Question: has social distancing had an impact on slowing the spread of COVID-19?

Title: U.S. county level analysis to determine If social distancing slowed the spread of COVID-19 Content: OBJECTIVE. To analyze the effectiveness of social distancing in the United States (U.S.). METHODS. A novel cell-phone ping data was used to quantify the measures of social distancing by all U.S. counties. RESULTS. Using a difference-in-difference approach results show that social distancing has been effective in slowing the spread of COVID-19. CONCLUSIONS. As policymakers face the very difficult question of the necessity and effectiveness of social distancing across the U.S., counties where the policies have been imposed have effectively increased social distancing and have seen slowing the spread of COVID-19. These results might help policymakers to make the public understand the risks and benefits of the lockdown.

Title: On the benefits of flattening the curve: A perspective() Content: The many variations on a graphic illustrating the impact of non-pharmaceutical measures to mitigate pandemic influenza that have appeared in recent news reports about COVID-19 suggest a need to better explain the mechanism by which social distancing reduces the spread of infectious diseases. And some reports understate one benefit of reducing the frequency or proximity of interpersonal encounters, a reduction in the total number of infections. In hopes that understanding will increase compliance, we describe how social distancing a) reduces the peak incidence of infections, b) delays the occurrence of this peak, and c) reduces the total number of infections during epidemics. In view of the extraordinary efforts underway to identify existing medications that are active against SARS-CoV-2 and to develop new antiviral drugs, vaccines and antibody therapies, any of which may have community-level effects, we also describe how pharmaceutical interventions affect transmission.

Title: Dynamics of COVID-19 under social distancing measures are driven by transmission network structure Content: In the absence of pharmaceutical interventions, social distancing is being used worldwide to curb the spread of COVID-19. The impact of these measures has been inconsistent, with some regions rapidly nearing disease elimination and others seeing delayed peaks or nearly flat epidemic curves. Here we build a stochastic epidemic model to examine the effects of COVID-19 clinical progression and transmission network structure on the outcomes of social distancing interventions. We find that the strength of within-household transmission is a critical determinant of success, governing the timing and size of the epidemic peak, the rate of decline, individual risks of infection, and the success of partial relaxation measures. The structure of residual external connections, driven by workforce participation and essential businesses, interacts to determine outcomes. These findings can improve future predictions of the timescale and efficacy of interventions needed to control similar outbreaks, and highlight the need for better quantification and control of household transmission.

Title: On the benefits of flattening the curve: A perspective Content: The many variations on a graphic illustrating the impact of non-pharmaceutical measures to mitigate pandemic influenza that have appeared in recent news reports about COVID-19 suggest a need to better explain the mechanism by which social distancing reduces the spread of infectious diseases. And some reports understate one benefit of reducing the frequency or proximity of interpersonal encounters, a reduction in the total number of infections. In hopes that understanding will increase compliance, we describe how social distancing (a) reduces the peak incidence of infections, (b) delays the occurrence of this peak, and (c) reduces the total number of infections during epidemics. In view of the extraordinary efforts underway to identify existing medications that are active against SARS-CoV-2 and to develop new antiviral drugs, vaccines and antibody therapies, any of which may have community-level effects, we also describe how pharmaceutical interventions affect transmission.

Title: COVID-19 related social distancing measures and reduction in city mobility Content: In the absence of any pharmacological intervention, one approach to slowing the COVID-19 pandemic is reducing the contact rate in the population through social distancing. Governments the world over have instituted different measures to increase social distancing but information on their effectiveness in reducing mobility is lacking. We analyzed the mobility data from 41 cities to look at the effect of these interventions. The median mobility across cities on March 2, 2020 was 100% (IQR: 94%, 107%), which decreased to a median of 10% (IQR: 7%, 17%) on March 26, 2020. We found that the mobility decreased on average by 3.4% (95%CI: 3.3%, 3.6%) per day from March 2 through March 26. Social distancing measures decreased the mobility by an additional 23% (95%CI: 20%, 27%). Our study provides initial evidence for the reduction in mobility in cities instituting social distancing measures.

Title: Social Distancing Has Merely Stabilized COVID-19 in the US Content: Social distancing measures, with varying degrees of restriction, have been imposed around the world in order to stem the spread of COVID-19. In this work we analyze the effect of current social distancing measures in the United States. We quantify the reduction in doubling rate, by state, that is associated with social distancing. We find that social distancing is associated with a statistically-significant reduction in the doubling rate for all but three states. At the same time, we do not find significant evidence that social distancing has resulted in a reduction in the number of daily confirmed cases. Instead, social distancing has merely stabilized the spread of the disease. We provide an illustration of our findings for each state, including point estimates of the effective reproduction number, R, both with and without social distancing. We also discuss the policy implications of our findings.

Title: Impact of non-pharmaceutical interventions against COVID-19 in Europe: a quasi-experimental study Content: The current epidemic of COVID-19 is unparalleled in recent history as are the social distancing interventions that have led to a significant halt on the economic and social life of so many countries. However, there is very little empirical evidence about which social distancing measures have the most impact. We report a quasi-experimental study of the impact of various interventions for control of the outbreak. Data on case numbers and deaths were taken from the daily published figures by the European Centre for Disease Control and dates of initiation of various control strategies from the Institute of Health Metrics and Evaluation website and published sources. Our primary analyses were modelled in R using Bayesian generalised additive mixed models (GAMM). We found that closure of education facilities, prohibiting mass gatherings and closure of some non-essential businesses were associated with reduced incidence whereas stay at home orders, closure of all non-businesses and requiring the wearing of facemasks or coverings in public was not associated with any independent additional impact. Our results could help inform strategies for coming out of lockdown.

Title: Fever and mobility data indicate social distancing has reduced incidence of communicable disease in the United States Content: In March of 2020, many U.S. state governments encouraged or mandated restrictions on social interactions to slow the spread of COVID-19, the disease caused by the novel coronavirus SARS-CoV-2 that has spread to nearly 180 countries. Estimating the effectiveness of these social-distancing strategies is challenging because surveillance of COVID-19 has been limited, with tests generally being prioritized for high-risk or hospitalized cases according to temporally and regionally varying criteria. Here we show that reductions in mobility across U.S. counties with at least 100 confirmed cases of COVID-19 led to reductions in fever incidences, as captured by smart thermometers, after a mean lag of 6.5 days ($90\%$ within 3--10 days) that is consistent with the incubation period of COVID-19. Furthermore, counties with larger decreases in mobility subsequently achieved greater reductions in fevers ($p<0.01$), with the notable exception of New York City and its immediate vicinity. These results indicate that social distancing has reduced the transmission of influenza like illnesses, including COVID 19, and support social distancing as an effective strategy for slowing the spread of COVID-19.

Title: Targeted adaptive isolation strategy for Covid-19 pandemic Content: We investigate the effects of social distancing in controlling the impact of the COVID-19 epidemic using a simple susceptible-infected-removed epidemic model. We show that an alternative or complementary approach based on targeted isolation of the vulnerable sub-population may provide a more efficient and robust strategy at a lower economic and social cost within a shorter timeframe resulting in a collectively immune population.

Title: Targeted adaptive isolation strategy for COVID-19 pandemic Content: We investigate the effects of social distancing in controlling the impact of the COVID-19 epidemic using a simple susceptible-infected-removed epidemic model. We show that an alternative or complementary approach based on targeted isolation of the vulnerable sub-population may provide a more efficient and robust strategy at a lower economic and social cost within a shorter timeframe resulting in a collectively immune population.

Title: Enacting national social distancing policies corresponds with dramatic reduction in COVID19 infection rates Content: The outbreak the SARS-CoV-2 (CoV-2) virus has resulted in over 2.5 million cases of COVID19, greatly stressing global healthcare infrastructure. Lacking medical prophylactic measures to combat disease spread, many nations have adopted social distancing policies in order to mitigate transmission of CoV-2. While mathematical models have suggested the efficacy of social distancing to curb the spread of CoV-2, there is a lack of systematic studies to quantify the real-world efficacy of these approaches. Here, we quantify the spread rate of COVID19 before and after national social distancing measures were implemented in 26 nations and compare this to the changes in COVID19 spread rate over equivalent time periods in 27 nations that did not enact social distancing policies. We find that social distancing policies significantly reduced the COVID19 spread rate. Using mixed linear regression models we estimate that social distancing policies reduced the spread of COVID19 by 66%. These data suggest that social distancing policies may be a powerful tool to prevent spread of COVID19 in real-world scenarios.

Title: Effects of Government Mandated Social Distancing Measures on Cumulative Incidence of COVID-19 in the United States and its Most Populated Cities Content: COVID-19, caused by the SARS-CoV-2 virus, has quickly spread throughout the world, necessitating assessment of the most effective containment methods. Very little research exists on the effects of social distancing measures on this pandemic. The purpose of this study was to examine the effects of government implemented social distancing measures on the cumulative incidence rates of COVID-19 in the United States on a state level, and in the 25 most populated cities, while adjusting for socio-demographic risk factors. The social distancing variables assessed in this study were: days to closing of non-essential business; days to stay home orders; days to restrictions on gathering, days to restaurant closings and days to school closing. Using negative binomial regression, adjusted rate ratios and 95% confidence intervals were calculated comparing two levels of a binary variable: above median value, and median value and below for days to implementing a social distancing measure. For city level data, the effects of these social distancing variables were also assessed in high (above median value) vs low (median value and below) population density cities. For the state level analysis, days to school closing was associated with cumulative incidence, with an adjusted rate ratio of 1.59 (95% CI:1.03,2.44), p=0.04 at 35 days. Some results were counterintuitive, including inverse associations between cumulative incidence and days to closure of non-essential business and restrictions on gatherings. This finding is likely due to reverse causality, where locations with slower growth rates initially chose not to implement measures, and later implemented measures when they absolutely needed to respond to increasing rates of infection. Effects of social distancing measures seemed to vary by population density in cities. Our results suggest that the effect of social distancing measures may differ between states and cities and between locations with different population densities. States and cities need individual approaches to containment of an epidemic, with an awareness of their own structure in terms of crowding and socio-economic variables. In an effort to reduce infection rates, cities may want to implement social distancing in advance of state mandates.

Title: Adoption and impact of non-pharmaceutical interventions for COVID-19 Content: Background: Several non-pharmaceutical interventions (NPIs) have been implemented across the world to control the coronavirus disease (COVID-19) pandemic. Social distancing (SD) interventions applied so far have included school closures, remote working and quarantine. These measures have been shown to have large impacts on pandemic influenza transmission. However, there has been comparatively little examination of such measures for COVID-19. Methods: We examined the existing literature, and collated data, on implementation of NPIs to examine their effects on the COVID-19 pandemic so far. Data on NPIs were collected from official government websites as well as from media sources. Results: Measures such as travel restrictions have been implemented in multiple countries and appears to have slowed the geographic spread of COVID-19 and reduced initial case numbers. We find that, due to the relatively sparse information on the differences with and without interventions, it is difficult to quantitatively assess the efficacy of many interventions. Similarly, whilst the comparison to other pandemic diseases such as influenza can be helpful, there are key differences that could affect the efficacy of similar NPIs. Conclusions: The timely implementation of control measures is key to their success and must strike a balance between early enough application to reduce the peak of the epidemic and ensuring that they can be feasibly maintained for an appropriate duration. Such measures can have large societal impacts and they need to be appropriately justified to the population. As the pandemic of COVID-19 progresses, quantifying the impact of interventions will be a vital consideration for the appropriate use of mitigation strategies.

Title: No Place Like Home: Cross-National Data Analysis of the Efficacy of Social Distancing During the COVID-19 Pandemic Content: BACKGROUND: In the absence of a cure in the time of a pandemic, social distancing measures seem to be the most effective intervention to slow the spread of disease. Various simulation-based studies have been conducted to investigate the effectiveness of these measures. While those studies unanimously confirm the mitigating effect of social distancing on disease spread, the reported effectiveness varies from 10% to more than 90% reduction in the number of infections. This level of uncertainty is mostly due to the complex dynamics of epidemics and their time-variant parameters. However, real transactional data can reduce uncertainty and provide a less noisy picture of the effectiveness of social distancing. OBJECTIVE: The aim of this paper was to integrate multiple transactional data sets (GPS mobility data from Google and Apple as well as disease statistics from the European Centre for Disease Prevention and Control) to study the role of social distancing policies in 26 countries and analyze the transmission rate of the coronavirus disease (COVID-19) pandemic over the course of 5 weeks. METHODS: Relying on the susceptible-infected-recovered (SIR) model and official COVID-19 reports, we first calculated the weekly transmission rate (ß) of COVID-19 in 26 countries for 5 consecutive weeks. Then, we integrated these data with the Google and Apple mobility data sets for the same time frame and used a machine learning approach to investigate the relationship between the mobility factors and ß values. RESULTS: Gradient boosted trees regression analysis showed that changes in mobility patterns resulting from social distancing policies explain approximately 47% of the variation in the disease transmission rates. CONCLUSIONS: Consistent with simulation-based studies, real cross-national transactional data confirms the effectiveness of social distancing interventions in slowing the spread of COVID-19. In addition to providing less noisy and more generalizable support for the idea of social distancing, we provide specific insights for public health policy makers regarding locations that should be given higher priority for enforcing social distancing measures.

