low specific humidity (3-6 g/kg) and low absolute humidity (4-7 g/m3). There was a lack of substantial community establishment in expected locations based on proximity. For example, while Wuhan, China (30.8° N) had 3136 deaths and 80â¯757 cases, Moscow, Russia (56.0° N), had 0 deaths and 10 cases and Hanoi, Vietnam (21.2° N), had 0 deaths and 31 cases. Conclusions and Relevance: In this study, the distribution of substantial community outbreaks of COVID-19 along restricted latitude, temperature, and humidity measurements was consistent with the behavior of a seasonal respiratory virus. Using weather modeling, it may be possible to estimate the regions most likely to be at a higher risk of substantial community spread of COVID-19 in the upcoming weeks, allowing for concentration of public health efforts on surveillance and containment.

Title: The correlation between the spread of COVID-19 infections and weather variables in 30 Chinese provinces and the impact of Chinese government mitigation plans Content: On February 1, 2020, China announced a novel coronavirus CoVID-19 outbreak to the public. CoVID-19 was classified as an epidemic by the World Health Organization (WHO). Although the disease was discovered and concentrated in Hubei Province, China, it was exported to all of the other Chinese provinces and spread globally. As of this writing, all plans have failed to contain the novel coronavirus disease, and it has continued to spread to the rest of the world. This study aimed to explore and interpret the effect of environmental and metrological variables on the spread of coronavirus disease in 30 provinces in China, as well as to investigate the impact of new China regulations and plans to mitigate further spread of infections. This article forecasts the size of the disease spreading based on time series forecasting. The growing size of CoVID-19 in China for the next 210 days is estimated by predicting the expected confirmed and recovered cases. The results revealed that weather conditions largely influence the spread of coronavirus in most of the Chinese provinces. This study has determined that increasing temperature and short-wave radiation would positively increase the number of confirmed cases, mortality rate, and recovered cases. The findings of this study agree with the results of our previous study.

Title: The correlation between the spread of COVID-19 infections and weather variables in 30 Chinese provinces and the impact of Chinese government mitigation plans. Content: On February 1, 2020, China announced a novel coronavirus CoVID-19 outbreak to the public. CoVID-19 was classified as an epidemic by the World Health Organization (WHO). Although the disease was discovered and concentrated in Hubei Province, China, it was exported to all of the other Chinese provinces and spread globally. As of this writing, all plans have failed to contain the novel coronavirus disease, and it has continued to spread to the rest of the world. This study aimed to explore and interpret the effect of environmental and metrological variables on the spread of coronavirus disease in 30 provinces in China, as well as to investigate the impact of new China regulations and plans to mitigate further spread of infections. This article forecasts the size of the disease spreading based on time series forecasting. The growing size of CoVID-19 in China for the next 210 days is estimated by predicting the expected confirmed and recovered cases. The results revealed that weather conditions largely influence the spread of coronavirus in most of the Chinese provinces. This study has determined that increasing temperature and short-wave radiation would positively increase the number of confirmed cases, mortality rate, and recovered cases. The findings of this study agree with the results of our previous study.

Title: Short-Term Effects of Ambient Ozone, PM2.5, and Meteorological Factors on COVID-19 Confirmed Cases and Deaths in Queens, New York Content: The outbreak of coronavirus disease 2019 (COVID-19), caused by the virus SARS-CoV-2, has been rapidly increasing in the United States. Boroughs of New York City, including Queens county, turn out to be the epicenters of this infection. According to the data provided by the New York State Department of Health, most of the cases of new COVID-19 infections in New York City have been found in the Queens county where 42,023 people have tested positive, and 3221 people have died as of 20 April 2020. Person-to-person transmission and travels were implicated in the initial spread of the outbreaks, but factors related to the late phase of rapidly spreading outbreaks in March and April are still uncertain. A few previous studies have explored the links between air pollution and COVID-19 infections, but more data is needed to understand the effects of short-term exposures of air pollutants and meteorological factors on the spread of COVID-19 infections, particularly in the U.S. disease epicenters. In this study, we have focused on ozone and PM2.5, two major air pollutants in New York City, which were previously found to be associated with respiratory viral infections. The aim of our regression modeling was to explore the associations among ozone, PM2.5, daily meteorological variables (wind speed, temperature, relative humidity, absolute humidity, cloud percentages, and precipitation levels), and COVID-19 confirmed new cases and new deaths in Queens county, New York during March and April 2020. The results from these analyses showed that daily average temperature, daily maximum eight-hour ozone concentration, average relative humidity, and cloud percentages were significantly and positively associated with new confirmed cases related to COVID-19; none of these variables showed significant associations with new deaths related to COVID-19. The findings indicate that short-term exposures to ozone and other meteorological factors can influence COVID-19 transmission and initiation of the disease, but disease aggravation and mortality depend on other factors.

Title: Singapore COVID-19 Pandemic Response as a Successful Model Framework for Low-Resource Health Care Settings in Africa? Content: The coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus continues to spread and evolve across the planet. The crosscutting impacts of the virus, individual country responses to the virus, and the state of preparedness of local public health systems greatly vary across the world. The ostensibly late arrival of the virus in Africa has allowed learning, innovation, and adaptation of methods that have been successful in the early-hit countries. This article analyzes how Singapore has responded to the COVID-19 pandemic and proposes that adaptations of the Singapore pandemic response model would bode well for Africa's response to the COVID-19 pandemic in ways that also take into account regional differences in health care infrastructures, socioeconomic resilience, poverty, and the vast population diversity in the African continent. As the pandemic evolves, the lessons learned in Asia, in particular, and the emerging new experiences in African countries should inform, ideally in real time, how best to steer the world populations into safety, including those in low-resource health care settings. Finally, we note that the current COVID-19 pandemic is also a test for our collective ability to scale and surge public health in response to future and likely equally challenging zoonosis infections that jump from animals to humans, not to mention climate change-related planetary health calamities in the 21st century. Hence, what we learn effectively from the current COVID-19 pandemic shall have broad, enduring, and intergenerational relevance for the future of planetary heath and society.

Title: Temperature, Humidity, and Latitude Analysis to Estimate Potential Spread and Seasonality of Coronavirus Disease 2019 (COVID-19) Content: IMPORTANCE: Coronavirus disease 2019 (COVID-19) infection has resulted in a global crisis. Investigating the potential association of climate and seasonality with the spread of this infection could aid in preventive and surveillance strategies. OBJECTIVE: To examine the association of climate with the spread of COVID-19 infection. DESIGN, SETTING, AND PARTICIPANTS: This cohort study examined climate data from 50 cities worldwide with and without substantial community spread of COVID-19. Eight cities with substantial spread of COVID-19 (Wuhan, China; Tokyo, Japan; Daegu, South Korea; Qom, Iran; Milan, Italy; Paris, France; Seattle, US; and Madrid, Spain) were compared with 42 cities that have not been affected or did not have substantial community spread. Data were collected from January to March 10, 2020. MAIN OUTCOMES AND MEASURES: Substantial community transmission was defined as at least 10 reported deaths in a country as of March 10, 2020. Climate data (latitude, mean 2-m temperature, mean specific humidity, and mean relative humidity) were obtained from ERA-5 reanalysis. RESULTS: The 8 cities with substantial community spread as of March 10, 2020, were located on a narrow band, roughly on the 30° N to 50° N corridor. They had consistently similar weather patterns, consisting of mean temperatures of between 5 and 11 °C, combined with low specific humidity (3-6 g/kg) and low absolute humidity (4-7 g/m(3)). There was a lack of substantial community establishment in expected locations based on proximity. For example, while Wuhan, China (30.8° N) had 3136 deaths and 80 757 cases, Moscow, Russia (56.0° N), had 0 deaths and 10 cases and Hanoi, Vietnam (21.2° N), had 0 deaths and 31 cases. CONCLUSIONS AND RELEVANCE: In this study, the distribution of substantial community outbreaks of COVID-19 along restricted latitude, temperature, and humidity measurements was consistent with the behavior of a seasonal respiratory virus. Using weather modeling, it may be possible to estimate the regions most likely to be at a higher risk of substantial community spread of COVID-19 in the upcoming weeks, allowing for concentration of public health efforts on surveillance and containment.

Title: A chemical cocktail during the COVID-19 outbreak in Beijing, China: Insights from six-year aerosol particle composition measurements during the Chinese New Year holiday Content: The rapidly spread coronavirus disease (COVID-19) has limited people's outdoor activities and hence caused substantial reductions in anthropogenic emissions around the world. However, the air quality in some megacities has not been improved as expected due to the complex responses of aerosol chemistry to the changes in precursors and meteorology. Here we demonstrate the responses of primary and secondary aerosol species to the changes in anthropogenic emissions during the COVID-19 outbreak in Beijing, China along with the Chinese New Year (CNY) holiday effects on air pollution by using six-year aerosol particle composition measurements. Our results showed large reductions in primary aerosol species associated with traffic, cooking and coal combustion emissions by 30–50% on average during the CNY, while the decreases in secondary aerosol species were much small (5–12%). These results point towards a future challenge in mitigating secondary air pollution because the reduced gaseous precursors may not suppress secondary aerosol formation efficiently under stagnant meteorological conditions. By analyzing the long-term measurements from 2012 to 2020, we found considerable increases in the ratios of nitrate to sulfate, secondary to primary OA, and sulfur and nitrogen oxidation capacity despite the overall decreasing trends in mass concentrations of most aerosol species, suggesting that the decreases in anthropogenic emissions have facilitated secondary formation processes during the last decade. Therefore, a better understanding of the mechanisms driving the chemical responses of secondary aerosol to the changes in anthropogenic emissions under complex meteorological environment is essential for future mitigation of air pollution in China.