Title: Diminishing Marginal Benefit of Social Distancing in Balancing COVID-19 Medical Demand-to-Supply Content: Social distancing has been adopted as a non-pharmaceutical intervention to prevent the COVID-19 pandemic from overwhelming the medical resources across the United States (US). The catastrophic socio-economic impacts of this intervention could outweigh its benefits if the timing and duration of implementation are left uncontrolled and ill-strategized. Here we investigate the dynamics of social distancing on age-stratified US population and benchmark its effectiveness in reducing the burden on hospital and ICU beds. Our findings highlight the diminishing marginal benefit of social distancing, characterized by a linear decrease in medical demands against an exponentially increasing social distancing duration. We determine an optimal intermittent social-to-no-distancing ratio of 5:1 corresponding to ~80% reduction in healthcare demands; beyond this ratio, benefit of social distancing diminishes to a negligible level. COVID-19 Medical Forecast: https://eece.wustl.edu/chakrabarty-group/covid/

Title: Augmenting contact matrices with time-use data for fine-grained intervention modelling of disease dynamics: A modelling analysis Content: Background: Social distancing is an important public health intervention to reduce or interrupt the sustained community transmission of emerging infectious pathogens, such as SARS-CoV-2 during the coronavirus disease 2019 (COVID-19) pandemic. We aimed to explore the impact on the epidemic curve of fewer contacts when individuals reduce the time they spend on selected daily activities. Methods: We combined the large-scale empirical data of a social contact survey and a time-use survey to estimate contact matrices by age group (0-15, 16-24, 25-44, 45-64, 65+) and daily activity (work, schooling, transportation, and four leisure activities: social visits, bar/cafe/restaurant visits, park visits, and non-essential shopping). We assumed that reductions in time are proportional to reductions in contacts. The derived matrices were then applied in an age-structured dynamic-transmission model of COVID-19 to explore the effects. Findings: The relative reductions in the derived contact matrices were highest when closing schools (in ages 0-14 years), workplaces (15-64 years), and stopping social visits (65+ years). For COVID-19, the closure of workplaces, schools, and stopping social visits had the largest impact on reducing the epidemic curve and delaying its peak, while the predicted impact of fewer contacts in parks, bars/cafes/restaurants, and non-essential shopping were minimal. Interpretation: We successfully augmented contact matrices with time-use data to predict the highest impact of social distancing measures from reduced contacts when spending less time at work, school, and on social visits. Although the predicted impact from other leisure activities with potential for close physical contact were minimal, changes in mixing patterns and time-use immediately after re-allowing social activities may pose increased short-term transmission risks, especially in potentially crowded environments indoors.

Title: Potential roles of social distancing in mitigating the spread of coronavirus disease 2019 (COVID-19) in South Korea Content: On January 20, 2020, the first COVID-19 case was confirmed in South Korea. After a rapid outbreak, the number of incident cases has been consistently decreasing since early March; this decrease has been widely attributed to its intensive testing. We report here on the likely role of social distancing in reducing transmission in South Korea. Our analysis suggests that transmission may still be persisting in some regions.

Title: The differential impact of physical distancing strategies on social contacts relevant for the spread of COVID-19 Content: Physical distancing measures are intended to mitigate the spread of COVID-19. However, the impact these measures have on social contact and disease transmission patterns remains unclear. We ran the first comparative contact survey (N=53,708) across eight countries (Belgium, France, Germany, Italy, Netherlands, Spain, United Kingdom, United States) for the period March 13 - April 13, 2020. Our results show that social contact numbers mainly decreased after governments issued physical distancing guidelines rather than after announcing national lockdown measures. Compared to pre-COVID levels, social contact numbers decreased by 48% - 85% across countries. Except in Italy, these reductions were smaller than those observed in Wuhan (China). However, they sufficed to bring the R0 below one in almost every context considered. Finally, in all countries studied, the numbers of contacts decreased more rapidly among older people than among younger people, indicating higher levels of protection for groups at greater risk.

Title: No Place Like Home: A Cross-National Assessment of the Efficacy of Social Distancing during the COVID-19 Pandemic. Content: BACKGROUND In the absence of a cure in the time of pandemics, social distancing measures seem to be the most effective intervention to slow down the spread of disease. Various simulation-based studies have been conducted in the past to investigate the effectiveness of such measures. While those studies unanimously confirm the mitigating effect of social distancing on the disease spread, the reported effectiveness varies from 10% to more than 90% reduction in the number of infections. This level of uncertainty is mostly due to the complex dynamics of epidemics and their time-variant parameters. A real transactional data, however, can reduce the uncertainty and provide a less noisy picture of social distancing effectiveness. OBJECTIVE In this paper, we integrate multiple transactional data sets (GPS mobility data from Google and Apple as well as disease statistics data from ECDC) to study the role of social distancing policies in 26 countries wherein the transmission rate of the COVID-19 pandemic is analyzed over the course of five weeks. METHODS Relying on the SIR model and official COVID-19 reports, we first calculated the weekly transmission rate (β) of the coronavirus disease in 26 countries for five consecutive weeks. Then we integrated that with the Google's and Apple's mobility data sets for the same time frame and used a machine learning approach to investigate the relationship between mobility factors and β values. RESULTS Gradient Boosted Trees (GBT) regression analysis showed that changes in mobility patterns, resulted from social distancing policies, explain around 47% of the variation in the disease transmission rate. CONCLUSIONS Consistent with simulation-based studies, real cross-national transactional data confirms the effectiveness of social distancing interventions in slowing down the spread of the disease. Apart from providing less noisy and more generalizable support for the whole social distancing idea, we provide specific insights for public health policy-makers as to what locations should be given a higher priority for enforcing social distancing measures. CLINICALTRIAL

Title: Impacts of State-Level Policies on Social Distancing in the United States Using Aggregated Mobility Data during the COVID-19 Pandemic Content: Social distancing has emerged as the primary mitigation strategy to combat the COVID-19 pandemic in the United States. However, large-scale evaluation of the public's response to social distancing campaigns has been lacking. We used anonymized and aggregated mobility data from Google Location History users to estimate the impact of social distancing recommendations on bulk mobility among users who have opted into this service. We found that state-of-emergency declarations resulted in approximately a 10% reduction in time spent away from places of residence. Implementation of one or more social distancing policies resulted in an additional 25% reduction in mobility the following week. Subsequent shelter-in-place mandates provided an additional 29% reduction. Our findings provide evidence that state-wide mandates are effective in promoting social distancing within this study group.

Title: The Benefits and Costs of Using Social Distancing to Flatten the Curve for COVID-19 Content: We examine the net benefits of social distancing to slow the spread of COVID-19 in USA. Social distancing saves lives but imposes large costs on society due to reduced economic activity. We use epidemiological and economic forecasting to perform a rapid benefit–cost analysis of controlling the COVID-19 outbreak. Assuming that social distancing measures can substantially reduce contacts among individuals, we find net benefits of about $5.2 trillion in our benchmark case. We examine the magnitude of the critical parameters that might imply negative net benefits, including the value of statistical life and the discount rate. A key unknown factor is the speed of economic recovery with and without social distancing measures in place. A series of robustness checks also highlight the key role of the value of mortality risk reductions and discounting in the analysis and point to a need for effective economic stimulus when the outbreak has passed.

Title: Modelling the potential impact of social distancing on the COVID-19 epidemic in South Africa Content: The novel coronavirus (COVID-19) pandemic continues to be a global health problem whose impact has been significantly felt in South Africa. Social distancing has been touted as the best form of response in managing a rapid increase in the number of infected cases. In this paper, we present a deterministic model to model the impact of social distancing on the transmission dynamics of COVID-19 in South Africa. The model is fitted to the currently available data on the cumulative number of infected cases and a scenario analysis on different levels of social distancing are presented. The results show a continued rise in the number of cases in the lock down period with the current levels of social distancing albeit at a lower rate. The model shows that the number of cases will rise to above 4000 cases by the end of the lockdown. The model also looks at the impact of relaxing the social distancing measures after the initial announcement of the lock down measures. A relaxation of the social distancing by 2% can result in a 23% rise in the number of cumulative cases while on the other hand increasing the levels of social distancing by 2% would reduce the number of cumulative cases by about 18%. These results have implications on the management and policy direction in the early phases of the epidemic.

Title: Social network-based distancing strategies to flatten the COVID-19 curve in a post-lockdown world Content: Social distancing and isolation have been widely introduced to counter the COVID-19 pandemic. Adverse social, psychological and economic consequences of a complete or near-complete lockdown demand the development of more moderate contact-reduction policies. Adopting a social network approach, we evaluate the effectiveness of three distancing strategies designed to keep the curve flat and aid compliance in a post-lockdown world. These are: limiting interaction to a few repeated contacts akin to forming social bubbles; seeking similarity across contacts; and strengthening communities via triadic strategies. We simulate stochastic infection curves incorporating core elements from infection models, ideal-type social network models and statistical relational event models. We demonstrate that a strategic social network-based reduction of contact strongly enhances the effectiveness of social distancing measures while keeping risks lower. We provide scientific evidence for effective social distancing that can be applied in public health messaging and that can mitigate negative consequences of social isolation.

Title: Social network-based distancing strategies to flatten the COVID-19 curve in a post-lockdown world. Content: Social distancing and isolation have been widely introduced to counter the COVID-19 pandemic. Adverse social, psychological and economic consequences of a complete or near-complete lockdown demand the development of more moderate contact-reduction policies. Adopting a social network approach, we evaluate the effectiveness of three distancing strategies designed to keep the curve flat and aid compliance in a post-lockdown world. These are: limiting interaction to a few repeated contacts akin to forming social bubbles; seeking similarity across contacts; and strengthening communities via triadic strategies. We simulate stochastic infection curves incorporating core elements from infection models, ideal-type social network models and statistical relational event models. We demonstrate that a strategic social network-based reduction of contact strongly enhances the effectiveness of social distancing measures while keeping risks lower. We provide scientific evidence for effective social distancing that can be applied in public health messaging and that can mitigate negative consequences of social isolation.

Title: Mobile phone location data reveal the effect and geographic variation of social distancing on the spread of the COVID-19 epidemic Content: The emergence of SARS-CoV-2 and the coronavirus infectious disease (COVID-19) has become a pandemic. Social (physical) distancing is a key non-pharmacologic control measure to reduce the transmission rate of SARS-COV-2, but high-level adherence is needed. Using daily travel distance and stay-at-home time derived from large-scale anonymous mobile phone location data provided by Descartes Labs and SafeGraph, we quantify the degree to which social distancing mandates have been followed in the U.S. and its effect on growth of COVID-19 cases. The correlation between the COVID-19 growth rate and travel distance decay rate and dwell time at home change rate was -0.586 (95% CI: -0.742 ~ -0.370) and 0.526 (95% CI: 0.293 ~ 0.700), respectively. Increases in state-specific doubling time of total cases ranged from 1.04 ~ 6.86 days to 3.66 ~ 30.29 days after social distancing orders were put in place, consistent with mechanistic epidemic prediction models. Social distancing mandates reduce the spread of COVID-19 when they are followed.

Title: Impact of Timing of and Adherence to Social Distancing Measures on COVID-19 Burden in the US: A Simulation Modeling Approach Content: BACKGROUND: Across the U.S., various social distancing measures were implemented to control COVID-19 pandemic. However, there is uncertainty in the effectiveness of such measures for specific regions with varying population demographics and different levels of adherence to social distancing. The objective of this paper is to determine the impact of social distancing measures in unique regions. METHODS: We developed COVid-19 Agent-based simulation Model (COVAM), an agent-based simulation model (ABM) that represents the social network and interactions among the people in a region considering population demographics, limited testing availability, imported infections from outside of the region, asymptomatic disease transmission, and adherence to social distancing measures. We adopted COVAM to represent COVID-19-associated events in Dane County, Wisconsin, Milwaukee metropolitan area, and New York City (NYC). We used COVAM to evaluate the impact of three different aspects of social distancing: 1) Adherence to social distancing measures; 2) timing of implementing social distancing; and 3) timing of easing social distancing. RESULTS: We found that the timing of social distancing and adherence level had a major effect on COVID-19 occurrence. For example, in NYC, implementing social distancing measures on March 5, 2020 instead of March 12, 2020 would have reduced the total number of confirmed cases from 191,984 to 43,968 as of May 30, whereas a 1-week delay in implementing such measures could have increased the number of confirmed cases to 1,299,420. Easing social distancing measures on June 1, 2020 instead of June 15, 2020 in NYC would increase the total number of confirmed cases from 275,587 to 379,858 as of July 31. CONCLUSION: The timing of implementing social distancing measures, adherence to the measures, and timing of their easing have major effects on the number of COVID-19 cases. PRIMARY FUNDING SOURCE: National Institute of Allergy and Infectious Diseases Institute

Title: Flattening the curve and the effect of atypical events on mitigation measures in Mexico: a modeling perspective Content: On 23 and 30 March 2020 the Mexican Federal government implemented social distancing measures to mitigate the COVID-19 epidemic. We use a mathematical model to explore atypical transmission events within the confinement period, triggered by the timing and strength of short time perturbations of social distancing. We show that social distancing measures were successful in achieving a significant reduction of the effective contact rate in the early weeks of the intervention. However, "flattening the curve" had an undesirable effect, since the epidemic peak was delayed too far, almost to the government preset day for lifting restrictions (01 June 2020). If the peak indeed occurs in late May or early June, then the events of children's day and mother's day may either generate a later peak (worst case scenario), a long plateau with relatively constant but high incidence (middle case scenario) or the same peak date as in the original baseline epidemic curve, but with a post-peak interval of slower decay.

Title: Quantifying what could have been – the impact of the Australian and New Zealand governments’ response to COVID-19 Content: The Australian and New Zealand governments both initiated strict social distancing measures in response to the COVID-19 pandemic in late March. It remains difficult to quantify the impact this had in reducing the spread of the virus. Bayesian structural time series model provide a model to quantify the scenario in which these government-level interventions were not placed. Our models predict these strict social distancing measures caused a 79% and 61% reduction in the daily cases of COVID-19 across Australia and New Zealand respectively. This provides both evidence and impetus for governments considering similar measures in response to COVID-19 and other pandemics.

Title: The effect of social distance measures on COVID-19 epidemics in Europe: an interrupted time series analysis Content: Following the introduction of unprecedented “stay-at-home” national policies, the COVID-19 pandemic recently started declining in Europe. Our research aims were to characterize the changepoint in the flow of the COVID-19 epidemic in each European country and to evaluate the association of the level of social distancing with the observed decline in the national epidemics. Interrupted time series analyses were conducted in 28 European countries. Social distance index was calculated based on Google Community Mobility Reports. Changepoints were estimated by threshold regression, national findings were analyzed by Poisson regression, and the effect of social distancing in mixed effects Poisson regression model. Our findings identified the most probable changepoints in 28 European countries. Before changepoint, incidence of new COVID-19 cases grew by 24% per day on average. From the changepoint, this growth rate was reduced to 0.9%, 0.3% increase, and to 0.7% and 1.7% decrease by increasing social distancing quartiles. The beneficial effect of higher social distance quartiles (i.e., turning the increase into decline) was statistically significant for the fourth quartile. Notably, many countries in lower quartiles also achieved a flat epidemic curve. In these countries, other plausible COVID-19 containment measures could contribute to controlling the first wave of the disease. The association of social distance quartiles with viral spread could also be hindered by local bottlenecks in infection control. Our results allow for moderate optimism related to the gradual lifting of social distance measures in the general population, and call for specific attention to the protection of focal micro-societies enriching high-risk elderly subjects, including nursing homes and chronic care facilities.

Title: How long does a lockdown need to be? Content: Social distancing, often in the form of lockdown, has been adopted by many countries as a way to contrast the spreading of COVID-19. We discuss the temporal aspects of social distancing in contrasting an epidemic diffusion. We argue that a strategy based uniquely on social distancing requires to maintain the relative measures for a very long time, while a more articulate strategy, which also uses early detection and prompt isolation, can be both more efficient on reducing the epidemic peak and allow to relax the social distancing measures after a much shorter time. We consider in more detail the situation in Italy, simulating the effect of different strategies through a recently introduced SIR-type epidemiological model. The short answer to the question in the title is:"it depends on what else you do".

Title: The science of social distancing and total lock down: Does it work? whom does it benefit? Content: COVID-19 is spiralling like wild fire and has caused a global mayhem. Many lives have already been lost, and the global economy is at stake. In the absence of vaccine, social distancing and total lock down is the current mainstay in efforts to mitigate and flatten the epidemic curves. Several countries across the globe are currently implementing social distancing and total lock downs. However, what is social distancing and lock down? Does it work? Whom does it benefit? We expound on these questions and conclude that social distancing and lock down benefit the local and international community alike. Moreover, we hypothesis that evacuations may facilitate international spread of a highly contagious disease of pandemic potential.

Title: Quantifying what could have been - The impact of the Australian and New Zealand governments' response to COVID-19 Content: BACKGROUND: The Australian and New Zealand governments both initiated strict social distancing measures in response to the COVID-19 pandemic in late March. It remains difficult to quantify the impact this had in reducing the spread of the virus. METHODS: Bayesian structural time series model provide a model to quantify the scenario in which these government-level interventions were not placed. Our models predict these strict social distancing measures caused a 79% and 61% reduction in the daily cases of COVID-19 across Australia and New Zealand respectively. CONCLUSION: This provides both evidence and impetus for governments considering similar measures in response to COVID-19 and other pandemics.

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: Social Distancing is Effective at Mitigating COVID-19 Transmission in the United States Content: COVID-19 is present in every state and over 90 percent of all counties in the United States. Decentralized government efforts to reduce spread, combined with the complex dynamics of human mobility and the variable intensity of local outbreaks makes assessing the effect of large-scale social distancing on COVID-19 transmission in the U.S. is a challenge. We generate a novel metric to represent social distancing behavior derived from mobile phone data, and examine its relationship with COVID-19 case reports at the county level. Our analysis reveals that social distancing is strongly correlated with decreased COVID-19 case growth rates for the 25 most affected counties in the United States, with a lag period consistent with the incubation time of SARS-CoV-2. We also demonstrate evidence that social distancing was already under way in many U.S. counties before county and state-level policies were implemented. This study strongly supports social distancing as an effective way to mitigate COVID-19 transmission in the United States.

Title: The effect of social distance measures on COVID-19 epidemics in Europe: an interrupted time series analysis Content: Following the introduction of unprecedented "stay-at-home" national policies, the COVID-19 pandemic recently started declining in Europe. Our research aims were to characterize the changepoint in the flow of the COVID-19 epidemic in each European country and to evaluate the association of the level of social distancing with the observed decline in the national epidemics. Interrupted time series analyses were conducted in 28 European countries. Social distance index was calculated based on Google Community Mobility Reports. Changepoints were estimated by threshold regression, national findings were analyzed by Poisson regression, and the effect of social distancing in mixed effects Poisson regression model. Our findings identified the most probable changepoints in 28 European countries. Before changepoint, incidence of new COVID-19 cases grew by 24% per day on average. From the changepoint, this growth rate was reduced to 0.9%, 0.3% increase, and to 0.7% and 1.7% decrease by increasing social distancing quartiles. The beneficial effect of higher social distance quartiles (i.e., turning the increase into decline) was statistically significant for the fourth quartile. Notably, many countries in lower quartiles also achieved a flat epidemic curve. In these countries, other plausible COVID-19 containment measures could contribute to controlling the first wave of the disease. The association of social distance quartiles with viral spread could also be hindered by local bottlenecks in infection control. Our results allow for moderate optimism related to the gradual lifting of social distance measures in the general population, and call for specific attention to the protection of focal micro-societies enriching high-risk elderly subjects, including nursing homes and chronic care facilities.

Title: Social Distancing Causally Impacts the Spread of SARS-CoV-2: A U.S. Nationwide Event Study Content: Background: To date, no study has examined the effectiveness of social distancing, while controlling for social mobility and social distancing restrictions in the United States. We utilize the quasi-experimental setting created by the nationwide protests precipitated by George Floyd's tragic death on May 25, 2020, to assess the causal impact of social distancing on the spread of SARS-CoV-2. Methods: Our sample period spans from January 22, 2020, to June 20, 2020, and consists of 474,422 county-days representing 3,142 counties from all 50 states and the District of Columbia. To assess the change in COVID-19 case counts following the protests, we employ a differences in differences estimation strategy in a multivariate setting, in which we control for social distancing restrictions and social mobility across counties. We also control for covariates that may influence COVID-19 transmission, and implement placebo tests using a Monte Carlo simulation. Findings: We document a country wide increase of over 3.06 cases per day, per 100,000 population, following the onset of the protests (95%CI: 2.47-3.65), and a further increase of 1.73 cases per day, per 100,000 population, in the counties in which the protests took place (95%CI: 0.59- 2.87). Relative to the week preceding the onset of the protests, this represents a 61.2% country wide increase in COVID-19 cases, and a further 34.6% increase in the protest counties. Interpretation: Our study documents a significant increase in COVID-19 case counts in counties that experienced a protest, and we conclude that social distancing practices causally impact the spread of SARS-CoV-2. The observed effect cannot be explained by changes in social distancing restrictions and social mobility, and placebo tests rule out the possibility that this finding is attributable to chance.

Title: The effect of COVID-19 and subsequent social distancing on travel behavior Content: Abstract The spread of the COVID-19 virus has resulted in unprecedented measures restricting travel and activity participation in many countries. Social distancing, i.e., reducing interactions between individuals in order to slow down the spread of the virus, has become the new norm. In this viewpoint I will discuss the potential implications of social distancing on daily travel patterns. Avoiding social contact might completely change the number and types of out-of-home activities people perform, and how people reach these activities. It can be expected that the demand for travel will reduce and that people will travel less by public transport. Social distancing might negatively affect subjective well-being and health status, as it might result in social isolation and limited physical activity. As a result, walking and cycling, recreationally or utilitarian, can be important ways to maintain satisfactory levels of health and well-being. Policymakers and planners should consequently try to encourage active travel, while public transport operators should focus on creating ways to safely use public transport.

Title: Effectiveness and Compliance to Social Distancing During COVID-19 Content: In the absence of pharmaceutical interventions to curb the spread of COVID-19, countries relied on a number of nonpharmaceutical interventions to fight the first wave of the pandemic. The most prevalent one has been stay-at-home orders, whose the goal is to limit the physical contact between people, which consequently will reduce the number of secondary infections generated. In this work, we use a detailed set of mobility data to evaluate the impact that these interventions had on alleviating the spread of the virus in the US as measured through the COVID-19-related deaths. To establish this impact, we use the notion of Granger causality between two time-series. We show that there is a unidirectional Granger causality, from the median percentage of time spent daily at home to the daily number of COVID-19-related deaths with a lag of 2 weeks. We further analyze the mobility patterns at the census block level to identify which parts of the population might encounter difficulties in adhering and complying with social distancing measures. This information is important, since it can consequently drive interventions that aim at helping these parts of the population.

Title: Social distancing: A non-pharmacological intervention for COVID-19 Content: Social distancing is one of the non-pharmacological measures to contain the infection of COVID-19. At this point in time, no vaccine is available to prevent the infection, no effective drugs are available to prevent and treat the disease, and none of the communities have acquired herd immunity. Various models have shown positive impact of social distancing, provided its implementation on vast majority of the population over a long period of time. Its effect is manifold. Besides flattening the curve, it impacts the political, fiscal, social, economic aspects of the society, along with socially vulnerable and economically underprivileged population. It becomes obsolete after the population develops herd immunity subsequent to widespread infection in the community, or after effective mass immunisation or specific drugs for its control, cure and prevention are available widely.

Title: Social distancing: A non-pharmacological intervention for COVID-19. Content: Social distancing is one of the non-pharmacological measures to contain the infection of COVID-19. At this point in time, no vaccine is available to prevent the infection, no effective drugs are available to prevent and treat the disease, and none of the communities have acquired herd immunity. Various models have shown positive impact of social distancing, provided its implementation on vast majority of the population over a long period of time. Its effect is manifold. Besides flattening the curve, it impacts the political, fiscal, social, economic aspects of the society, along with socially vulnerable and economically underprivileged population. It becomes obsolete after the population develops herd immunity subsequent to widespread infection in the community, or after effective mass immunisation or specific drugs for its control, cure and prevention are available widely.

Title: A novel COVID-19 epidemiological model with explicit susceptible and asymptomatic isolation compartments reveals unexpected consequences of timing social distancing Content: Motivated by the current COVID-19 epidemic, this work introduces an epidemiological model in which separate compartments are used for susceptible and asymptomatic "socially distant" populations. Distancing directives are represented by rates of flow into these compartments, as well as by a reduction in contacts that lessens disease transmission. The dynamical behavior of this system is analyzed, under various different rate control strategies, and the sensitivity of the basic reproduction number to various parameters is studied. One of the striking features of this model is the existence of a critical implementation delay in issuing separation mandates: while a delay of about four weeks does not have an appreciable effect, issuing mandates after this critical time results in a far greater incidence of infection. In other words, there is a nontrivial but tight "window of opportunity" for commencing social distancing. Different relaxation strategies are also simulated, with surprising results. Periodic relaxation policies suggest a schedule which may significantly inhibit peak infective load, but that this schedule is very sensitive to parameter values and the schedule's frequency. Further, we considered the impact of steadily reducing social distancing measures over time. We find that a too-sudden reopening of society may negate the progress achieved under initial distancing guidelines, if not carefully designed.

Title: Evaluating the Effectiveness of Social Distancing Interventions to Delay or Flatten the Epidemic Curve of Coronavirus Disease Content: By April 2, 2020, >1 million persons worldwide were infected with severe acute respiratory syndrome coronavirus 2. We used a mathematical model to investigate the effectiveness of social distancing interventions in a mid-sized city. Interventions reduced contacts of adults >60 years of age, adults 20-59 years of age, and children <19 years of age for 6 weeks. Our results suggest interventions started earlier in the epidemic delay the epidemic curve and interventions started later flatten the epidemic curve. We noted that, while social distancing interventions were in place, 20% of new cases and most hospitalizations and deaths were averted, even with modest reductions in contact among adults. However, when interventions ended, the epidemic rebounded. Our models suggest that social distancing can provide crucial time to increase healthcare capacity but must occur in conjunction with testing and contact tracing of all suspected cases to mitigate virus transmission.

Title: Evaluating the Effectiveness of Social Distancing Interventions to Delay or Flatten the Epidemic Curve of Coronavirus Disease. Content: By April 2, 2020, >1 million persons worldwide were infected with severe acute respiratory syndrome coronavirus 2. We used a mathematical model to investigate the effectiveness of social distancing interventions in a mid-sized city. Interventions reduced contacts of adults >60 years of age, adults 20-59 years of age, and children <19 years of age for 6 weeks. Our results suggest interventions started earlier in the epidemic delay the epidemic curve and interventions started later flatten the epidemic curve. We noted that, while social distancing interventions were in place, 20% of new cases and most hospitalizations and deaths were averted, even with modest reductions in contact among adults. However, when interventions ended, the epidemic rebounded. Our models suggest that social distancing can provide crucial time to increase healthcare capacity but must occur in conjunction with testing and contact tracing of all suspected cases to mitigate virus transmission.