Title: A country comparison of place-based activity response to COVID-19 policies Content: The emergence of the novel Coronavirus Disease in late 2019 (COVID-19) and subsequent pandemic led to an immense disruption in the daily lives of almost everyone on the planet. Faced with the consequences of inaction, most national governments responded with policies that restricted the activities conducted by their inhabitants. As schools and businesses shuttered, the mobility of these people decreased. This reduction in mobility, and related activities, was recorded through ubiquitous location-enabled personal mobile devices. Patterns emerged that varied by place-based activity. In this work the differences in these place-based activity patterns are investigated across nations, specifically focusing on the relationship between government enacted policies and changes in community activity patterns. We show that people's activity response to government action varies widely both across nations as well as regionally within them. Three assessment measures are devised and the results correlate with a number of global indices. We discuss these findings and the relationship between government action and residents' response.

Title: Causal empirical estimates suggest COVID-19 transmission rates are highly seasonal Content: Nearly every country is now combating the 2019 novel coronavirus (COVID-19). It has been hypothesized that if COVID-19 exhibits seasonality, changing temperatures in the coming months will shift transmission patterns around the world. Such projections, however, require an estimate of the relationship between COVID-19 and temperature at a global scale, and one that isolates the role of temperature from confounding factors, such as public health capacity. This paper provides the first plausibly causal estimates of the relationship between COVID-19 transmission and local temperature using a global sample comprising of 166,686 confirmed new COVID-19 cases from 134 countries from January 22, 2020 to March 15, 2020. We find robust statistical evidence that a 1°C increase in local temperature reduces transmission by 13% [-21%, -4%, 95%CI]. In contrast, we do not find that specific humidity or precipitation influence transmission. Our statistical approach separates effects of climate variation on COVID-19 transmission from other potentially correlated factors, such as differences in public health responses across countries and heterogeneous population densities. Using constructions of expected seasonal temperatures, we project that changing temperatures between March 2020 and July 2020 will cause COVID-19 transmission to fall by 43% on average for Northern Hemisphere countries and to rise by 71% on average for Southern Hemisphere countries. However, these patterns reverse as the boreal winter approaches, with seasonal temperatures in January 2021 increasing average COVID-19 transmission by 59% relative to March 2020 in northern countries and lowering transmission by 2% in southern countries. These findings suggest that Southern Hemisphere countries should expect greater transmission in the coming months. Moreover, Northern Hemisphere countries face a crucial window of opportunity: if contagion-containing policy interventions can dramatically reduce COVID-19 cases with the aid of the approaching warmer months, it may be possible to avoid a second wave of COVID-19 next winter.

Title: Relationship between Average Daily Temperature and Average Cumulative Daily Rate of Confirmed Cases of COVID-19 Content: The rapid outbreak of the new Coronavirus (COVID-19) pandemic and the spread of the virus worldwide, especially in the Northern Hemisphere, have prompted various investigations about the impact of environmental factors on the rate of development of this epidemic. Different studies have called the attention to various parameters that may have influenced the spread of the virus, and in particular, the impact of climatic parameters has been emphasized. The main purpose of this study is to investigate the correlation between the average daily temperature and the rate of coronavirus epidemic growth in the infected regions. The main hypothesis object of our research is that between regions exhibiting a significant difference in the mean daily temperature, a significant difference is also observed in the average cumulative daily rate of confirmed cases, and that this does not happen if there is no significant difference in mean daily temperature. To test this research hypothesis, we carried on the case study of three regions in each of five countries and analyzed the correlation through F-test, and Independent-Samples T-Test. In all five selected countries, we found that when there is a significant difference in the daily mean temperature between two regions of a country, a significant difference exists also in the average cumulative daily rate of confirmed cases. Conversely, if there are no significant differences in the mean daily temperature of two regions in the same country, no significant difference is observed in the average cumulative daily rate of confirmed cases for these regions.

Title: A mechanism-based parameterisation scheme to investigate the association between transmission rate of COVID-19 and meteorological factors on plains in China Content: The novel coronavirus disease 2019 (COVID-19), which first emerged in Hubei province, China, has become a pandemic. However, data regarding the effects of meteorological factors on its transmission are limited and inconsistent. A mechanism-based parameterisation scheme was developed to investigate the association between the scaled transmission rate (STR) of COVID-19 and the meteorological parameters in 20 provinces/municipalities located on the plains in China. We obtained information on the scale of population migrated from Wuhan, the world epicentre of the COVID-19 outbreak, into the study provinces/municipalities using mobile-phone positioning system and big data techniques. The highest STRs were found in densely populated metropolitan areas and in cold provinces located in north-eastern China. Population density had a non-linear relationship with disease spread (linearity index, 0.9). Among various meteorological factors, only temperature was significantly associated with the STR after controlling for the effect of population density. A negative and exponential relationship was identified between the transmission rate and the temperature (correlation coefficient, -0.56; 99% confidence level). The STR increased substantially as the temperature in north-eastern China decreased below 0 °C (the STR ranged from 3.5 to 12.3 when the temperature was between -9.41 °C and -13.87 °C), whilst the STR showed less temperature dependence in the study areas with temperate weather conditions (the STR was 1.21 ± 0.57 when the temperature was above 0 °C). Therefore, a higher population density was linearly whereas a lower temperature (<0 °C) was exponentially associated with an increased transmission rate of COVID-19. These findings suggest that the mitigation of COVID-19 spread in densely populated and/or cold regions will be a great challenge.

Title: The Wider Impacts of the Coronavirus Pandemic on the NHS Content: The coronavirus pandemic has had huge impacts on the National Health Service (NHS). Patients suffering from the illness have placed unprecedented demands on acute care, particularly on Intensive Care Units (ICUs). This has led to an effort to dramatically increase the resources available to NHS hospitals in treating these patients, involving re‐organisation of hospital facilities, redeployment of existing staff and a drive to bring in recently retired and newly graduated staff to fight the pandemic. These increases in demand and changes to supply have had large knock‐on effects on the care provided to the wider population. This paper discusses likely implications for healthcare delivery in the short and medium term of the responses to the coronavirus pandemic, focusing primarily on the implications for non‐coronavirus patients. Patterns of past care suggest those most likely to be affected by these disruptions will be older individuals and those living in more deprived areas, potentially exacerbating pre‐existing health inequalities. Effects are likely to persist into the longer‐run, with particular challenges around recruitment and ongoing staff shortages. This article is protected by copyright. All rights reserved

Title: Association of COVID-19 pandemic with meteorological parameters over Singapore Content: Meteorological parameters are the critical factors affecting the transmission of infectious diseases such as Middle East Respiratory Syndrome (MERS), Severe Acute Respiratory Syndrome (SARS), and influenza. Consequently, infectious disease incidence rates are likely to be influenced by the weather change. This study investigates the role of Singapore's hot tropical weather in COVID-19 transmission by exploring the association between meteorological parameters and the COVID-19 pandemic cases in Singapore. This study uses the secondary data of COVID-19 daily cases from the webpage of Ministry of Health (MOH), Singapore. Spearman and Kendall rank correlation tests were used to investigate the correlation between COVID-19 and meteorological parameters. Temperature, dew point, relative humidity, absolute humidity, and water vapor showed positive significant correlation with COVID-19 pandemic. These results will help the epidemiologists to understand the behavior of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) virus against meteorological variables. This study finding would be also a useful supplement to help the local healthcare policymakers, Center for Disease Control (CDC), and the World Health Organization (WHO) in the process of strategy making to combat COVID-19 in Singapore.

Title: Distribution of the SARS-CoV-2 Pandemic and Its Monthly Forecast Based on Seasonal Climate Patterns Content: This paper investigates whether the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) pandemic could have been favored by specific weather conditions and other factors. It is found that the 2020 winter weather in the region of Wuhan (Hubei, Central China)—where the virus first broke out in December and spread widely from January to February 2020—was strikingly similar to that of the Northern Italian provinces of Milan, Brescia and Bergamo, where the pandemic broke out from February to March. The statistical analysis was extended to cover the United States of America, which overtook Italy and China as the country with the highest number of confirmed COronaVIrus Disease 19 (COVID-19) cases, and then to the entire world. The found correlation patterns suggest that the COVID-19 lethality significantly worsens (4 times on average) under weather temperatures between 4 °C and 12 °C and relative humidity between 60% and 80%. Possible co-factors such as median population age and air pollution were also investigated suggesting an important influence of the former but not of the latter, at least, on a synoptic scale. Based on these results, specific isotherm world maps were generated to locate, month by month, the world regions that share similar temperature ranges. From February to March, the 4–12 °C isotherm zone extended mostly from Central China toward Iran, Turkey, West-Mediterranean Europe (Italy, Spain and France) up to the United State of America, optimally coinciding with the geographic regions most affected by the pandemic from February to March. It is predicted that in the spring, as the weather gets warm, the pandemic will likely worsen in northern regions (United Kingdom, Germany, East Europe, Russia and North America) while the situation will likely improve in the southern regions (Italy and Spain). However, in autumn, the pandemic could come back and affect the same regions again. The Tropical Zone and the entire Southern Hemisphere, but in restricted colder southern regions, could avoid a strong pandemic because of the sufficiently warm weather during the entire year and because of the lower median age of their population. Google-Earth-Pro interactive-maps covering the entire world are provided as supplementary files.