Title: Social network-based distancing strategies to flatten the COVID 19 curve in a post-lockdown world Content: Social distancing and isolation have been introduced widely to counter the COVID-19 pandemic. However, more moderate contact reduction policies become desirable owing to adverse social, psychological, and economic consequences of a complete or near-complete lockdown. Adopting a social network approach, we evaluate the effectiveness of three targeted distancing strategies designed to 'keep the curve flat' and aid compliance in a post-lockdown world. These are limiting interaction to a few repeated contacts, seeking similarity across contacts, and strengthening communities via triadic strategies. We simulate stochastic infection curves that incorporate core elements from infection models, ideal-type social network models, and statistical relational event models. We demonstrate that strategic reduction of contact can strongly increase the efficiency of social distancing measures, introducing the possibility of allowing some social contact while keeping risks low. This approach provides nuanced insights to policy makers for effective social distancing that can mitigate negative consequences of social isolation.

Title: Voluntary Cyclical Distancing: A potential alternative to constant level mandatory social distancing, relying on an 'infection weather report' Content: COVID-19 has significantly changed our daily lives. Stay-at-home orders and forced closings of all non-essential businesses has had a significant impact on our economy. While it is important to ensure that the healthcare system is not overwhelmed, there are many questions that remain about the efficacy of extreme social distancing, and whether there are alternatives to mandatory lockdowns. This paper analyzes the utility of various levels of social distancing, and suggests an alternative approach using voluntary distancing informed by an infectious load index or 'infection weather report.'

Title: Strong Social Distancing Measures In The United States Reduced The COVID-19 Growth Rate Content: State and local governments imposed social distancing measures in March and April 2020 to contain the spread of the novel coronavirus disease (COVID-19). These measures included bans on large social gatherings; school closures; closures of entertainment venues, gyms, bars, and restaurant dining areas; and shelter-in-place orders. We evaluated the impact of these measures on the growth rate of confirmed COVID-19 cases across US counties between March 1, 2020, and April 27, 2020. An event study design allowed each policy's impact on COVID-19 case growth to evolve over time. Adoption of government-imposed social distancing measures reduced the daily growth rate of confirmed COVID-19 cases by 5.4 percentage points after one to five days, 6.8 percentage points after six to ten days, 8.2 percentage points after eleven to fifteen days, and 9.1 percentage points after sixteen to twenty days. Holding the amount of voluntary social distancing constant, these results imply that there would have been ten times greater spread of COVID-19 by April 27 without shelter-in-place orders (ten million cases) and more than thirty-five times greater spread without any of the four measures (thirty-five million cases). Our article illustrates the potential danger of exponential spread in the absence of interventions, providing information relevant to strategies for restarting economic activity.

Title: Isolation and contact tracing can tip the scale to containment of COVID-19 in populations with social distancing Content: Background: Novel coronavirus (SARS-CoV-2) has extended its range of transmission in all parts of the world, with substantial variation in rates of transmission and severity of associated disease. Many countries have implemented social distancing as a measure to control further spread. Methods: We evaluate whether and under which conditions containment or slowing down COVID-19 epidemics are possible by isolation and contact tracing in settings with various levels of social distancing. We use a stochastic transmission model in which every person generates novel infections according to a probability distribution that is affected by the incubation period distribution (time from infection to symptoms), distribution of the latent period (time from infection to onset of infectiousness), and overall transmissibility. The model distinguishes between close contacts (e.g., within a household) and other contacts in the population. Social distancing affects the number of contacts outside but not within the household. Findings: The proportion of asymptomatic or unascertained cases has a strong impact on the controllability of the disease. If the proportion of asymptomatic infections is larger than 30%, contact tracing and isolation cannot achieve containment for an R0 of 2.5. Achieving containment by social distancing requires a reduction of numbers of non-household contacts by around 90%. Depending on the realized level of contact reduction, tracing and isolation of only household contacts, or of household and non-household contacts are necessary to reduce the effective reproduction number to below 1. A combination of social distancing with isolation and contact tracing leads to synergistic effects that increase the prospect of containment. Interpretation: Isolation and contact tracing can be an effective means to slow down epidemics, but only if the majority of cases are ascertained. In a situation with social distancing, contact tracing can act synergistically and tip the scale towards containment, and can therefore be a tool for controlling COVID-19 epidemics as part of an exit strategy from current lockdown measures.

Title: Act early, save lives: managing COVID-19 in Greece Content: Abstract Objectives: To assess the impact of the implemented social distancing interventions (SD) in Greece. Study Design: A dynamic, discrete time, stochastic individual-based model was developed to simulate COVID-19 transmission. Methods: We fit the transmission model to the observed trends in deaths and ICU beds use. Results: If Greece had not implemented the SD measures, the healthcare system would have been overwhelmed between 30 March and 4 of April. Additionally, the SD interventions averted 4360 deaths and prevent the healthcare system from overwhelmed. Conclusions: The fast reflexes of the Greek government limit the burden of the Covid-19 outbreak.

Title: Forecasting the Spread of COVID-19 under Different Reopening Strategies Content: We combine COVID-19 case data with demographic and mobility data to estimate a modified susceptible-infected-recovered (SIR) model for the spread of this disease in the United States. We find that the incidence of infectious COVID-19 individuals has a concave effect on contagion, as would be expected if people have inter-related social networks. We also demonstrate that social distancing and population density have large effects on the rate of contagion. The social distancing in late March and April substantially reduced the number of COVID-19 cases. However, the concave contagion pattern means that when social distancing measures are lifted, the growth rate is considerable but will not be exponential as predicted by standard SIR models. Furthermore, counties with the lowest population density could likely avoid high levels of contagion even with no social distancing. We forecast rates of new cases for COVID-19 under different social distancing norms and find that if social distancing is eliminated there will be a massive increase in the cases of COVID-19, about double what would occur if the US only restored to 50% of the way to normalcy.

Title: Investigating duration and intensity of Covid-19 social-distancing strategies Content: The exponential character of the recent Covid-19 outbreak requires a change in strategy from containment to mitigation. Meanwhile, most countries apply social distancing with the objective to keep the number of critical cases below the capabilities of the health care system. Due to the novelty and rapid spread of the virus, an a priori assessment of this strategy was not possible. In this study, we present a model-based systems analysis to assess the effectiveness of social distancing measures in terms of intensity and duration of application. Results show a super-linear scaling between intensity (percent contact reduction) and required duration of application to have an added value (lower fatality rate). This holds true for an effective reproduction of R > 1 and is reverted for R < 1. If R is not reduced below 1, secondary effects of required long-term isolation are likely to unravel the added value of disease mitigation. We recommend an extinction strategy implemented by intense countermeasures.

Title: Social distancing, population density, and spread of COVID-19 in England: a longitudinal study Content: BACKGROUND: The UK government introduced social distancing measures between 16-22 March 2020, aiming to slow down transmission of COVID-19. AIM: To explore the spreading of COVID-19 in relation to population density after the introduction of social distancing measures. DESIGN & SETTING: Longitudinal design with 5-weekly COVID-19 incidence rates per 100 000 people for 149 English Upper Tier Local Authorities (UTLAs), between 16 March and 19 April 2020. METHOD: Multivariable multilevel model to analyse weekly incidence rates per 100 000 people; time was level-1 unit and UTLA level-2 unit. Population density was divided into quartiles. The model included an interaction between week and population density. Potential confounders were percentage aged ≥65, percentage non-white British, and percentage in two highest classes of the National Statistics Socioeconomic Classification. Co-variates were male life expectancy at birth, and COVID-19 prevalence rate per 100 000 people on March 15. Confounders and co-variates were standardised around the mean. RESULTS: Incidence rates per 100 000 people peaked in the week of March 30-April 5, showing higher adjusted incidence rate per 100 000 people (46.2; 95% confidence interval [CI] = 40.6 to 51.8) in most densely populated ULTAs (quartile 4) than in less densely populated ULTAs (quartile 1: 33.3, 95% CI = 27.4 to 37.2; quartile 2: 35.9, 95% CI = 31.6 to 40.1). Thereafter, incidence rate dropped in the most densely populated ULTAs resulting in rate of 22.4 (95% CI = 16.9 to 28.0) in the week of April 13-19; this was lower than in quartiles 1, 2, and 3, respectively 31.4 (95% CI = 26.5 to 36.3), 34.2 (95% CI = 29.9 to 38.5), and 43.2 (95% CI = 39.0 to 47.4). CONCLUSION: After the introduction of social distancing measures, the incidence rates per 100 000 people dropped stronger in most densely populated ULTAs.

Title: The impact of early social distancing at COVID-19 Outbreak in the largest Metropolitan Area of Brazil. Content: We evaluated the impact of early social distancing on the COVID-19 transmission in the Sao Paulo metropolitan area. Using an age-stratified SEIR model, we determined the time-dependent reproductive number, and forecasted the ICU beds necessary to tackle this epidemic. Within 60 days, these measures might prevent 89,133 deaths.

Title: DOUBLE POWER LAW FOR COVID-19: PREDICTION OF NEW CASES AND DEATH RATES IN ITALY AND SPAIN Content: The novel corona virus SARS-CoV-2 appeared at the end of 2019, spreading rapidly and causing a severe respiratory syndrome (COVID-19) with high mortality (2-5%). Until a vaccine or therapy is found, the most effective method of prophylaxis has been to minimize transmission via rigorous social distancing and seclusion of all but essential workers. Such measures, implemented at different times and to varying degrees worldwide, have reduced the rate of transmission compared with early phases of the pandemic, resulting in flattening of the curve followed by a gradual reduction in mortality after >6 weeks of rigorous social distancing measures. The cost of rigorous social distancing has been seen in radically reduced economic activity, job losses, disruption of schooling and social institutions. A key question facing policy makers and individuals is when to resume normal economic and social activity in the face of persistent community transmission of SARS-CoV-2 . To help address this question, we have developed a model that accurately describes the entire transmission and mortality curves in Italy and Spain, two hard-hit countries that have maintained severe social distancing measures for over 2 months. Our model quantitatively describes the rapid rise and slow decay of new cases and deaths observed under stringent social distancing (the long tail effect). We predict that even when social distancing is rigorously maintained, the number of COVID-19 deaths after peak mortality may be 2-3 times larger than the total number of deaths up to the peak. Our model has important policy implications for countries currently debating how to ease social distancing measures.

Title: Differential Effects of Intervention Timing on COVID-19 Spread in the United States Content: Assessing the effects of early non-pharmaceutical interventions(1–5) on COVID-19 spread in the United States is crucial for understanding and planning future control measures to combat the ongoing pandemic(6–10). Here we use county-level observations of reported infections and deaths(11), in conjunction with human mobility data(12) and a metapopulation transmission model(13,14), to quantify changes of disease transmission rates in US counties from March 15, 2020 to May 3, 2020. We find significant reductions of the basic reproductive numbers in major metropolitan areas in association with social distancing and other control measures. Counterfactual simulations indicate that, had these same control measures been implemented just 1–2 weeks earlier, a substantial number of cases and deaths could have been averted. Specifically, nationwide, 56.5% [95% Cl: 48.1%−65.9%] of reported infections and 54.0% [95% Cl: 43.6%−63.8%] of reported deaths as of May 3, 2020 could have been avoided if the same control measures had been implemented just one week earlier. We also examine the effects of delays in re-implementing social distancing following a relaxation of control measures. A longer response time results in a stronger rebound of infections and death. Our findings underscore the importance of early intervention and aggressive response in controlling the COVID-19 pandemic.

Title: Causal Impact of Masks, Policies, Behavior on Early Covid-19 Pandemic in the U.S. Content: This paper evaluates the dynamic impact of various policies, such as school, business, and restaurant closures, adopted by the US states on the growth rates of confirmed Covid-19 cases and social distancing behavior measured by Google Mobility Reports, where we take into consideration of people's voluntarily behavioral response to new information of transmission risks. Using the US state-level data, our analysis finds that both policies and information on transmission risks are important determinants of people's social distancing behavior, and shows that a change in policies explains a large fraction of observed changes in social distancing behavior. Our counterfactual experiments indicate that removing all policies on April 1st of 2020 would have lead to 30 to 200 times more additional cases by late May. Removing only the non-essential businesses closures (while maintaining restrictions on movie theaters and restaurants) would have increased the weekly growth rate of cases between -0.02 and 0.06 and would have lead to -10% to 40% more cases by late May. Finally, nationally mandating face masks for employees on April 1st would have reduced the case growth rate by 0.1-0.25. This leads to 30% to 57% fewer reported cases by late May, which translates into, roughly, 30-57 thousand saved lives.

Title: Impact of Social Distancing Measures on COVID-19 Healthcare Demand in Central Texas Content: A novel coronavirus (SARS-CoV-2) emerged in Wuhan, China in late 2019 and rapidly spread worldwide. In the absence of effective antiviral drugs and vaccines, well-targeted social distancing measures are essential for mitigating the COVID-19 pandemic, reducing strain on local health systems, and preventing mortality. Here, we provide a quantitative assessment of the efficacy of social distancing to slow COVID-19 transmission and reduce hospital surge, depending on the timing and extent of the measures imposed for a metropolitan region and its health care systems. We built a granular mathematical model of COVID-19 transmission that incorporated age-specific and risk-stratified heterogeneity, estimates for the transmission, and severity of COVID-19 using current best evidence. We performed thousands of stochastic simulations of COVID-19 transmission in the Austin-Round Rock Metropolitan Area to project the impact of school closures coupled with social distancing measures that were estimated to reduce non-household contacts by 0%, 25%, 50%, 75% or 90%. We compare early versus late implementation and estimate the number of COVID-19 hospitalizations, ICU patients, ventilator needs and deaths through mid-August, 2020. We queried local emergency services and hospital systems to estimate total hospital bed, ICU, and ventilator capacity for the region. We expected COVID-19 hospital beds and ICU requirements would surpass local capacity by mid-May if no intervention was taken. Assuming a four-day epidemic doubling time, school closures alone would be expected to reduce peak hospitalizations by only 18% and cumulative deaths by less than 3%. Immediate social distancing measures that reduced non-household contacts by over 75%, such as stay-at-home orders and closing of non-essential businesses, would be required to ensure that COVID-19 cases do not overwhelm local hospital surge capacity. Peak ICU bed demand prior to mid August 2020 would be expected to be reduced from 2,121 (95% CI: 2,018-2,208) with no intervention to 698 (95% CI: 204-1,100) with 75% social distancing and 136 (95% CI: 38-308) with 90% social distancing; current ICU bed capacity was estimated at 680. A two-week delay in implementation of such measures is projected to accelerate a local ICU bed shortage by four weeks. School closures alone hardly impact the epidemic curve. Immediate social distancing measures that reduce non-household contacts by over 75% were required to ensure that COVID-19 cases do not overwhelm local hospital surge capacity. These findings helped inform the Stay Home-Work Safe order enacted by the city of Austin, Texas on March 24, 2020 as a means of mitigating the emerging COVID-19 epidemic.

Title: Effects of social distancing on the spreading of COVID-19 inferred from mobile phone data Content: A better understanding of how the COVID-19 epidemic responds to social distancing efforts is required for the control of future outbreaks and to calibrate partial lock-downs. We present quantitative relationships between key parameters characterizing the COVID-19 epidemiology and social distancing efforts of nine selected European countries. Epidemiological parameters were extracted from the number of daily deaths data, while mitigation efforts are estimated from mobile phone tracking data. The decrease of the basic reproductive number (R0) as well as the duration of the initial exponential expansion phase of the epidemic strongly correlates with the magnitude of mobility reduction. Utilizing these relationships we decipher the relative impact of the timing and the extent of social distancing on the total death burden of the epidemic.

Title: Modeling the dynamics of COVID19 spread during and after social distancing Content: Non-pharmaceutical intervention measures, such as social distancing, have so far been the only means to slow the spread of COVID19. In the United States, strict social distancing has resulted in different types infection dynamics. In some states, such as New York, extensive infection spread was followed by a pronounced decline of infection levels. In other states, such as California, less infection spread occurred before strict social distancing, and a different pattern was observed. Instead of a pronounced infection decline, a long-lasting plateau is evident, characterized by similar daily new infection levels. While these plateau dynamics cannot be readily reproduced with standard SIR infection models, we show that network models, in which individuals and their social contacts are explicitly tracked, can reproduce the plateau if network connections are cut due to social distancing measures. The reason is that in networks characterized by a degree of 2D spatial structure, infection tends to spread quadratically with time, but as edges are randomly removed, the infection spreads along nearly one-dimensional infection "corridors", resulting in plateau dynamics. Interestingly, the plateau dynamics are predicted to eventually transition into an infection decline phase without any further increase in social distancing measures. Additionally, the models suggest that a potential second wave becomes significantly less pronounced if social distancing is only relaxed once the dynamics have transitioned to the decline phase. The network models analyzed here allow us to interpret and reconcile different infection dynamics during social distancing observed in various US states.

Title: A Modelling Analysis of Strategies for Relaxing COVID-19 Social Distancing Content: Abstract Background: The ability of countries to contain and control COVID-19 virus transmission via social distancing is critical in the absence of a vaccine. Early activation of robust measures has been shown to control the daily infection rate, and consequential pressure on the health care system. As countries begin to control COVID-19 spread an understanding of how to ease social distancing measures to prevent a rebound in cases and deaths is required. Methods: Using COVID-19 transmission data from the outbreak source in Hubei Province, China prior to activation of containment measures, we adapted an established individual-based simulation model of the city of Newcastle, Australia. Simulation of virus transmission in this model, with and without, social distancing measures activated permitted us to quantify social distancing effectiveness. Optimal strategies for relaxing social distancing were determined under two settings: with high numbers of daily cases, as in New York; and where early social distancing activation resulted in limited ongoing transmission, as in Perth, Australia. Findings: In countries where strong social distancing measures were activated after the COVID-19 virus had spread widely, our study found these measures are required to be maintained for significant periods before being eased, to return to a situation where daily case numbers become low. In countries where early responses to the COVID-19 pandemic have been highly successful, as in Australia, we show that a staged relaxation of social distancing prevents a rebound in cases. Interpretation: Modelling studies and direct observation have shown that robust and timely social distancing have the most effect in containing the spread of the COVID-19 virus. Questions arise as to the duration of strong social distancing measures, given they are highly disruptive to society and economic activity. This study demonstrates the necessity of holding robust social distancing in place until COVID-19 virus transmission has significantly decreased, and how they may then be safely eased.

Title: Association of County-Level Socioeconomic and Political Characteristics with Engagement in Social Distancing for COVID-19 Content: The U.S. is the epicenter of the coronavirus disease 2019 (COVID-19) pandemic. In response, governments have implemented measures to slow transmission through "social distancing." However, the practice of social distancing may depend on prevailing socioeconomic conditions and beliefs. Using 15-17 million anonymized cell phone records, we find that lower per capita income and greater Republican orientation were associated with significantly reduced social distancing among U.S. counties. These associations persisted after adjusting for county-level sociodemographic and labor market characteristics as well as state fixed effects. These results may help policymakers and health professionals identify communities that are most vulnerable to transmission and direct resources and communications accordingly.

Title: Modeling the effects of intervention strategies on COVID-19 transmission dynamics Content: OBJECTIVES: To model the effects of continuous, intermittent, and stepping-down social distancing (SD) strategies and personal protection measures on COVID-19 transmission dynamics. METHODS: Constant, intermittent, and stepping-down SD strategies were modeled at 4 mean magnitudes (5%, 10 %, 15 % and 20 %), 2 time windows (40-days, 80-days), and 2 levels of personal caution (30 % and 50 %). RESULTS: The stepping-down strategy was the best long-term SD strategy to minimize the peak number of active COVID-19 cases and associated deaths. The stepping-down strategy also resulted in a reduction in total time required to SD over a two-year period by 6.5 % compared to an intermittent or constant SD strategy. An 80-day SD time-window was statistically more effective in maintaining control over the COVID-19 pandemic than a 40-day window. However, the results were dependent upon 50 % of people being cautious (engaging in personal protection measures). CONCLUSION: If people exercise caution while in public by protecting themselves (e.g., wearing a facemask, proper hand hygiene and avoid agglomeration) the magnitude and duration of SD necessary to maintain control over the pandemic can be reduced. Our models suggest that the most effective way to reduce SD over a two-year period is a stepping-down approach every 80 days. According to our model, this method would prevent a second peak and the number of intensive care units needed per day would be within the threshold of those currently available.

Title: Modeling the effects of intervention strategies on COVID-19 transmission dynamics Content: OBJECTIVES: To model the effects of continuous, intermittent, and stepping-down social distancing (SD) strategies and personal protection measures on COVID-19 transmission dynamics. METHODS: Constant, intermittent, and stepping-down SD strategies were modeled at 4 mean magnitudes (5%, 10%, 15% and 20%), 2 time windows (40-days, 80-days), and 2 levels of personal caution (30% and 50%). RESULTS: The stepping-down strategy was the best long-term SD strategy to minimize the peak number of active COVID-19 cases and associated deaths. The stepping-down strategy also resulted in a reduction in total time required to SD over a two-year period by 6.5% compared to an intermittent or constant SD strategy. An 80-day SD time-window was statistically more effective in maintaining control over the COVID-19 pandemic than a 40-day window. However, the results were dependent upon 50% of people being cautious (engaging in personal protection measures). CONCLUSION: If people exercise caution while in public by protecting themselves (e.g., wearing a facemask, proper hand hygiene and avoid agglomeration) the magnitude and duration of SD necessary to maintain control over the pandemic can be reduced. Our models suggest that the most effective way to reduce SD over a two-year period is a stepping-down approach every 80 days. According to our model, this method would prevent a second peak and the number of intensive care units needed per day would be within the threshold of those currently available.

Title: Social distancing, population density, and spread of COVID-19 in England: a longitudinal study. Content: BACKGROUND The UK government introduced social distancing measures between 16-22 March 2020, aiming to slow down transmission of COVID-19. AIM To explore the spreading of COVID-19 in relation to population density after the introduction of social distancing measures. DESIGN & SETTING Longitudinal design with 5-weekly COVID-19 incidence rates per 100 000 people for 149 English Upper Tier Local Authorities (UTLAs), between 16 March and 19 April 2020. METHOD Multivariable multilevel model to analyse weekly incidence rates per 100 000 people; time was level-1 unit and UTLA level-2 unit. Population density was divided into quartiles. The model included an interaction between week and population density. Potential confounders were percentage aged ≥65, percentage non-white British, and percentage in two highest classes of the National Statistics Socioeconomic Classification. Co-variates were male life expectancy at birth, and COVID-19 prevalence rate per 100 000 people on March 15. Confounders and co-variates were standardised around the mean. RESULTS Incidence rates per 100 000 people peaked in the week of March 30-April 5, showing higher adjusted incidence rate per 100 000 people (46.2; 95% confidence interval [CI] = 40.6 to 51.8) in most densely populated ULTAs (quartile 4) than in less densely populated ULTAs (quartile 1: 33.3, 95% CI = 27.4 to 37.2; quartile 2: 35.9, 95% CI = 31.6 to 40.1). Thereafter, incidence rate dropped in the most densely populated ULTAs resulting in rate of 22.4 (95% CI = 16.9 to 28.0) in the week of April 13-19; this was lower than in quartiles 1, 2, and 3, respectively 31.4 (95% CI = 26.5 to 36.3), 34.2 (95% CI = 29.9 to 38.5), and 43.2 (95% CI = 39.0 to 47.4). CONCLUSION After the introduction of social distancing measures, the incidence rates per 100 000 people dropped stronger in most densely populated ULTAs.

Title: Analyzing situational awareness through public opinion to predict adoption of social distancing amid pandemic COVID-19 Content: COVID-19 pandemic has affected over 100 countries in a matter of weeks. People's response toward social distancing in the emerging pandemic is uncertain. In this study, we evaluated the influence of information (formal and informal) sources on situational awareness of the public for adopting health-protective behaviors such as social distancing. For this purpose, a questionnaire-based survey was conducted. The hypothesis proposed suggests that adoption of social distancing practices is an outcome of situational awareness which is achieved by the information sources. Results suggest that information sources, formal (P = .001) and informal (P = 0.007) were found to be significantly related to perceived understanding. Findings also indicate that social distancing is significantly influenced by situational awareness, P = .000. It can, therefore, be concluded that an increase in situational awareness in times of public health crisis using formal information sources can significantly increase the adoption of protective health behavior and in turn contain the spread of infectious diseases.

Title: Observed mobility behavior data reveal social distancing inertia Content: The research team has utilized an integrated dataset, consisting of anonymized location data, COVID-19 case data, and census population information, to study the impact of COVID-19 on human mobility. The study revealed that statistics related to social distancing, namely trip rate, miles traveled per person, and percentage of population staying at home have all showed an unexpected trend, which we named social distancing inertia. The trends showed that as soon as COVID-19 cases were observed, the statistics started improving, regardless of government actions. This suggests that a portion of population who could and were willing to practice social distancing voluntarily and naturally reacted to the emergence of COVID-19 cases. However, after about two weeks, the statistics saturated and stopped improving, despite the continuous rise in COVID-19 cases. The study suggests that there is a natural behavior inertia toward social distancing, which puts a limit on the extent of improvement in the social-distancing-related statistics. The national data showed that the inertia phenomenon is universal, happening in all the U.S. states and for all the studied statistics. The U.S. states showed a synchronized trend, regardless of the timeline of their statewide COVID-19 case spreads or government orders.

Title: Optimal control of the COVID-19 pandemic with non-pharmaceutical interventions Content: The COVID-19 pandemic has forced societies across the world to resort to social distancing to slow the spread of the SARS-CoV-2 virus. Due to the economic impacts of social distancing, there is growing desire to relax these measures. To characterize a range of possible strategies for control and to understand their consequences, we performed an optimal control analysis of a mathematical model of SARS-CoV-2 transmission. Given that the pandemic is already underway and controls have already been initiated, we calibrated our model to data from the US and focused our analysis on optimal controls from May 2020 through December 2021. We found that a major factor that differentiates strategies that prioritize lives saved versus reduced time under control is how quickly control is relaxed once social distancing restrictions expire in May 2020. Strategies that maintain control at a high level until summer 2020 allow for tapering of control thereafter and minimal deaths, whereas strategies that relax control in the short term lead to fewer options for control later and a higher likelihood of exceeding hospital capacity. Our results also highlight that the potential scope for controlling COVID-19 until a vaccine is available depends on epidemiological parameters about which there is still considerable uncertainty, including the basic reproduction number and the effectiveness of social distancing. In light of those uncertainties, our results do not constitute a quantitative forecast and instead provide a qualitative portrayal of possible outcomes from alternative approaches to control.

Title: The Effect of Social Distancing on the Reach of an Epidemic in Social Networks Content: How does social distancing affect the reach of an epidemic in social networks? We present Monte Carlo simulation results of a Susceptible- Infected-Removed (SIR) model on a network, where individuals are limited in the number of other people they can interact with. While increased social distancing always reduces the spread of an infectious disease, the magnitude varies greatly depending on the topology of the social network. Our results also reveal the importance of coordination at the global level. In particular, the public health benefits from social distancing to a group (e.g., a country) may be completely undone if that group maintains connections with outside groups that are not social distancing.