Title: Containing the Spread of Coronavirus Disease 2019 (COVID-19): Meteorological Factors and Control Strategies Content: The novel coronavirus disease 2019 (COVID-19) has spread globally and the meteorological factors vary greatly across the world. Understanding the effect of meteorological factors and control strategies on COVID-19 transmission is critical to contain the epidemic. Using individual-level data in mainland China, Hong Kong, and Singapore, and the number of confirmed cases in other regions, we explore the effect of temperature, relative humidity, and control measures on the spread of COVID-19. We found that high temperature mitigates the transmission of the disease. High relative humidity promotes COVID-19 transmission when temperature is low, but tends to reduce transmission when temperature is high. Implementing classical control measures can dramatically slow the spread of the disease. However, due to the occurrence of pre-symptomatic infections, the effect of the measures to shorten onset-to-isolation time is markedly reduced and the importance of contact tracing and quarantine and social distancing increases. The analytic results also highlight the importance of early intervention to contain the spread of COVID-19.

Title: A mechanism-based parameterisation scheme to investigate the association between transmission rate of COVID-19 and meteorological factors on plains in China Content: The novel coronavirus disease 2019 (COVID-19), which first emerged in Hubei province, China, has become a pandemic. However, data regarding the effects of meteorological factors on its transmission are limited and inconsistent. A mechanism-based parameterisation scheme was developed to investigate the association between the scaled transmission rate (STR) of COVID-19 and the meteorological parameters in 20 provinces/municipalities located on the plains in China. We obtained information on the scale of population migrated from Wuhan, the world epicentre of the COVID-19 outbreak, into the study provinces/municipalities using mobile-phone positioning system and big data techniques. The highest STRs were found in densely populated metropolitan areas and in cold provinces located in north-eastern China. Population density had a non-linear relationship with disease spread (linearity index, 0.9). Among various meteorological factors, only temperature was significantly associated with the STR after controlling for the effect of population density. A negative and exponential relationship was identified between the transmission rate and the temperature (correlation coefficient, −0.56; 99% confidence level). The STR increased substantially as the temperature in north-eastern China decreased below 0 °C (the STR ranged from 3.5 to 12.3 when the temperature was between −9.41 °C and −13.87 °C), whilst the STR showed less temperature dependence in the study areas with temperate weather conditions (the STR was 1.21 ± 0.57 when the temperature was above 0 °C). Therefore, a higher population density was linearly whereas a lower temperature (<0 °C) was exponentially associated with an increased transmission rate of COVID-19. These findings suggest that the mitigation of COVID-19 spread in densely populated and/or cold regions will be a great challenge.

Title: COVID-19 and Environmental factors. A PRISMA-compliant systematic review Content: The emergence of a novel human coronavirus, SARS-CoV-2, has become a global health concern causing severe respiratory tract infections in humans. Human-to-human transmissions have been described with incubation times between 2-10 days, facilitating its airborne spread via droplets. The impact of environmental factors on the coronavirus disease 2019 (COVID-19) outbreak is under consideration. We therefore reviewed the literature on all available information about the impact of environmental factors on human coronaviruses. Temperature, humidity and other environmental factors have been recorded as environmental drivers of the COVID-19 outbreak in China and in other countries. Higher temperatures might be positive to decrease the COVID-19 incidence. In our review, the analysis of 23 studies show evidence that high temperature and high humidity reduce the COVID-19 transmission. However, further studies concerning other environmental (namely meteorological) factors role should be conducted in order to further prove this correlation. As no specific therapies are available for SARS-CoV-2, early containment and prevention of further spread will be crucial to stop the ongoing outbreak and to control this novel infectious thread.

Title: Effects of temperature variation and humidity on the death of COVID-19 in Wuhan, China Content: Abstract Meteorological parameters are the important factors influencing the infectious diseases such as severe acute respiratory syndrome (SARS) and influenza. This study aims to explore the association between Corona Virus Disease 2019 (COVID-19) deaths and weather parameters. In this study, we collected the daily death numbers of COVID-19, meteorological parameters and air pollutant data from 20 January 2020 to 29 February 2020 in Wuhan, China. Generalized additive model was applied to explore the effect of temperature, humidity and diurnal temperature range on the daily death counts of COVID-19. There were 2299 COVID-19 death counts in Wuhan during the study period. A positive association with COVID-19 daily death counts was observed for diurnal temperature range (r = 0.44), but negative association for relative humidity (r = −0.32). In addition, one unit increase in diurnal temperature range was only associated with a 2.92% (95% CI: 0.61%, 5.28%) increase in COVID-19 deaths in lag 3. However, both 1 unit increase of temperature and absolute humidity were related to the decreased COVID-19 death in lag 3 and lag 5, with the greatest decrease both in lag 3 [−7.50% (95% CI: −10.99%, −3.88%) and −11.41% (95% CI: −19.68%, −2.29%)]. In summary, this study suggests the temperature variation and humidity may also be important factors affecting the COVID-19 mortality.

Title: Models of transmission of COVID-19 with time under the influence of meteorological determinants Content: Based on the statistical analyses of the data on the number of confirmed COVID-19 cases and meteorological determinants in some of the severely affected cities in Spain, Italy and the USA, some models are constructed showing the relationship of I' (the number of infected individuals divided by the total population of a city) with temperature, relative humidity, wind velocity and time. Three models are based on the data before lockdown/travel restrictions in these cities, and the other three models are based on data both before and after lockdown/travel restrictions. These models, in general, indicate that the transmission of COVID-19 could be relatively high either for elevated temperatures with lower relative humidity or for lower temperatures with higher relative humidity conditions. Although one model indicates exponential increase in number of infection cases with time, the more statistically significant models show that the number of cases varies quadratically with time. We have discussed in short, how all these features could be linked with the alterations of structural characteristics of the SARS-CoV-2 virus. Finally, the possibility of natutal disappearance of COVID-19 pandemic, at the global level, has been discussed in the context of the most statistically significant model.

Title: A multi-hazards earth science perspective on the COVID-19 pandemic: the potential for concurrent and cascading crises Content: Meteorological and geophysical hazards will concur and interact with coronavirus disease (COVID-19) impacts in many regions on Earth. These interactions will challenge the resilience of societies and systems. A comparison of plausible COVID-19 epidemic trajectories with multi-hazard time-series curves enables delineation of multi-hazard scenarios for selected countries (United States, China, Australia, Bangladesh) and regions (Texas). In multi-hazard crises, governments and other responding agents may be required to make complex, highly compromised, hierarchical decisions aimed to balance COVID-19 risks and protocols with disaster response and recovery operations. Contemporary socioeconomic changes (e.g. reducing risk mitigation measures, lowering restrictions on human activity to stimulate economic recovery) may alter COVID-19 epidemiological dynamics and increase future risks relating to natural hazards and COVID-19 interactions. For example, the aggregation of evacuees into communal environments and increased demand on medical, economic, and infrastructural capacity associated with natural hazard impacts may increase COVID-19 exposure risks and vulnerabilities. COVID-19 epidemiologic conditions at the time of a natural hazard event might also influence the characteristics of emergency and humanitarian responses (e.g. evacuation and sheltering procedures, resource availability, implementation modalities, and assistance types). A simple epidemic phenomenological model with a concurrent disaster event predicts a greater infection rate following events during the pre-infection rate peak period compared with post-peak events, highlighting the need for enacting COVID-19 counter measures in advance of seasonal increases in natural hazards. Inclusion of natural hazard inputs into COVID-19 epidemiological models could enhance the evidence base for informing contemporary policy across diverse multi-hazard scenarios, defining and addressing gaps in disaster preparedness strategies and resourcing, and implementing a future-planning systems approach into contemporary COVID-19 mitigation strategies. Our recommendations may assist governments and their advisors to develop risk reduction strategies for natural and cascading hazards during the COVID-19 pandemic. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s10669-020-09772-1) contains supplementary material, which is available to authorized users.

Title: Covid-19: implications for prehospital, emergency and hospital care in patients with acute coronary syndromes Content: Hospitals play a critical role in providing communities with essential medical care during all types of disaster. Depending on their scope and nature, disasters can lead to a rapidly increasing service demand that can overwhelm the functional capacity and safety of hospitals and the healthcare system at large. Planning during the community outbreak of coronavirus disease 2019 (Covid-19) is critical for maintaining healthcare services during our response. This paper describes, besides general measures in times of a pandemic, also the necessary changes in the invasive diagnosis and treatment of patients presenting with different entities of acute coronary syndromes including structural adaptations (networks, spokes and hub centres) and therapeutic adjustments.