Title: Analyzing situational awareness through public opinion to predict adoption of social distancing amid pandemic COVID‐19 Content: COVID‐19 pandemic has affected over 100 countries in a matter of weeks. People's response toward social distancing in the emerging pandemic is uncertain. In this study, we evaluated the influence of information (formal and informal) sources on situational awareness of the public for adopting health‐protective behaviors such as social distancing. For this purpose, a questionnaire‐based survey was conducted. The hypothesis proposed suggests that adoption of social distancing practices is an outcome of situational awareness which is achieved by the information sources. Results suggest that information sources, formal (P = .001) and informal (P = 0.007) were found to be significantly related to perceived understanding. Findings also indicate that social distancing is significantly influenced by situational awareness, P = .000. It can, therefore, be concluded that an increase in situational awareness in times of public health crisis using formal information sources can significantly increase the adoption of protective health behavior and in turn contain the spread of infectious diseases.

Title: The Effectiveness of Social Distancing in Mitigating COVID-19 Spread: a modelling analysis Content: Background The novel coronavirus COVID19 has been classified by the World Health Organisation as a pandemic due to its worldwide spread. The ability of countries to contain and control transmission is critical in the absence of a vaccine. We evaluated a range of social distancing measures to determine which strategies are most effective in reducing the peak daily infection rate, and consequential pressure on the health care system. Methods Using COVID19 transmission data from the outbreak source in Hubei Province, China, collected prior to activation of containment measures, we adapted an established individual based simulation model of the city of Newcastle, Australia, population 272,409. Simulation of virus transmission in this community model without interventions provided a baseline from which to compare alternative social distancing strategies. The infection history of each individual was determined, as was the time infected. From this model generated data, the rate of growth in cases, the magnitude of the epidemic peak, and the outbreak duration were obtained. Findings The application of all four social distancing interventions: school closure, workplace non-attendance, increased case isolation, and community contact reduction is highly effective in flattening the epidemic curve, reducing the maximum daily case numbers, and lengthening outbreak duration. These were also found to be effective even after 10 weeks delay from index case arrivals. The most effective single intervention was found to be increasing case isolation, to 100 percent of children and 90 percent of adults. Interpretation As strong social distancing intervention strategies had the most effect in reducing the epidemic peak, this strategy may be considered when weaker strategies are first tried and found to be less effective. Questions arise as to the duration of strong social distancing measures, given they are highly disruptive to society. Tradeoffs may need to be made between the effectiveness of social distancing strategies and population willingness to adhere to them.

Title: COVID-19 pandemic-related lockdown: response time is more important than its strictness Content: The rapid spread of SARS-CoV-2 and its threat to health systems worldwide have led governments to take acute actions to enforce social distancing. Previous studies used complex epidemiological models to quantify the effect of lockdown policies on infection rates. However, these rely on prior assumptions or on official regulations. Here, we use country-specific reports of daily mobility from people cellular usage to model social distancing. Our data-driven model enabled the extraction of mobility characteristics which were crossed with observed mortality rates to show that: (1) the time at which social distancing was initiated is of utmost importance and explains 62% of the number of deaths, while the lockdown strictness or its duration are not as informative; (2) a delay of 7.49 days in initiating social distancing would double the number of deaths; and (3) the expected time from infection to fatality is 25.75 days and significantly varies among countries.

Title: Strong Social Distancing Measures In The United States Reduced The COVID-19 Growth Rate Content: State and local governments imposed social distancing measures in March and April of 2020 to contain the spread of novel coronavirus disease 2019 (COVID-19). These included large event bans, school closures, closures of entertainment venues, gyms, bars, and restaurant dining areas, and shelter-in-place orders (SIPOs). We evaluated the impact of these measures on the growth rate of confirmed COVID-19 cases across US counties between March 1, 2020 and April 27, 2020. An event-study design allowed each policy's impact on COVID-19 case growth to evolve over time. Adoption of government-imposed social distancing measures reduced the daily growth rate by 5.4 percentage points after 1-5 days, 6.8 after 6-10 days, 8.2 after 11-15 days, and 9.1 after 16-20 days. Holding the amount of voluntary social distancing constant, these results imply 10 times greater spread by April 27 without SIPOs (10 million cases) and more than 35 times greater spread without any of the four measures (35 million). Our paper illustrates the potential danger of exponential spread in the absence of interventions, providing relevant information to strategies for restarting economic activity. [Editor's Note: This Fast Track Ahead Of Print article is the accepted version of the peer-reviewed manuscript. The final edited version will appear in an upcoming issue of Health Affairs.].

Title: Strong Social Distancing Measures In The United States Reduced The COVID-19 Growth Rate. Content: State and local governments imposed social distancing measures in March and April of 2020 to contain the spread of novel coronavirus disease 2019 (COVID-19). These included large event bans, school closures, closures of entertainment venues, gyms, bars, and restaurant dining areas, and shelter-in-place orders (SIPOs). We evaluated the impact of these measures on the growth rate of confirmed COVID-19 cases across US counties between March 1, 2020 and April 27, 2020. An event-study design allowed each policy's impact on COVID-19 case growth to evolve over time. Adoption of government-imposed social distancing measures reduced the daily growth rate by 5.4 percentage points after 1-5 days, 6.8 after 6-10 days, 8.2 after 11-15 days, and 9.1 after 16-20 days. Holding the amount of voluntary social distancing constant, these results imply 10 times greater spread by April 27 without SIPOs (10 million cases) and more than 35 times greater spread without any of the four measures (35 million). Our paper illustrates the potential danger of exponential spread in the absence of interventions, providing relevant information to strategies for restarting economic activity. [Editor's Note: This Fast Track Ahead Of Print article is the accepted version of the peer-reviewed manuscript. The final edited version will appear in an upcoming issue of Health Affairs.].

Title: Age-structured impact of social distancing on the COVID-19 epidemic in India Content: The outbreak of the novel coronavirus, COVID-19, has been declared a pandemic by the WHO. The structures of social contact critically determine the spread of the infection and, in the absence of vaccines, the control of these structures through large-scale social distancing measures appears to be the most effective means of mitigation. Here we use an age-structured SIR model with social contact matrices obtained from surveys and Bayesian imputation to study the progress of the COVID-19 epidemic in India. The basic reproductive ratio R0 and its time-dependent generalization are computed based on case data, age distribution and social contact structure. The impact of social distancing measures - workplace non-attendance, school closure, lockdown - and their efficacy with durations are then investigated. A three-week lockdown is found insufficient to prevent a resurgence and, instead, protocols of sustained lockdown with periodic relaxation are suggested. Forecasts are provided for the reduction in age-structured morbidity and mortality as a result of these measures. Our study underlines the importance of age and social contact structures in assessing the country-specific impact of mitigatory social distancing.

Title: The association between age, COVID-19 symptoms, and social distancing behavior in the United States Content: BACKGROUND: Public health authorities recommend that people practice social distancing, especially if they have symptoms of coronavirus disease (COVID-19), or are older and more at risk of serious illness if they become infected. We test the hypothesis that these groups are following these recommendations and are more likely to undertake social distancing. METHODS: We conducted an open online survey of 4,676 U.S. adults aged 18 and older between April 4 and April 7, 2020. We model the effects of age and common COVID-19 symptoms in the last two weeks on going out of the home for non-healthcare reasons the day before taking the survey, using a logistic model and the number of close contacts (within 6 feet) that respondents had with non-household members, using a Poisson count model. Our models control for several covariates, including other flu-like symptoms, sex, education, income, whether the respondent worked in February, household size, population density in the respondent’s ZIP code, state fixed effects, and the day of completion of the survey. We also weight our analyses to make the sample representative of the U.S. adult population. FINDINGS: About 52 percent of the adult United States population went out of their home the previous day. On average, adults had close contact with 1.9 non-household members. We find that having at least one COVID-19 symptom (fever, dry cough, or shortness of breath) increased the likelihood of going out the previous day and having additional close contacts with non-household members; however, the estimates were not statistically significant. When disaggregating our analysis by COVID-19 symptoms, we find no strong evidence of greater social distancing by people with a fever or cough in the last two weeks, but we do find that those who experienced shortness of breath have fewer close contacts, with an incidence rate ratio (IRR) of 0.49 (95% CI: 0.30–0.78). Having other flu-like symptoms reduces the odds of going out by 0.32 (95% CI: 0.18–0.60) and the incidence rate of having close contacts by 42 percent (IRR = 0.58; 95% CI: 0.38–0.88). We find that older people are just as likely to leave their homes as younger people, but people over the age of 50 had less than half the predicted number of close contacts than those who were younger than 30. Our approach has the limitation that the survey sample is self-selected. Our findings may therefore be subject to selection bias that is not adequately controlled for by weighting. In addition, the possibility exists of confounding of the results due to omitted variable bias. CONCLUSIONS: We provide evidence that older people are having significantly fewer close contacts than younger people, which is in line with the public health authorities’ recommendations. We also find that people experiencing shortness of breath are practicing more intense social distancing. However, we find that those with two other common COVID-19 symptoms, fever and dry cough, are not engaging in greater social distancing, suggesting that increased targeting on relevant symptoms, and messaging, may be required.

Title: Impact assessment of non-pharmaceutical interventions against COVID-19 and influenza in Hong Kong: an observational study Content: Background: A range of public health measures have been implemented to delay and reduce local transmission of COVID-19 in Hong Kong, and there have been major changes in behaviours of the general public. We examined the effect of these interventions and behavioral changes on the incidence of COVID-19 as well as on influenza virus infections which may share some aspects of transmission dynamics with COVID-19. Methods: We reviewed policy interventions and measured changes in population behaviours through two telephone surveys, on January 20-23 and February 11-14. We analysed data on laboratory-confirmed COVID-19 cases, influenza surveillance data in outpatients of all ages, and influenza hospitalisations in children. We estimated the daily effective reproduction number (R_t), for COVID-19 and influenza A(H1N1). Findings: COVID-19 transmissibility has remained at or below 1, indicating successful containment to date. Influenza transmission declined substantially after the implementation of social distancing measures and changes in population behaviours in late January, with a 44% (95% confidence interval, CI: 34% to 53%) reduction in transmissibility in the community, and a 33% (95% CI: 24% to 43%) reduction in transmissibility based on paediatric hospitalization rates. In the two surveys we estimated that 74.5% and 97.5% of the general adult population wore masks when going out, and 61.3% and 90.2% avoided going to crowded places, respectively. Implications: Containment measures, social distancing measures and changes in population behaviour have successfully prevented spread of COVID-19. The social distancing measures and behavioural changes led to a substantial reduction in influenza transmission in early February 2020. However, it may be challenging to avoid fatigue and sustain these measures and population behaviours as COVID-19 continues to spread globally. Funding: Health and Medical Research Fund, Hong Kong

Title: Non-COVID-19 Deaths After Social Distancing in Norway Content: Lay persons and policy makers have speculated on how the imposition of social distancing to reduce SARS CoV-2 (severe acute respiratory syndrome coronavirus 2) infection has affected non-COVID-19 (coronavirus disease of 2019) deaths. No rigorous estimation of the effect appears in the scholarly literature. We use time-series methods to compare non-COVID-19 deaths observed in Norway before and during the epidemic to those expected from non-COVID-19 deaths in Sweden as well as from the history of Norwegian mortality trends. We find that in the first 8 weeks after the divergence between Swedish and Norwegian policies (the only period for which dependable data can be had) approximately 650 fewer Norwegians died than expected.

Title: Get the News Out Loudly and Quickly: The Influence of the Media on Limiting Emerging Infectious Disease Outbreaks Content: During outbreaks of infectious diseases with high morbidity and mortality, individuals closely follow media reports of the outbreak. Many will attempt to minimize contacts with other individuals in order to protect themselves from infection and possibly death. This process is called social distancing. Social distancing strategies include restricting socializing and travel, and using barrier protections. We use modeling to show that for short-term outbreaks, social distancing can have a large influence on reducing outbreak morbidity and mortality. In particular, public health agencies working together with the media can significantly reduce the severity of an outbreak by providing timely accounts of new infections and deaths. Our models show that the most effective strategy to reduce infections is to provide this information as early as possible, though providing it well into the course of the outbreak can still have a significant effect. However, our models for long-term outbreaks indicate that reporting historic infection data can result in more infections than with no reporting at all. We examine three types of media influence and we illustrate the media influence with a simulated outbreak of a generic emerging infectious disease in a small city. Social distancing can never be complete; however, for a spectrum of outbreaks, we show that leaving isolation (stopping applying social distancing measures) for up to 4 hours each day has modest effect on the overall morbidity and mortality.

Title: Quantifying Projected Impact of Social Distancing Policies on COVID-19 Outcomes in the US Content: Current social distancing measures to impede COVID-19 (such as shelter-in-place) are economically unsustainable in the long term. Models are needed to understand the implications of possible relaxation options for these measures. We report such models, together with corresponding parameter estimation techniques and prediction outcomes, borrowing insights from another domain; namely, information cascades. Our models assume that the containment of the virus into isolated locales is no longer possible. Instead, we explore options that reduce the rate of spread. We predict COVID-19 contagion trends in different geographic regions under a"what if"scenario to understand the effects of potential policy decisions on regional trends. Our model allows experimentation with other policies that, we hope, can contribute to socially viable outcomes both in terms of health system capacity and economic impact. We apply our model to over 30 highly impacted states in the US and publish the results at https://covid19predictions.csl.illinois.edu/

Title: Public perceptions and experiences of social distancing and social isolation during the COVID-19 pandemic: A UK-based focus group study Content: OBJECTIVE: Explore the perceptions and experiences of the UK public of social distancing and social isolation measures related to the COVID-19 pandemic. DESIGN: Qualitative study comprising five focus groups carried out online during the early stages of the UK's social distancing and isolation measures (5-12 days post lockdown). SETTING: Online video-conferencing PARTICIPANTS: 27 participants, all UK residents aged 18 years and older, representing a range of gender, ethnic, age and occupational backgrounds. RESULTS: The social distancing and isolation associated with COVID-19 policy has had having substantial negative impacts on the mental health and wellbeing of the UK public within a short time of policy implementation. It has disproportionately negatively affected those in low-paid or precarious employment. Practical social and economic losses - the loss of (in-person) social interaction, loss of income and loss of structure and routine - led to psychological and emotional 'losses' - the loss of motivation, loss of meaning, and loss of self-worth. Participants reported high adherence to distancing and isolation guidelines but reported seeing or hearing of non-adherence in others. A central concern for participants was the uncertainty duration of the measures, and their ability to cope longer-term. Some participants felt they would have lingering concerns over social contact while others were eager to return to high levels of social activity. CONCLUSIONS: A rapid response is necessary in terms of public health programming to mitigate the mental health impacts of COVID-19 social distancing and isolation. Initial high levels of support for, and adherence to, social distancing and isolation is likely to wane over time, particularly where end dates are uncertain. Social distancing and isolation 'exit strategies' must account for the fact that, although some individuals will voluntarily or habitually continue to socially distance, others will seek high levels of social engagement as soon as possible.

Title: Predictions of COVID-19 dynamics in the UK: short-term forecasting and analysis of potential exit strategies Content: Background: Efforts to suppress transmission of SARS-CoV-2 in the UK have seen non-pharmaceutical interventions being invoked. The most severe measures to date include all restaurants, pubs and cafes being ordered to close on 20th March, followed by a "stay at home" order on the 23rd March and the closure of all non-essential retail outlets for an indefinite period. Government agencies are presently analysing how best to develop an exit strategy from these measures and to determine how the epidemic may progress once measures are lifted. Mathematical models are currently providing short and long term forecasts regarding the future course of the COVID-19 outbreak in the UK to support evidence-based policymaking. Methods: We present a deterministic, age-structured transmission model that uses real-time data on confirmed cases requiring hospital care and mortality to provide up-to-date predictions on epidemic spread in ten regions of the UK. We simulated a suite of scenarios to assess the impact of differing approaches to relaxing social distancing measures from 7th May 2020, on the estimated number of patients requiring inpatient and critical care treatment, and deaths. With regard to future epidemic outcomes, we investigated the impact of reducing compliance, ongoing shielding of elder age groups, reapplying stringent social distancing measures using region based triggers and the role of asymptomatic transmission. Findings: We find that significant relaxation of social distancing measures on 7th May can lead to a rapid resurgence of COVID-19 disease and the health system being quickly overwhelmed by a sizeable, second epidemic wave. In all considered age-shielding based strategies, we projected serious demand on critical care resources during the course of the pandemic. The reintroduction and release of strict measures on a regional basis, based on ICU bed occupancy, results in a long epidemic tail, until the second half of 2021, but ensures that the health service is protected by reintroducing social distancing measures for all individuals in a region when required. Conclusions: Our work supports the decision to apply stringent non-pharmaceutical measures in March 2020 to suppress the epidemic. We provide strong evidence to support the need for a cautious, measured approach to relaxation of lockdown measures, to protect the most vulnerable members of society and support the health service through subduing demand on hospital beds, in particular bed occupancy in intensive care units.

Title: Quantifying the impact of physical distance measures on the transmission of COVID-19 in the UK Content: BACKGROUND: To mitigate and slow the spread of COVID-19, many countries have adopted unprecedented physical distancing policies, including the UK. We evaluate whether these measures might be sufficient to control the epidemic by estimating their impact on the reproduction number (R(0), the average number of secondary cases generated per case). METHODS: We asked a representative sample of UK adults about their contact patterns on the previous day. The questionnaire was conducted online via email recruitment and documents the age and location of contacts and a measure of their intimacy (whether physical contact was made or not). In addition, we asked about adherence to different physical distancing measures. The first surveys were sent on Tuesday, 24 March, 1 day after a “lockdown” was implemented across the UK. We compared measured contact patterns during the “lockdown” to patterns of social contact made during a non-epidemic period. By comparing these, we estimated the change in reproduction number as a consequence of the physical distancing measures imposed. We used a meta-analysis of published estimates to inform our estimates of the reproduction number before interventions were put in place. RESULTS: We found a 74% reduction in the average daily number of contacts observed per participant (from 10.8 to 2.8). This would be sufficient to reduce R(0) from 2.6 prior to lockdown to 0.62 (95% confidence interval [CI] 0.37–0.89) after the lockdown, based on all types of contact and 0.37 (95% CI = 0.22–0.53) for physical (skin to skin) contacts only. CONCLUSIONS: The physical distancing measures adopted by the UK public have substantially reduced contact levels and will likely lead to a substantial impact and a decline in cases in the coming weeks. However, this projected decline in incidence will not occur immediately as there are significant delays between infection, the onset of symptomatic disease, and hospitalisation, as well as further delays to these events being reported. Tracking behavioural change can give a more rapid assessment of the impact of physical distancing measures than routine epidemiological surveillance.

Title: Social distancing measures to control the COVID-19 pandemic: potential impacts and challenges in Brazil. Content: The COVID-19 pandemic has challenged researchers and policy makers to identify public safety measures forpreventing the collapse of healthcare systems and reducingdeaths. This narrative review summarizes the available evidence on the impact of social distancing measures on the epidemic and discusses the implementation of these measures in Brazil. Articles on the effect of social distancing on COVID-19 were selected from the PubMed, medRXiv and bioRvix databases. Federal and state legislation was analyzed to summarize the strategies implemented in Brazil. Social distancing measures adopted by the population appear effective, particularly when implemented in conjunction with the isolation of cases and quarantining of contacts. Therefore, social distancing measures, and social protection policies to guarantee the sustainability of these measures, should be implemented. To control COVID-19 in Brazil, it is also crucial that epidemiological monitoring is strengthened at all three levels of the Brazilian National Health System (SUS). This includes evaluating and usingsupplementary indicators to monitor the progression of the pandemic and the effect of the control measures, increasing testing capacity, and making disaggregated notificationsand testing resultstransparentand broadly available.

Title: Medidas de distanciamento social no controle da pandemia de COVID-19: potenciais impactos e desafios no Brasil./ Medidas de distanciamento social no controle da pandemia de COVID-19: potenciais impactos e desafios no Brasil./ Social distancing measures to control the COVID-19 pandemic: potential impacts and challenges in Brazil Content: The COVID-19 pandemic has challenged researchers and policy makers to identify public safety measures forpreventing the collapse of healthcare systems and reducingdeaths. This narrative review summarizes the available evidence on the impact of social distancing measures on the epidemic and discusses the implementation of these measures in Brazil. Articles on the effect of social distancing on COVID-19 were selected from the PubMed, medRXiv and bioRvix databases. Federal and state legislation was analyzed to summarize the strategies implemented in Brazil. Social distancing measures adopted by the population appear effective, particularly when implemented in conjunction with the isolation of cases and quarantining of contacts. Therefore, social distancing measures, and social protection policies to guarantee the sustainability of these measures, should be implemented. To control COVID-19 in Brazil, it is also crucial that epidemiological monitoring is strengthened at all three levels of the Brazilian National Health System (SUS). This includes evaluating and usingsupplementary indicators to monitor the progression of the pandemic and the effect of the control measures, increasing testing capacity, and making disaggregated notificationsand testing resultstransparentand broadly available.

Title: Impact of social distancing measures for preventing coronavirus disease 2019 : A systematic review and meta-analysis protocol Content: Abstract Introduction: Social distancing measures (SDMs) protect public health from the outbreak of coronavirus disease 2019 (COVID-19). However, the impact of SDMs has been inconsistent and unclear. This study aims to assess the effects of SDMs (e.g. isolation, quarantine) for reducing the transmission of COVID-19. Methods and analysis: We will conduct a systematic review meta-analysis research of both randomised controlled trials and non-randomised controlled trials. We will search MEDLINE, EMBASE, Allied & Complementary Medicine, COVID-19 Research and WHO database on COVID-19 for primary studies assessing the enablers and barriers associated with SDMs, and will be reported in accordance with PRISMA statement. The PRISMA-P checklist will be used while preparing this protocol. We will use Joanna Briggs Institute guidelines (JBI Critical Appraisal Checklists) to assess the methodological qualities and synthesised performing thematic analysis. Two reviewers will independently screen the papers and extracted data. If sufficient data are available, the random-effects model for meta-analysis will be performed to measure the effect size of SDMs or the strengths of relationships. To assess the heterogeneity of effects, I2 together with the observed effects (Q-value, with degrees of freedom) will be used to provide the true effects in the analysis. Ethics and dissemination: Ethics approval and consent will not be required for this systematic review of the literature as it does not involve human participation. We will be able to disseminate the study findings using the following strategies: we will be publishing at least one paper in peer-reviewed journals, and an abstract will be presented at suitable national/international conferences or workshops. We will also share important information with public health authorities as well as with the World Health Organization.

Title: Dynamic coupling between the COVID epidemic timeline and the behavioral response to PAUSE in New York State counties Content: By many expert opinions, the COVID 19 epidemic is still much in its developing stages. While awaiting for effective clinical answers for the outbreak, the best approach remains that of social distancing. This raises a few crucial questions related to the extent to which social distancing measures were (1) well timed, (2) necessary and (3) efficient. By investigating correlations between epidemic measures such as daily infection rate, and social mobility measures (as reported by Apple and Google), our study aims to establish whether data identifies social distancing measures as a primary player in controlling the outbreak in New York State.

Title: Quantifying the impact of physical distance measures on the transmission of COVID-19 in the UK Content: Background: To mitigate and slow the spread of COVID-19, many countries have adopted unprecedented physical distancing policies, including the UK. We evaluate whether these measures might be sufficient to control the epidemic by estimating their impact on the reproduction number (R0, the average number of secondary cases generated per case). Methods: We asked a representative sample of UK adults about their contact patterns on the previous day. The questionnaire documents the age and location of contacts and as well as a measure of their intimacy (whether physical contact was made or not). In addition, we asked about adherence to different physical distancing measures. The first surveys were sent on Tuesday 24th March, one day after a “lockdown” was implemented across the UK. We compared measured contact patterns during the lockdown to patterns of social contact made during a non-epidemic period. By comparing these, we estimated the change in reproduction number as a consequence of the physical distancing measures imposed. We used a meta-analysis of published estimates to inform our estimates of the reproduction number before interventions were put in place. Findings: We found a 73% reduction in the average daily number of contacts observed per participant (from 10.2 to 2.9). This would be sufficient to reduce R0 from 2.6 prior to lockdown to 0.62 (95% confidence interval [CI] 0.37 - 0.89) after the lockdown, based on all types of contact and 0.37 (95% CI = 0.22 - 0.53) for physical contacts only. Interpretation: The physical distancing measures adopted by the UK public have substantially reduced contact levels and will likely lead to a substantial impact and a decline in cases in the coming weeks. However, this projected decline in incidence will not occur immediately as there are significant delays between infection, the onset of symptomatic disease and hospitalisation, as well as further delays to these events being reported. Tracking behavioural change can give a more rapid assessment of the impact of physical distancing measures than routine epidemiological surveillance.

Title: Social Distancing as a Health Behavior: County-Level Movement in the United States During the COVID-19 Pandemic Is Associated with Conventional Health Behaviors Content: BACKGROUND: Social distancing-when people limit close contact with others outside their household-is a primary intervention available to combat the COVID-19 pandemic. The importance of social distancing is unlikely to change until effective treatments or vaccines become widely available. However, relatively little is known about how best to promote social distancing. Applying knowledge from social and behavioral research on conventional health behaviors (e.g., smoking, physical activity) to support public health efforts and research on social distancing is promising, but empirical evidence supporting this approach is needed. PURPOSE: We examined whether one type of social distancing behavior-reduced movement outside the home-was associated with conventional health behaviors. METHOD: We examined the association between GPS-derived movement behavior in 2,858 counties in USA from March 1 to April 7, 2020 and the prevalence of county-level indicators influenced by residents' conventional health behaviors. RESULTS: Changes in movement were associated with conventional health behaviors, and the magnitude of these associations were similar to the associations among the conventional health behaviors. Counties with healthier behaviors-particularly less obesity and greater physical activity-evidenced greater reduction in movement outside the home during the initial phases of the pandemic in the USA. CONCLUSIONS: Social distancing, in the form of reduced movement outside the home, is associated with conventional health behaviors. Existing scientific literature on health behavior and health behavior change can be more confidently used to promote social distancing behaviors during the COVID-19 pandemic.

Title: The Benefits and Costs of Social Distancing in Rich and Poor Countries Content: Social distancing is the primary policy prescription for combating the COVID-19 pandemic, and has been widely adopted in Europe and North America. We estimate the value of disease avoidance using an epidemiological model that projects the spread of COVID-19 across rich and poor countries. Social distancing measures that"flatten the curve"of the disease to bring demand within the capacity of healthcare systems are predicted to save many lives in high-income countries, such that practically any economic cost is worth bearing. These social distancing policies are estimated to be less effective in poor countries with younger populations less susceptible to COVID-19, and more limited healthcare systems, which were overwhelmed before the pandemic. Moreover, social distancing lowers disease risk by limiting people's economic opportunities. Poorer people are less willing to make those economic sacrifices. They place relatively greater value on their livelihood concerns compared to contracting COVID-19. Not only are the epidemiological and economic benefits of social distancing much smaller in poorer countries, such policies may exact a heavy toll on the poorest and most vulnerable. Workers in the informal sector lack the resources and social protections to isolate themselves and sacrifice economic opportunities until the virus passes. By limiting their ability to earn a living, social distancing can lead to an increase in hunger, deprivation, and related mortality and morbidity. Rather than a blanket adoption of social distancing measures, we advocate for the exploration of alternative harm-reduction strategies, including universal mask adoption and increased hygiene measures.