Title: Meteorological Conditions and Covid-19 in Large U.S. Cities Content: To determine whether prevalence of Coronavirus disease 2019 (Covid-19) is modulated by meteorological conditions, we herein conducted meta-regression of data in large U.S. cities. We selected 33 large U.S. cities with a population of >500,000. The integrated numbers of confirmed Covid-19 cases in the country to which the city belongs on 14 May 2020, the estimated population in 2019 in the country, and monthly meteorological conditions at the city for 4 months (from January to April 2020) were obtained. Meteorological conditions consisted of mean temperature (F), total precipitation (inch), mean wind speed (MPH), mean sky cover, and mean relative humidity (%). Monthly data for 4 months were averaged or integrated. The Covid-19 prevalence was defined as the integrated number of Covid-19 cases divided by the population. Random-effects meta-regression was performed by means of OpenMetaAnalyst. In a meta-regression graph, Covid-19 prevalence (plotted as the logarithm transformed prevalence on the y-axis) was depicted as a function of a given factor (plotted as a meteorological datum on the x-axis). A slope of the meta-regression line was significantly negative (coefficient, -0.069; P < 0.001) for the mean temperature and significantly positive for the mean wind speed (coefficient, 0.174; P = 0.027) and the sky cover (coefficient, 2.220; P = 0.023). In conclusion, lower temperature and higher wind speed/sky cover may be associated with higher Covid-19 prevalence, which should be confirmed by further epidemiological researches adjusting for various risk and protective factors (in addition to meteorological conditions) of Covid-19.

Title: Mitigating the twin threats of climate-driven Atlantic hurricanes and COVID-19 transmission. Content: The co-occurrence of the 2020 Atlantic hurricane season and the ongoing Coronavirus Disease 2019 (COVID-19) pandemic creates complex dilemmas for protecting populations from these intersecting threats. Climate change is likely contributing to stronger, wetter, slower-moving, and more dangerous hurricanes. Climate-driven hazards underscore the imperative for timely warning, evacuation, and sheltering of storm-threatened populations - proven life-saving protective measures that gather evacuees together inside durable enclosed spaces when a hurricane approaches. Meanwhile, the rapid acquisition of scientific knowledge regarding how COVID-19 spreads has guided mass anti-contagion strategies including lockdowns, sheltering-at-home, physical distancing, donning personal protective equipment, conscientious hand washing, and hygiene practices. These life-saving strategies, credited with preventing millions of COVID-19 cases, separate and move people apart. Enforcement coupled with fear of contracting COVID-19 have motivated high levels of adherence to these stringent regulations. How will populations react when warned to shelter from an oncoming Atlantic hurricane while COVID-19 is actively circulating in the community? Emergency managers, healthcare providers, and public health preparedness professionals must create viable solutions to confront these potential scenarios: elevated rates of hurricane-related injury and mortality among persons who refuse to evacuate due to fear of COVID-19, and resurgence of COVID-19 cases among hurricane evacuees who shelter together.

Title: Predictive Modeling on the Number of Covid-19 Death Toll in the United States Considering the Effects of Coronavirus-Related Changes and Covid-19 Recovered Cases Content: COVID-19 is caused by a coronavirus called SARS-CoV-2. Many countries around the world implemented their own policies and restrictions designed to limit the spread of Covid-19 in recent months. Businesses and schools transitioned into working and learning remotely. In the United States, many states were under strict orders to stay home at least in the month of April. In recent weeks, there are some significant changes related restrictions include social-distancing, reopening states, and staying-at-home orders. The United States surpassed 2 million coronavirus cases on Monday, June 15, 2020 less than five months after the first case was confirmed in the country. The virus has killed at least 115,000 people in the United States as of Monday, June 15, 2020, according to data from Johns Hopkins University. With the recent easing of coronavirus-related restrictions and changes on business and social activity such as stay-at-home, social distancing since late May 2020 hoping to restore economic and business activities, new Covid-19 outbreaks are on the rise in many states across the country. Some researchers expressed concern that the process of easing restrictions and relaxing stay-at-home orders too soon could quickly surge the number of infected Covid-19 cases as well as the death toll in the United States. Some of these increases, however, could be due to more testing sites in the communities while others may be are the results of easing restrictions due to recent reopening and changed policies, though the number of daily death toll does not appear to be going down in recent days due to Covid-19 in the U.S. This raises the challenging question: * How can policy decision-makers and community leaders make the decision to implement public policies and restrictions and keep or lift staying-at-home orders of ongoing Covid-19 pandemic for their communities in a scientific way? In this study, we aim to develop models addressing the effects of recent Covid-19 related changes in the communities such as reopening states, practicing social-distancing, and staying-at-home orders. Our models account for the fact that changes to these policies which can lead to a surge of coronavius cases and deaths, especially in the United States. Specifically, in this paper we develop a novel generalized mathematical model and several explicit models considering the effects of recent reopening states, staying-at-home orders and social-distancing practice of different communities along with a set of selected indicators such as the total number of coronavirus recovered and new cases that can estimate the daily death toll and total number of deaths in the United States related to Covid-19 virus. We compare the modeling results among the developed models based on several existing criteria. The model also can be used to predict the number of death toll in Italy and the United Kingdom (UK). The results show very encouraging predictability for the proposed models in this study. The model predicts that 128,500 to 140,100 people in the United States will have died of Covid-19 by July 4, 2020. The model also predicts that between 137,900 and 154,000 people will have died of Covid-19 by July 31, and 148,500 to 169,700 will have died by the end of August 2020, as a result of the SARS-CoV-2 coronavirus that causes COVID-19 based on the Covid-19 death data available on June 13, 2020. The model also predicts that 34,900 to 37,200 people in Italy will have died of Covid-19 by July 4, and 36,900 to 40,400 people will have died by the end of August based on the data available on June 13, 2020. The model also predicts that between 43,500 and 46,700 people in the United Kingdom will have died of Covid-19 by July 4, and 48,700 to 51,900 people will have died by the end of August, as a result of the SARS-CoV-2 coronavirus that causes COVID-19 based on the data available on June 13, 2020. The model can serve as a framework to help policy makers a scientific approach in quantifying decision-makings related to Covid-19 affairs.

Title: Distribution of the SARS-CoV-2 Pandemic and Its Monthly Forecast Based on Seasonal Climate Patterns Content: This paper investigates whether the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) pandemic could have been favored by specific weather conditions and other factors. It is found that the 2020 winter weather in the region of Wuhan (Hubei, Central China)-where the virus first broke out in December and spread widely from January to February 2020-was strikingly similar to that of the Northern Italian provinces of Milan, Brescia and Bergamo, where the pandemic broke out from February to March. The statistical analysis was extended to cover the United States of America, which overtook Italy and China as the country with the highest number of confirmed COronaVIrus Disease 19 (COVID-19) cases, and then to the entire world. The found correlation patterns suggest that the COVID-19 lethality significantly worsens (4 times on average) under weather temperatures between 4 ∘ C and 12 ∘ C and relative humidity between 60% and 80%. Possible co-factors such as median population age and air pollution were also investigated suggesting an important influence of the former but not of the latter, at least, on a synoptic scale. Based on these results, specific isotherm world maps were generated to locate, month by month, the world regions that share similar temperature ranges. From February to March, the 4-12 ∘ C isotherm zone extended mostly from Central China toward Iran, Turkey, West-Mediterranean Europe (Italy, Spain and France) up to the United State of America, optimally coinciding with the geographic regions most affected by the pandemic from February to March. It is predicted that in the spring, as the weather gets warm, the pandemic will likely worsen in northern regions (United Kingdom, Germany, East Europe, Russia and North America) while the situation will likely improve in the southern regions (Italy and Spain). However, in autumn, the pandemic could come back and affect the same regions again. The Tropical Zone and the entire Southern Hemisphere, but in restricted colder southern regions, could avoid a strong pandemic because of the sufficiently warm weather during the entire year and because of the lower median age of their population. Google-Earth-Pro interactive-maps covering the entire world are provided as supplementary files.

Title: Taking the pulse of COVID-19: A spatiotemporal perspective Content: The sudden outbreak of the Coronavirus disease (COVID-19) swept across the world in early 2020, triggering the lockdowns of several billion people across many countries, including China, Spain, India, the U.K., Italy, France, Germany, and most states of the U.S. The transmission of the virus accelerated rapidly with the most confirmed cases in the U.S., and New York City became an epicenter of the pandemic by the end of March. In response to this national and global emergency, the NSF Spatiotemporal Innovation Center brought together a taskforce of international researchers and assembled implemented strategies to rapidly respond to this crisis, for supporting research, saving lives, and protecting the health of global citizens. This perspective paper presents our collective view on the global health emergency and our effort in collecting, analyzing, and sharing relevant data on global policy and government responses, geospatial indicators of the outbreak and evolving forecasts; in developing research capabilities and mitigation measures with global scientists, promoting collaborative research on outbreak dynamics, and reflecting on the dynamic responses from human societies.