Title: Causal Impact of Masks, Policies, Behavior on Early Covid-19 Pandemic in the U.S Content: This paper evaluates the dynamic impact of various policies adopted by US states on the growth rates of confirmed Covid-19 cases and deaths as well as social distancing behavior measured by Google Mobility Reports, where we take into consideration people's voluntarily behavioral response to new information of transmission risks. Our analysis finds that both policies and information on transmission risks are important determinants of Covid-19 cases and deaths and shows that a change in policies explains a large fraction of observed changes in social distancing behavior. Our counterfactual experiments suggest that nationally mandating face masks for employees on April 1st could have reduced the growth rate of cases and deaths by more than 10 percentage points in late April, and could have led to as much as 17 to 55 percent less deaths nationally by the end of May, which roughly translates into 17 to 55 thousand saved lives. Our estimates imply that removing non-essential business closures (while maintaining school closures, restrictions on movie theaters and restaurants) could have led to -20 to 60 percent more cases and deaths by the end of May. We also find that, without stay-at-home orders, cases would have been larger by 25 to 170 percent, which implies that 0.5 to 3.4 million more Americans could have been infected if stay-at-home orders had not been implemented. Finally, not having implemented any policies could have led to at least a 7 fold increase with an uninformative upper bound in cases (and deaths) by the end of May in the US, with considerable uncertainty over the effects of school closures, which had little cross-sectional variation.

Title: Optimal Control applied to a SEIR model of 2019-nCoV with social distancing Content: Does the implementation of social distancing measures have merit in controlling the spread of the novel coronavirus? In this study, we develop a mathematical model to explore the effect of social distancing on new disease infections. Mathematical analyses of our model indicate that successful eradication of the disease is strongly dependent on the chosen preventive measure. Numerical computations of the model solution demonstrate that the ability to flatten the curve becomes easier as social distancing is strictly enforced. Based on our model, we also formulate an optimal control problem and solve it using Pontryagin's Maximum Principle and an efficient numerical iterative method. Our numerical results of an optimal 2019-nCoV treatment protocol that yields a minimum disease burden from this disease indicates that social distancing is vitally important.

Title: Early Mandated Social Distancing is a Strong Predictor of Reduction in Highest Number of New COVID-19 cases per Day within Various Geographic Regions Content: Mandated social distancing has been globally applied to limit the spread of corona virus disease 2019 (COVID-19) from highly pathogenic severe acute respiratory syndrome (SARS) -associated coronavirus 2 (SARS-CoV-2). The benefit of this community-based intervention in limiting COVID-19 has not been proven nor quantified. We examined the effect of timing of mandated social distancing on the rate of COVID-19 in 119 geographic regions derived from 41 states within United States and 78 countries. We found that highest number of new COVID-19 cases per day per million persons was significantly associated with total number of COVID-19 cases per million persons on the day before mandated social distancing (Beta;=0.66, p<0.0001). Our findings suggest that the initiation of mandated social distancing for each doubling in number of existing COVID-19 cases would result in eventual peak with 58% higher number of COVID-19 infections per day. Subgroup analysis on those regions where the highest number of new COVID-19 cases per day have peaked increased Beta to .85 (p<0.0001). We demonstrate that initiating mandated social distancing at a 10 times smaller number of COVID-19 cases will reduce the number of daily new COVID-19 cases at peak by 80% highlighting the importance of this community-based intervention.

Title: Quantifying the impact of physical distance measures on the transmission of COVID-19 in the UK Content: BACKGROUND: To mitigate and slow the spread of COVID-19, many countries have adopted unprecedented physical distancing policies, including the UK. We evaluate whether these measures might be sufficient to control the epidemic by estimating their impact on the reproduction number (R0, the average number of secondary cases generated per case). METHODS: We asked a representative sample of UK adults about their contact patterns on the previous day. The questionnaire was conducted online via email recruitment and documents the age and location of contacts and a measure of their intimacy (whether physical contact was made or not). In addition, we asked about adherence to different physical distancing measures. The first surveys were sent on Tuesday, 24 March, 1 day after a "lockdown" was implemented across the UK. We compared measured contact patterns during the "lockdown" to patterns of social contact made during a non-epidemic period. By comparing these, we estimated the change in reproduction number as a consequence of the physical distancing measures imposed. We used a meta-analysis of published estimates to inform our estimates of the reproduction number before interventions were put in place. RESULTS: We found a 74% reduction in the average daily number of contacts observed per participant (from 10.8 to 2.8). This would be sufficient to reduce R0 from 2.6 prior to lockdown to 0.62 (95% confidence interval [CI] 0.37-0.89) after the lockdown, based on all types of contact and 0.37 (95% CI = 0.22-0.53) for physical (skin to skin) contacts only. CONCLUSIONS: The physical distancing measures adopted by the UK public have substantially reduced contact levels and will likely lead to a substantial impact and a decline in cases in the coming weeks. However, this projected decline in incidence will not occur immediately as there are significant delays between infection, the onset of symptomatic disease, and hospitalisation, as well as further delays to these events being reported. Tracking behavioural change can give a more rapid assessment of the impact of physical distancing measures than routine epidemiological surveillance.

Title: Mathematical modeling of COVID-19 containment strategies with considerations for limited medical resources Content: The outbreak of SARS-CoV-2 in China has spread around the world, infecting millions and causing governments to implement strict policies to counteract the spread of the disease. One of the most effective strategies in reducing the severity of the pandemic is social distancing, where members of the population systematically reduce their interactions with others to limit the transmission rate of the virus. However, the implementation of social distancing can be difficult and costly, making it imperative that both policy makers and the citizenry understand the potential benefits if done correctly and the risks if not. In this work, a mathematical model is developed to study the effects of social distancing on the spread of the SARS-CoV-2 virus in Canada. The model is based upon a standard epidemiological SEIRD model that has been stratified to directly incorporate the proportion of individuals who are following social distancing protocols. The model parameters characterizing the disease are estimated from current epidemiological data on COVID-19 using machine learning techniques. The results of the model show that social distancing policies in Canada have already saved thousands of lives and that the prolonged adherence to social distancing guidelines could save thousands more. Importantly, our model indicates that social distancing can significantly delay the onset of infection peaks, allowing more time for the production of a vaccine or additional medical resources. Furthermore, our results stress the importance of easing social distancing restrictions gradually, rather than all at once, in order to prevent a second wave of infections. Model results are compared to the current capacity of the Canadian healthcare system by examining the current and future number of ventilators available for use, emphasizing the need for the increased production of additional medical resources.

Title: An analysis of the ethics of lockdown in India Content: Over the past 6 months, coronavirus-induced disease (COVID-19) has spread across 212 countries, affecting millions of people. As it has no known cure, social distancing is highly recommended for prevention of spread of the disease. Here, we have described the impact of the social distancing measures implemented by the Government of India on various sections of the society, especially the vulnerable sections. Furthermore, we have presented an analysis of these measures, according to the World Health Organization´s Guidance for Managing Ethical Issues in Infectious Disease Outbreaks (2016); we have also applied principles, as applicable, from the Indian Council of Medical Research’s National Ethical Guidelines for Biomedical and Health Research Involving Human Participants (2017). Finally, we have presented several measures that should have been adopted before and in addition to implementing the lockdown to improve its effectiveness.

Title: Pandemic, Shutdown and Consumer Spending: Lessons from Scandinavian Policy Responses to COVID-19 Content: This paper uses transaction data from a large bank in Scandinavia to estimate the effect of social distancing laws on consumer spending in the COVID-19 pandemic. The analysis exploits a natural experiment to disentangle the effects of the virus and the laws aiming to contain it: Denmark and Sweden were similarly exposed to the pandemic but only Denmark imposed significant restrictions on social and economic activities. We estimate that aggregate spending dropped by around 25 percent in Sweden and, as a result of the shutdown, by 4 additional percentage points in Denmark. This implies that most of the economic contraction is caused by the virus itself and occurs regardless of social distancing laws. The age gradient in the estimates suggest that social distancing reinforces the virus-induced drop in spending for low health-risk individuals but attenuates it for high-risk individuals by lowering the overall prevalence of the virus in the society.

Title: A simplified model for expected development of the SARS-CoV-2 (Corona) spread in Germany and US after social distancing Content: Widespread opinions and discussion exist regarding the efficiency of social distancing after crucial spread of the SARS-CoV-2 virus during the actual Covid-19 pandemic. While Germany has released a federal law that prohibits any type of direct contact for more than 2 people other countries including the US released curfews. People are now wondering whether these measures are helpful to stop or hamper the Covid-19 pandemic and to limit the spread of the new corona virus. A quantitative statement on this question depends on many parameters that are difficult to grasp mathematically and cannot therefore be made conclusively (they include consistent adherence to the measures decided, the estimated number of unreported cases, the possible limitation by test capacities, possible mutations of the virus, etc ...). However, it turns out that a reduction in the actual daily new infection rate (actual daily growth rate of reported cases, in short: infection rate) from the current value of 30-35% in the US to 10% would be extremely effective in stopping the spread of the virus. The severe restrictions in Germany which closed any public events, schools and universities a week ago might already have contributed to a reduction of the growth rate of reported cases below 30%.

Title: Social Distancing as a Health Behavior: County-Level Movement in the United States During the COVID-19 Pandemic Is Associated with Conventional Health Behaviors Content: BACKGROUND: Social distancing—when people limit close contact with others outside their household—is a primary intervention available to combat the COVID-19 pandemic. The importance of social distancing is unlikely to change until effective treatments or vaccines become widely available. However, relatively little is known about how best to promote social distancing. Applying knowledge from social and behavioral research on conventional health behaviors (e.g., smoking, physical activity) to support public health efforts and research on social distancing is promising, but empirical evidence supporting this approach is needed. PURPOSE: We examined whether one type of social distancing behavior—reduced movement outside the home—was associated with conventional health behaviors. METHOD: We examined the association between GPS-derived movement behavior in 2,858 counties in USA from March 1 to April 7, 2020 and the prevalence of county-level indicators influenced by residents’ conventional health behaviors. RESULTS: Changes in movement were associated with conventional health behaviors, and the magnitude of these associations were similar to the associations among the conventional health behaviors. Counties with healthier behaviors—particularly less obesity and greater physical activity—evidenced greater reduction in movement outside the home during the initial phases of the pandemic in the USA. CONCLUSIONS: Social distancing, in the form of reduced movement outside the home, is associated with conventional health behaviors. Existing scientific literature on health behavior and health behavior change can be more confidently used to promote social distancing behaviors during the COVID-19 pandemic.

Title: A Decision Analytic Approach for Social Distancing Policies During the COVID-19 Pandemic Content: The COVID-19 pandemic has become a crucial public health issue in many countries including the United States. In the absence of the right vaccine strain and sufficient antiviral stockpiles on hand, nonpharmaceutical interventions have become valuable public health tools at the early stages of the pandemic and they are employed by many countries across the globe. These interventions are designed to increase social distancing between individuals to reduce the transmission of the virus and eventually dampen the burden on the healthcare system. The virus transmissibility is a function of the average number of contacts individuals have in their communities and it is highly dependent on population density and daily mobility patterns, along with other social factors. These show significant variation across the United States. In this article, we study the effectiveness of social distancing measures in communities with different population density. Specifically, first we show how the empirical estimation of reproduction number differs for two completely different states, thus the experience of the COVID-19 outbreak is drastically different, suggesting different outbreak growth rates in practice. Second, we develop an age-structured compartmental model for simulating the disease spread to demonstrate the variation in the observed outbreak characteristics. We find that early trigger and late trigger options present a trade-off between the peak magnitude and the overall death toll of the outbreak which may also vary across different populations.

Title: Dynamical model for social distancing in the U.S. during the COVID-19 epidemic Content: Background Social distancing has led to a flattening of the curve in many states across the U.S. This is part of a novel, massive, global social experiment which has served to mitigate the pandemic in the absence of a vaccine or effective anti-viral drugs. Hence it is important to be able to forecast hospitalizations reasonably accurately. Methods We propose on phenomenological grounds a generalized diffusion equation which in- corporates the effect of social distancing to forecast the temporal evolution of the probability of having a given number of hospitalizations. The probability density function is log-normal in the number of hospitalizations, which is useful in describing pandemics where the number of hospital- izations is very high. Findings We used this insight and data to make forecasts for states using Monte Carlo methods. Back testing validates our approach, which yields good results about a week into the future. States are beginning to reopen at the time of publication and our forecasts indicate possible precursors of increased hospitalizations. Additionally we studied the reproducibility Ro in New York (Italian strain) and California (Wuhan strain). We find that even if there is a difference in the transmission of the two strains, social distancing has been able to control the progression of COVID 19. Funding None.

Title: Impact of social distancing during COVID-19 pandemic on crime in Los Angeles and Indianapolis Content: Governments have implemented social distancing measures to address the ongoing COVID-19 pandemic. The measures include instructions that individuals maintain social distance when in public, school closures, limitations on gatherings and business operations, and instructions to remain at home. Social distancing may have an impact on the volume and distribution of crime. Crimes such as residential burglary may decrease as a byproduct of increased guardianship over personal space and property. Crimes such as domestic violence may increase because of extended periods of contact between potential offenders and victims. Understanding the impact of social distancing on crime is critical for ensuring the safety of police and government capacity to deal with the evolving crisis. Understanding how social distancing policies impact crime may also provide insights into whether people are complying with public health measures. Examination of the most recently available data from both Los Angeles, CA, and Indianapolis, IN, shows that social distancing has had a statistically significant impact on a few specific crime types. However, the overall effect is notably less than might be expected given the scale of the disruption to social and economic life.