Title: Association between ambient temperature and COVID-19 infection in 122 cities from China Content: Abstract Background Coronavirus disease 2019 (COVID-19) has become a severe public health problem globally. Both epidemiological and laboratory studies have shown that ambient temperature could affect the transmission and survival of coronaviruses. This study aimed to determine whether the temperature is an essential factor in the infection caused by this novel coronavirus. Methods Daily confirmed cases and meteorological factors in 122 cities were collected between January 23, 2020, to February 29, 2020. A generalized additive model (GAM) was applied to explore the nonlinear relationship between mean temperature and COVID-19 confirmed cases. We also used a piecewise linear regression to determine the relationship in detail. Results The exposure-response curves suggested that the relationship between mean temperature and COVID-19 confirmed cases was approximately linear in the range of <3 °C and became flat above 3 °C. When mean temperature (lag0–14) was below 3 °C, each 1 °C rise was associated with a 4.861% (95% CI: 3.209–6.513) increase in the daily number of COVID-19 confirmed cases. These findings were robust in our sensitivity analyses. Conclusions Our results indicate that mean temperature has a positive linear relationship with the number of COVID-19 cases with a threshold of 3 °C. There is no evidence supporting that case counts of COVID-19 could decline when the weather becomes warmer, which provides useful implications for policymakers and the public.

Title: The most eagerly awaited summer of the Anthropocene: A perspective of SARS-CoV-2 decay and seasonal change Content: To date, the world perhaps has never waited for the summer so impatiently in the entire Anthropocene, owing to the debate whether increasing temperature and humidity will decrease the environmental endurance of SARS-CoV-2. We present the perspective on the seasonal change on SARS-CoV-2 decay and COVID-19 spread. Our arguments are based on: i) structural similarity of coronavirus with several enteric viruses, and its vulnerability; ii) reports related to decay of those similar transmissible gastroenteritis viruses (TGEV) like norovirus and iii) improvement in the human immunity during summer with respect to winter. We present reasons why we can be optimistic about the slowdown of corona in the upcoming summer.

Title: New Coronavirus Infection (Covid-19): Clinical and Epidemiological Aspects ;Новая коронавирусная инфекция (COVID-19): клинико-эпидемиологические аспекты Content: Environmental change, climate warming, population density increase, high migration activity of the population and other factors provoke the emergence and spread of new infections around the world The emergence in December 2019 of diseases caused by the new coronavirus («coronavirus disease 2019») has already gone down in history as an emergency of international importance It is known that the most common clinical manifestation of a new infection is pneumonia, and also in a significant part of patients — respiratory distress syndrome Our article provides a brief analytical review of these temporary guidelines Ministry of Health of the Russian Federation «Prevention, Diagnosis and Treatment of a New Coronavirus Infection (COVID-19)», version 3 (03 03 20) and other published sources The team of authors expresses the hope that these data will be useful to doctors in providing medical care to patients with a new coronary virus infection, as well as to teachers in preparing students and residents Source: Ministry of Health of the Russian Federation Temporary guidelines «Prevention, diagnosis and treatment of new coronavirus infection (COVID-19)», version 4 (27 03 20) Available on: https://static-3 rosminzdrav ru/system/attachments/attaches/000/049/881/original/COVID19_recomend_v4 pdf ;Изменение окружающей среды, потепление климата, увеличение плотности населения высокая миграционная активность населения и другие факторы провоцируют появление и распространение новых инфекций по всему миру Появление в декабре 2019 года заболеваний, вызванных новым коронавирусом («coronavirus disease 2019»), уже вошло в историю как чрезвычайная ситуация международного значения Известно, что наиболее распространенным клиническим проявлением новой инфекции является пневмония, а также у значительной части пациентов — респираторный дистресс-синдром В нашей статье представлен краткий аналитический обзор данных временных методических рекомендаций Министерства здравоохранения Российской Федерации «Профилактика, диагностика и лечение новой коронавирусной инфекции (COVID-19)», версия 3 (03 03 20) и других литературных источников Авторский коллектив выражает надежду, что эти данные будут полезны врачам при оказании медицинской помощи больным новой коронавирусной инфекцией, а также преподавателям при подготовке студентов и ординаторов Источник: Министерство здравоохранения Российской Федерации Временные методические рекомендации «Профилактика, диагностика и лечение новой коронавирусной инфекции (COVID-19)», версия 4 (27 03 20) Доступно на: https://static-3 rosminzdrav ru/system/attachments/attaches/000/049/881/original/COVID19_recomend_v pdf

Title: Air quality changes during the COVID-19 lockdown over the Yangtze River Delta Region: An insight into the impact of human activity pattern changes on air pollution variation Content: The outbreak of COVID-19 has spreaded rapidly across the world. To control the rapid dispersion of the virus, China has imposed national lockdown policies to practise social distancing. This has led to reduced human activities and hence primary air pollutant emissions, which caused improvement of air quality as a side-product. To investigate the air quality changes during the COVID-19 lockdown over the YRD Region, we apply the WRF-CAMx modelling system together with monitoring data to investigate the impact of human activity pattern changes on air quality. Results show that human activities were lowered significantly during the period: industrial operations, VKT, constructions in operation, etc. were significantly reduced, leading to lowered SO2, NOx, PM2.5 and VOCs emissions by approximately 16-26%, 29-47%, 27-46% and 37-57% during the Level I and Level II response periods respectively. These emission reduction has played a significant role in the improvement of air quality. Concentrations of PM2.5, NO2 and SO2 decreased by 31.8%, 45.1% and 20.4% during the Level I period; and 33.2%, 27.2% and 7.6% during the Level II period compared with 2019. However, ozone did not show any reduction and increased greatly. Our results also show that even during the lockdown, with primary emissions reduction of 15%-61%, the daily average PM2.5 concentrations range between 15 and 79 µg m-3, which shows that background and residual pollutions are still high. Source apportionment results indicate that the residual pollution of PM2.5 comes from industry (32.2-61.1%), mobile (3.9-8.1%), dust (2.6-7.7%), residential sources (2.1-28.5%) in YRD and 14.0-28.6% contribution from long-range transport coming from northern China. This indicates that in spite of the extreme reductions in primary emissions, it cannot fully tackle the current air pollution. Re-organisation of the energy and industrial strategy together with trans-regional joint-control for a full long-term air pollution plan need to be further taken into account.

Title: Susceptible supply limits the role of climate in the early SARS-CoV-2 pandemic Content: Preliminary evidence suggests that climate may modulate the transmission of SARS-CoV-2. Yet it remains unclear whether seasonal and geographic variations in climate can substantially alter the pandemic trajectory, given high susceptibility is a core driver. Here, we use a climate-dependent epidemic model to simulate the SARS-CoV-2 pandemic probing different scenarios based on known coronavirus biology. We find that while variations in weather may be important for endemic infections, during the pandemic stage of an emerging pathogen the climate drives only modest changes to pandemic size. A preliminary analysis of non-pharmaceutical control measures indicates that they may moderate the pandemic-climate interaction via susceptible depletion. Our findings suggest, without effective control measures, strong outbreaks are likely in more humid climates and summer weather will not substantially limit pandemic growth.

Title: Impact of meteorological factors on the COVID-19 transmission: A multi-city study in China Content: The purpose of the present study is to explore the associations between novel coronavirus disease 2019 (COVID-19) case counts and meteorological factors in 30 provincial capital cities of China. We compiled a daily dataset including confirmed case counts, ambient temperature (AT), diurnal temperature range (DTR), absolute humidity (AH) and migration scale index (MSI) for each city during the period of January 20th to March 2nd, 2020. First, we explored the associations between COVID-19 confirmed case counts, meteorological factors, and MSI using non-linear regression. Then, we conducted a two-stage analysis for 17 cities with more than 50 confirmed cases. In the first stage, generalized linear models with negative binomial distribution were fitted to estimate city-specific effects of meteorological factors on confirmed case counts. In the second stage, the meta-analysis was conducted to estimate the pooled effects. Our results showed that among 13 cities that have less than 50 confirmed cases, 9 cities locate in the Northern China with average AT below 0 °C, 12 cities had average AH below 4 g/m3, and one city (Haikou) had the highest AH (14.05 g/m3). Those 17 cities with 50 and more cases accounted for 90.6% of all cases in our study. Each 1 °C increase in AT and DTR was related to the decline of daily confirmed case counts, and the corresponding pooled RRs were 0.80 (95% CI: 0.75, 0.85) and 0.90 (95% CI: 0.86, 0.95), respectively. For AH, the association with COVID-19 case counts were statistically significant in lag 07 and lag 014. In addition, we found the all these associations increased with accumulated time duration up to 14 days. In conclusions, meteorological factors play an independent role in the COVID-19 transmission after controlling population migration. Local weather condition with low temperature, mild diurnal temperature range and low humidity likely favor the transmission.

Title: Effect of weather on COVID-19 spread in the US: A prediction model for India in 2020 Content: Abstract The effect of weather on COVID-19 spread is poorly understood. Recently, few studies have claimed that warm weather can possibly slowdown the global pandemic, which has already affected over 1.6 million people worldwide. Clarification of such relationships in the worst affected country, the US, can be immensely beneficial to understand the role of weather in transmission of the disease in the highly populated countries, such as India. We collected the daily data of new cases in 50 US states between Jan 1–Apr 9, 2020 and also the corresponding weather information (i.e., temperature (T) and absolute humidity (AH)). Distribution modeling of new cases across AH and T, helped identify the narrow and vulnerable AH range. We validated the results for 10-day intervals against monthly observations, and also worldwide trends. The results were used to predict Indian regions which would be vulnerable to weather based spread in upcoming months of 2020. COVID-19 spread in the US is significant for states with 4 < AH < 6 g/m3 and number of new cases > 10,000, irrespective of the chosen time intervals for study parameters. These trends are consistent with worldwide observations, but do not correlate well with India so far possibly due the total cases reported per interval < 10,000. The results clarify the relationship between weather parameters and COVID-19 spread. The vulnerable weather parameters will help classify the risky geographic areas in different countries. Specifically, with further reporting of new cases in India, prediction of states with high risk of weather based spread will be apparent.

Title: Short-term effects of weather parameters on COVID-19 morbidity in select US cities Content: Abstract Little is known about the environmental conditions that drive the spatiotemporal patterns of SARS-CoV-2, and preliminary research suggests an association with weather parameters. However, the relationship with temperature and humidity is not yet apparent for COVID-19 cases in US cities first impacted. The objective of this study is to evaluate the association between COVID-19 cases and weather parameters in select US cities. A case-crossover design with a distributed lag nonlinear model was used to evaluate the contribution of ambient temperature and specific humidity on COVID-19 cases in select US cities. The case-crossover examines each COVID case as its own control at different time periods (before and after transmission occurred). We modeled the effect of temperature and humidity on COVID-19 transmission using a lag period of 7 days. A subset of 8 cities were evaluated for the relationship with weather parameters and 5 cities were evaluated in detail. Short-term exposure to humidity was positively associated with COVID-19 transmission in 4 cities. The associations were small with ¾ cities exhibiting higher COVID19 transmission with specific humidity that ranged from 6 to 9 g/kg. Our results suggest that weather should be considered in infectious disease modeling efforts and future work is needed over a longer time period and across different locations to clearly establish the weather-COVID19 relationship.

Title: Containing the spread of coronavirus disease 2019 (COVID-19): Meteorological factors and control strategies Content: Abstract The novel coronavirus disease 2019 (COVID-19) has spread globally and the meteorological factors vary greatly across the world. Understanding the effect of meteorological factors and control strategies on COVID-19 transmission is critical to contain the epidemic. Using individual-level data in mainland China, Hong Kong, and Singapore, and the number of confirmed cases in other regions, we explore the effect of temperature, relative humidity, and control measures on the spread of COVID-19. We find that high temperature mitigates the transmission of the disease. High relative humidity promotes COVID-19 transmission when temperature is low, but tends to reduce transmission when temperature is high. Implementing classical control measures can dramatically slow the spread of the disease. However, due to the occurrence of pre-symptomatic infections, the effect of the measures to shorten treatment time is markedly reduced and the importance of contact quarantine and social distancing increases.

Title: Temperature significantly changes COVID-19 transmission in (sub)tropical cities of Brazil Content: The coronavirus disease 2019 (COVID-19) outbreak has become a severe public health issue. The novelty of the virus prompts a search for understanding of how ecological factors affect the transmission and survival of the virus. Several studies have robustly identified a relationship between temperature and the number of cases. However, there is no specific study for a tropical climate such as Brazil. This work aims to determine the relationship of temperature to COVID-19 infection for the state capital cities of Brazil. Cumulative data with the daily number of confirmed cases was collected from February 27 to April 1, 2020, for all 27 state capital cities of Brazil affected by COVID-19. A generalized additive model (GAM) was applied to explore the linear and nonlinear relationship between annual average temperature compensation and confirmed cases. Also, a polynomial linear regression model was proposed to represent the behavior of the growth curve of COVID-19 in the capital cities of Brazil. The GAM dose-response curve suggested a negative linear relationship between temperatures and daily cumulative confirmed cases of COVID-19 in the range from 16.8 °C to 27.4 °C. Each 1 °C rise of temperature was associated with a -4.8951% (t = -2.29, p = 0.0226) decrease in the number of daily cumulative confirmed cases of COVID-19. A sensitivity analysis assessed the robustness of the results of the model. The predicted R-squared of the polynomial linear regression model was 0.81053. In this study, which features the tropical temperatures of Brazil, the variation in annual average temperatures ranged from 16.8 °C to 27.4 °C. Results indicated that temperatures had a negative linear relationship with the number of confirmed cases. The curve flattened at a threshold of 25.8 °C. There is no evidence supporting that the curve declined for temperatures above 25.8 °C. The study had the goal of supporting governance for healthcare policymakers.

Title: Predict the next moves of COVID-19: reveal the temperate and tropical countries scenario Content: The spread of COVID-19 engulfs almost all the countries and territories of the planet, and infections and fatality are increasing rapidly. The first epi-center of its' massive spread was in Wuhan, Hubei province, China having a temperate weather, but the spread has got an unprecedented momentum in European temperate countries mainly in Italy and Spain (as of March 30, 2020). However, Malaysia and Singapore and the neighboring tropical countries of China got relatively low spread and fatality that created a research interest on whether there are potential impacts of weather condition on COVID-19 spread. Adopting the SIR (Susceptible Infected Removed) deviated model to predict potential cases and death in the coming days from COVID-19 was done using the secondary and official sources of data. This study shows that COVID-19 spread and fatality tend to be high across the world but compared to tropical countries, it is going to be incredibly high in the temperate countries having lower temperature (7-16°C) and humidity (80-90%) in last March. However, some literature predicted that this might not to be true, rather irrespective of weather conditions there might be a continuous spread and death. Moreover, a large number of asymptotic COVID-19 carrier in both temperate and tropical countries may re-outbreak in the coming winter. Therefore, a comprehensive global program with the leadership of WHO for testing of entire population of the world is required, which will be very useful for the individual states to take proper political action, social movement and medical services.

Title: Environments, Behaviors, and Inequalities: Reflecting on the Impacts of the Influenza and Coronavirus Pandemics in the United States Content: In the past century, dramatic shifts in demographics, globalization and urbanization have facilitated the rapid spread and transmission of infectious diseases across continents and countries. In a matter of weeks, the 2019 coronavirus pandemic devastated communities worldwide and reinforced the human perception of frailty and mortality. Even though the end of this pandemic story has yet to unfold, there is one parallel that is undeniable when a comparison is drawn between the 2019 coronavirus and the 1918 influenza pandemics. The public health response to disease outbreaks has remained nearly unchanged in the last 101 years. Furthermore, the role of environments and human behaviors on the effect and response to the coronavirus pandemic has brought to light many of the historic and contemporaneous inequalities and injustices that plague the United States. Through a reflection of these pandemic experiences, the American burden of disparity and disproportionality on morbidity, mortality and overall social determinants of health has been examined. Finally, a reimagination of a post-coronavirus existence has also been presented along with a discussion of possible solutions and considerations for moving forward to a new and better normal.

Title: Association of COVID-19 pandemic with meteorological parameters over Singapore Content: Abstract Meteorological parameters are the critical factors affecting the transmission of infectious diseases such as Middle East Respiratory Syndrome (MERS), Severe Acute Respiratory Syndrome (SARS), and influenza. Consequently, infectious disease incidence rates are likely to be influenced by the weather change. This study investigates the role of Singapore's hot tropical weather in COVID-19 transmission by exploring the association between meteorological parameters and the COVID-19 pandemic cases in Singapore. This study uses the secondary data of COVID-19 daily cases from the webpage of Ministry of Health (MOH), Singapore. Spearman and Kendall rank correlation tests were used to investigate the correlation between COVID-19 and meteorological parameters. Temperature, dew point, relative humidity, absolute humidity, and water vapor showed positive significant correlation with COVID-19 pandemic. These results will help the epidemiologists to understand the behavior of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) virus against meteorological variables. This study finding would be also a useful supplement to help the local healthcare policymakers, Center for Disease Control (CDC), and the World Health Organization (WHO) in the process of strategy making to combat COVID-19 in Singapore.

Title: The COVID-19 pandemic's impact on U.S. electricity demand and supply: an early view from the data Content: After the onset of the recent COVID-19 pandemic, a number of studies reported on possible changes in electricity consumption trends. The overall theme of these reports was that ``electricity use has decreased during the pandemic, but the power grid is still reliable''---mostly due to reduced economic activity. In this paper we analyze electricity data upto end of May 2020, examining both electricity demand and variables that can indicate stress on the power grid, such as peak demand and demand ramp-rate. We limit this study to three states in the USA: New York, California, and Florida. The results indicate that the effect of the pandemic on electricity demand is not a simple reduction from comparable time frames, and there are noticeable differences among regions. The variables that can indicate stress on the grid also conveyed mixed messages: some indicate an increase in stress, some indicate a decrease, and some do not indicate any clear difference. A positive message is that some of the changes that were observed around the time stay-at-home orders were issued appeared to revert back by May 2020. A key challenge in ascribing any observed change to the pandemic is correcting for weather. We provide a weather-correction method, apply it to a small city-wide area, and discuss the implications of the estimated changes in demand. The weather correction exercise underscored that weather-correction is as challenging as it is important.

Title: Weather variables impact on COVID-19 incidence Content: We test the hypothesis of COVID-19 contagion being influenced by meteorological parameters such as temperature or humidity. We analysed data at high spatial resolution (regions in Italy and counties in the USA) and found that while at low resolution this might seem the case, at higher resolution no correlation is found. Our results are consistent with a poor outdoors transmission of the disease. However, a possible indirect correlation between good weather and a decrease in disease spread may occur, as people spend longer time outdoors.

Title: Seasonality of infectious diseases and severe acute respiratory syndrome–what we don't know can hurt us Content: Summary The novel severe acute respiratory syndrome (SARS) coronavirus caused severe disease and heavy economic losses before apparently coming under complete control. Our understanding of the forces driving seasonal disappearance and recurrence of infectious diseases remains fragmentary, thus limiting any predictions about whether, or when, SARS will recur. It is true that most established respiratory pathogens of human beings recur in wintertime, but a new appreciation for the high burden of disease in tropical areas reinforces questions about explanations resting solely on cold air or low humidity. Seasonal variation in host physiology may also contribute. Newly emergent zoonotic diseases such as ebola or pandemic strains of influenza have recurred in unpredictable patterns. Most established coronaviruses exhibit winter seasonality, with a unique ability to establish persistent infections in a minority of infected animals. Because SARS coronavirus RNA can be detected in the stool of some individuals for at least 9 weeks, recurrence of SARS from persistently shedding human or animal reservoirs is biologically plausible.

Title: The dark cloud with a silver lining: Assessing the impact of the SARS COVID-19 pandemic on the global environment Content: The Severe Acute Respiratory Syndrome-Coronavirus Disease 2019 (COVID-19) pandemic caused by a novel coronavirus known as SARS-CoV-2 has caused tremendous suffering and huge economic losses. We hypothesized that extreme measures of partial-to-total shutdown might have influenced the quality of the global environment because of decreased emissions of atmospheric pollutants. We tested this hypothesis using satellite imagery, climatic datasets (temperature, and absolute humidity), and COVID-19 cases available in the public domain. While the majority of the cases were recorded from Western countries, where mortality rates were strongly positively correlated with age, the number of cases in tropical regions was relatively lower than European and North American regions, possibly attributed to faster human-to-human transmission. There was a substantial reduction in the level of nitrogen dioxide (NO2: 0.00002 mol m-2), a low reduction in CO (<0.03 mol m-2), and a low-to-moderate reduction in Aerosol Optical Depth (AOD: ~0.1-0.2) in the major hotspots of COVID-19 outbreak during February-March 2020, which may be attributed to the mass lockdowns. Our study projects an increasing coverage of high COVID-19 hazard at absolute humidity levels ranging from 4 to 9 g m-3 across a large part of the globe during April-July 2020 due to a high prospective meteorological suitability for COVID-19 spread. Our findings suggest that there is ample scope for restoring the global environment from the ill-effects of anthropogenic activities through temporary shutdown measures.

Title: 21-Day Lockdown in India Dramatically Reduced Air Pollution Indices in Lucknow and New Delhi, India Content: In December 2019, the outbreak of viral disease labeled as Novel Coronavirus started in Wuhan, China, which later came to be known as Covid-19. The disease has spread in almost every part of the world and has been declared a global pandemic in March 2020 by World Health Organization (WHO). The corona virus outbreak has emerged as one of the deadliest pandemics of all time in human history. The ongoing pandemic of COVID-19 has forced several countries of the world to observe complete lockdown forcing people to live in their homes. India also faced the phase of total lockdown for 21 days (in first phase) to avoid the spread of coronavirus to the maximum possible extent. This lockdown impacted the pollution levels of environment and improved air and water quality in the short span owing to very less human activities. The present work scientifically analyzed the available data for primary air pollutants (PM(2.5), NO(2), SO(2) and CO) from two major Indian cities, Lucknow and New Delhi. The analysis was based on air quality data for before lockdown and after lockdown (first phase of 21 days) periods of 21 days each. The results showed significant decline in the studied air pollution indices and demonstrated improvement of air quality in both the cities. The major impact was seen in the levels of PM(2.5), NO(2) and CO. The levels of SO(2) showed less significant decline during the lockdown period. The results are presented with future perspectives to mitigate air pollution in near future by adopting the short and periodical lockdown as a tool. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00128-020-02895-w) contains supplementary material, which is available to authorized users.

Title: Optimal temperature zone for the dispersal of COVID-19 Content: It is essential to know the environmental parameters within which the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can survive to understand its global dispersal pattern. We found that 60.0% of the confirmed cases of coronavirus disease 2019 (COVID-19) occurred in places where the air temperature ranged from 5 °C to 15 °C, with a peak in cases at 11.54 °C. Moreover, approximately 73.8% of the confirmed cases were concentrated in regions with absolute humidity of 3 g/m3 to 10 g/m3. SARS-CoV-2 appears to be spreading toward higher latitudes. Our findings suggest that there is an optimal climatic zone in which the concentration of SARS-CoV-2 markedly increases in the ambient environment (including the surfaces of objects). These results strongly imply that the COVID-19 pandemic may spread cyclically and outbreaks may recur in large cities in the mid-latitudes in autumn 2020.

Title: Effects of the COVID-19 Pandemic on Population Mobility under Mild Policies: Causal Evidence from Sweden Content: Sweden has adopted far less restrictive social distancing policies than most countries following the COVID-19 pandemic. This paper uses data on all mobile phone users, from one major Swedish mobile phone network, to examine the impact of the Coronavirus outbreak under the Swedish mild recommendations and restrictions regime on individual mobility and if changes in geographical mobility vary over different socio-economic strata. Having access to data for January-March in both 2019 and 2020 enables the estimation of causal effects of the COVID-19 outbreak by adopting a Difference-in-Differences research design. The paper reaches four main conclusions: (i) The daytime population in residential areas increased significantly (64 percent average increase); (ii) The daytime presence in industrial and commercial areas decreased significantly (33 percent average decrease); (iii) The distance individuals move from their homes during a day was substantially reduced (38 percent decrease in the maximum distance moved and 36 percent increase in share of individuals who move less than one kilometer from home); (iv) Similar reductions in mobility were found for residents in areas with different socioeconomic and demographic characteristics. These results show that mild government policies can compel people to adopt social distancing behavior.

Title: Anomalous atmospheric circulation favored the spread of COVID-19 in Europe Content: The current pandemic caused by the coronavirus SARS-CoV-2 is having negative health, social and economic consequences worldwide. In Europe, the pandemic started to develop strongly at the end of February and beginning of March 2020. It has subsequently spread over the continent, with special virulence in northern Italy and inland Spain. In this study we show that an unusual persistent anticyclonic situation prevailing in southwestern Europe during February 2020 (i.e. anomalously strong positive phase of the North Atlantic and Arctic Oscillations) could have resulted in favorable conditions, in terms of air temperature and humidity, in Italy and Spain for a quicker spread of the virus compared with the rest of the European countries. It seems plausible that the strong atmospheric stability and associated dry conditions that dominated in these regions may have favored the virus's propagation, by short-range droplet transmission as well as likely by long-range aerosol (airborne) transmission.

Title: Meteorological factors and domestic new cases of coronavirus disease (COVID-19) in nine Asian cities: A time-series analysis Content: AIM To investigate the associations of meteorological factors and the daily new cases of coronavirus disease (COVID-19) in nine Asian cities. METHOD Pearson correlation and generalized additive modeling were performed to assess the relationships between daily new COVID-19 cases and meteorological factors (daily average temperature and relative humidity) with the most updated data currently available. RESULTS The Pearson correlation showed that daily new confirmed cases of COVID-19 were more correlated with the average temperature than with relative humidity. Daily new confirmed cases were negatively correlated with the average temperature in Beijing (r=-0.565, P<0.01), Shanghai (r=-0.471, P<0.01), and Guangzhou (r=-0.530, P<0.01) , yet in contrast, positively correlated with that in Japan (r=0.441, P<0.01). In most of the cities (Shanghai, Guangzhou, Hong Kong, Seoul, Tokyo, and Kuala Lumpur), generalized additive modeling analysis showed the number of daily new confirmed cases was positively associated with both average temperature and relative humidity, especially in lagged 3d model, where a positive influence of temperature on the daily new confirmed cases was discerned in 5 cities except in Beijing, Wuhan, Korea, and Malaysia. Nevertheless, the results were inconsistent across cities and lagged time, suggesting meteorological factors were unlikely to greatly influence the COVID-19 epidemic. CONCLUSION The associations between meteorological factors and the number of COVID-19 daily cases are inconsistent across cities and lagged time. Large-scale public health measures and expanded regional research are still required until a vaccine becomes available and herd immunity is established.

Title: ICU admissions and in-hospital deaths linked to covid-19 in the Paris region are correlated with previously observed ambient temperature Content: OBJECTIVE To study the effect of weather on severity indicators of coronavirus disease 2019 (covid-19). DESIGN Ecological study. SETTING Paris region. POPULATION Severely ill patients with covid-19. MAIN OUTCOME MEASURES Daily covid-19-related intensive care unit (ICU) admission and in-hospital deaths in the Paris region, and the daily weather characteristics of Paris midtown. RESULTS Daily ICU admissions and in-hospital deaths were strongly and negatively correlated to ambient temperatures, with a time lag. The highest Pearson correlation coefficients and statistically significant P values were found 8 days before occurrence of ICU admissions and 15 days before deaths. CONCLUSIONS The study findings show a strong effect of previously observed ambient temperature that has an effect on severity indicators of covid-19.

Title: Adapting to the Coronavirus Disease 2019 Pandemic in New York City Content: The coronavirus disease 2019 has and continues to overwhelm the medical establishment in New York City. It has moved faster and had rates of mortality higher than what were initially forecast. All departments within large hospital systems have had to learn lessons and adapt in real time to the crisis. We share our experience and what we have learned as it pertains to this pandemic and hope that it aides, guides, and influences other departments of physical medicine and rehabilitation regarding their potential roles and areas of growth during this time.

Title: Observed and Potential Impacts of the COVID-19 Pandemic on the Environment Content: Various environmental factors influence the outbreak and spread of epidemic or even pandemic events which, in turn, may cause feedbacks on the environment. The novel coronavirus disease (COVID-19) was declared a pandemic on 13 March 2020 and its rapid onset, spatial extent and complex consequences make it a once-in-a-century global disaster. Most countries responded by social distancing measures and severely diminished economic and other activities. Consequently, by the end of April 2020, the COVID-19 pandemic has led to numerous environmental impacts, both positive such as enhanced air and water quality in urban areas, and negative, such as shoreline pollution due to the disposal of sanitary consumables. This study presents an early overview of the observed and potential impacts of the COVID-19 on the environment. We argue that the effects of COVID-19 are determined mainly by anthropogenic factors which are becoming obvious as human activity diminishes across the planet, and the impacts on cities and public health will be continued in the coming years.

Title: Mental Health and Behavior of College Students During the Early Phases of the COVID-19 Pandemic: Longitudinal Smartphone and Ecological Momentary Assessment Study Content: BACKGROUND: The vast majority of people worldwide have been impacted by coronavirus disease (COVID-19). In addition to the millions of individuals who have been infected with the disease, billions of individuals have been asked or required by local and national governments to change their behavioral patterns. Previous research on epidemics or traumatic events suggests that this can lead to profound behavioral and mental health changes; however, researchers are rarely able to track these changes with frequent, near-real-time sampling or compare their findings to previous years of data for the same individuals. OBJECTIVE: By combining mobile phone sensing and self-reported mental health data among college students who have been participating in a longitudinal study for the past 2 years, we sought to answer two overarching questions. First, have the behaviors and mental health of the participants changed in response to the COVID-19 pandemic compared to previous time periods? Second, are these behavior and mental health changes associated with the relative news coverage of COVID-19 in the US media? METHODS: Behaviors such as the number of locations visited, distance traveled, duration of phone usage, number of phone unlocks, sleep duration, and sedentary time were measured using the StudentLife smartphone sensing app. Depression and anxiety were assessed using weekly self-reported ecological momentary assessments of the Patient Health Questionnaire-4. The participants were 217 undergraduate students, with 178 (82.0%) students providing data during the Winter 2020 term. Differences in behaviors and self-reported mental health collected during the Winter 2020 term compared to previous terms in the same cohort were modeled using mixed linear models. RESULTS: During the first academic term impacted by COVID-19 (Winter 2020), individuals were more sedentary and reported increased anxiety and depression symptoms (P<.001) relative to previous academic terms and subsequent academic breaks. Interactions between the Winter 2020 term and the week of the academic term (linear and quadratic) were significant. In a mixed linear model, phone usage, number of locations visited, and week of the term were strongly associated with increased amount of COVID-19-related news. When mental health metrics (eg, depression and anxiety) were added to the previous measures (week of term, number of locations visited, and phone usage), both anxiety (P<.001) and depression (P=.03) were significantly associated with COVID-19-related news. CONCLUSIONS: Compared with prior academic terms, individuals in the Winter 2020 term were more sedentary, anxious, and depressed. A wide variety of behaviors, including increased phone usage, decreased physical activity, and fewer locations visited, were associated with fluctuations in COVID-19 news reporting. While this large-scale shift in mental health and behavior is unsurprising, its characterization is particularly important to help guide the development of methods to reduce the impact of future catastrophic events on the mental health of the population.

Title: High Temperature and High Humidity Reduce the Transmission of COVID-19 Content: With the ongoing global pandemic of COVID-19, a question is whether the coming summer in the northern hemisphere will reduce the transmission intensity of COVID-19 with increased humidity and temperature. In this paper, we investigate this problem using the data from the cases with symptom-onset dates from January 19 to February 10, 2020 for 100 Chinese cities, and cases with confirmed dates from March 15 to April 25 for 1,005 U.S. counties. Statistical analysis is performed to assess the relationship between the transmissibility of COVID-19 and the temperature/humidity, by controlling for various demographic, socio-economic, geographic, healthcare and policy factors and correcting for cross-sectional correlation. We find a similar influence of the temperature and relative humidity on effective reproductive number (R values) of COVID-19 for both China and the U.S. before lockdown in both countries: one-degree Celsius increase in temperature reduces R value by about 0.023 (0.026 (95% CI [-0.0395,-0.0125]) in China and 0.020 (95% CI [-0.0311, -0.0096]) in the U.S.), and one percent relative humidity rise reduces R value by 0.0078 (0.0076 (95% CI [-0.0108,-0.0045]) in China and 0.0080 (95% CI [-0.0150,-0.0010]) in the U.S.). If assuming a 30 degree and 25 percent increase in temperature and relative humidity from winter to summer in the northern hemisphere, we expect the R values to decline about 0.89 (0.69 by temperature and 0.20 by humidity). Given the notion that the non-intervened R values are around 2.5 to 3, only weather factors cannot make the R values below their critical condition of R<1, under which the epidemic diminishes gradually. Therefore, public health intervention such as social distancing is crucial to block the transmission of COVID-19 even in summer.

Title: Letter to the Editor: Note on published research on the effects of COVID-19 on the environment without sufficient depth of science Content: Dear Editor-in-Chief: We have given two articles published recently in Science of the Total Environment by Mandal and Pal (2020) and Zambrano-Monserrate et al. (2020) a thorough reading. Both articles present a significant association between the novel Coronavirus (COVID-19) social distancing policies and improvement in environmental quality such as air pollution, land surface temperature, and noise. Both articles present good research, complemented by detailed explanations and displays, yet we have a few concerns that affect the interpretation and meaning of the results.

Title: The role of climate during the COVID-19 epidemic in New South Wales, Australia Content: Previous research has identified a relationship between climate and occurrence of SARS-CoV and MERS-CoV cases, information that can be used to reduce the risk of infection. Using COVID-19 notification and postcode data from New South Wales, Australia during the exponential phase of the epidemic in 2020, we used time series analysis to investigate the relationship between 749 cases of locally acquired COVID-19 and daily rainfall, 9 a.m. and 3 p.m. temperature, and 9 a.m. and 3 p.m. relative humidity. Lower 9 a.m. relative humidity (but not rainfall or temperature) was associated with increased case occurrence; a reduction in relative humidity of 1% was predicted to be associated with an increase of COVID-19 cases by 6.11%. During periods of low relative humidity, the public health system should anticipate an increased number of COVID-19 cases.

Title: COVID-19 transmission in Mainland China is associated with temperature and humidity: a time-series analysis Content: COVID-19 has become a pandemic. The influence of meteorological factors on the transmission and spread of COVID-19 if of interest. This study sought to examine the associations of daily average temperature (AT) and relative humidity (ARH) with the daily count of COVID-19 cases in 30 Chinese provinces (in Hubei from December 1, 2019 to February 11, 2020 and in other provinces from January 20, 2020 to Februarys 11, 2020). A Generalized Additive Model (GAM) was fitted to quantify the province-specific associations between meteorological variables and the daily cases of COVID-19 during the study periods. In the model, the 14-day exponential moving averages (EMAs) of AT and ARH, and their interaction were included with time trend and health-seeking behavior adjusted. Their spatial distributions were visualized. AT and ARH showed significantly negative associations with COVID-19 with a significant interaction between them (0.04, 95% confidence interval: 0.004-0.07) in Hubei. Every 1°C increase in the AT led to a decrease in the daily confirmed cases by 36% to 57% when ARH was in the range from 67% to 85.5%. Every 1% increase in ARH led to a decrease in the daily confirmed cases by 11% to 22% when AT was in the range from 5.04°C to 8.2°C. However, these associations were not consistent throughout Mainland China.

Title: Effects of temperature variation and humidity on the mortality of COVID-19 in Wuhan Content: Object Meteorological parameters are the important factors influencing the infectious diseases like severe acute respiratory syndrome (SARS). This study aims to explore the association between coronavirus disease (COVID-19) death and weather parameters. Methods In this study, we collected the daily death number of COVID-19, meteorological and air pollutant data from 20 January, 2020 to 29 February, 2020 in Wuhan, China. Then, the generalized additive model was applied to explore the impact of temperature, humidity and diurnal temperature range on daily mortality of COVID-19. Results There were in total 2299 COVID-19 mortality counts in Wuhan. A positive association with COVID-19 mortality was observed for diurnal temperature range (r = 0.44), but negative association for relative humidity (r = -0.32). In addition, each 1 unit increase in diurnal temperature range was only associated with a 2.92% (95% CI: 0.61%, 5.28%) increase in COVID-19 mortality at lag 3. However, both per 1 unit increase of temperature and absolute humidity were related to the decreased COVID-19 mortality at lag 3 and lag 5, respectively. Conclusion In summary, this study suggests the temperature variation and humidity may be important factors affecting the COVID-19 mortality.