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Ivf4OF0X2I | N0qlvDjnEv | KDD.org/2023/Workshop/epiDAMIK/Paper7/-/Official_Review | {"title": "This paper proposed a risk-based ring vaccination method that achieves better performance than the existing no-prioritization ring method and random method.", "review": "This paper proposed a risk-based ring vaccination method that achieves better performance than the existing no-prioritization ring method and random method.\n\nStrength:\n1. Good motivation: The idea of risk-based vaccination allows more effective vaccine distribution.\n2. The experience showcases the effectiveness of the proposed risk-based ring vaccination method.\n\nWeakness:\n1. Only one experiment setup is used in experiments for evaluation. Another experiment for other diseases, or at least one other model, is more useful to better showcase the proposed method.\n2. The vaccine budget (50/100/200) seems a little random. A better budget based on real-world Ebola vaccine production rate is more useful to showcase the effectiveness of the proposed method in the application", "rating": "4: Good paper, accept", "confidence": "5: The reviewer is absolutely certain that the evaluation is correct and very familiar with the relevant literature"} | review | 2023 | KDD.org/2023/Workshop/epiDAMIK | Risk-Based Ring Vaccination: A Strategy for Pandemic Control and Vaccine Allocation | ["Dinh Song An Nguyen", "Marie-Laure Charpignon", "Kathryn L Schaber", "Maimuna S. Majumder", "Andrew Perrault"] | Throughout an infectious disease crisis, resources that can be used to slow and prevent spread are often scarce or expensive. Designing control policies to optimally allocate these resources to maximize objectives is challenging. Here, we study the case of ring vaccination, a strategy that is used to control the spread of infection by vaccinating the contacts of identified infected individuals and their contacts of contacts. Using agent-based modeling to simulate an Ebola outbreak, we introduce a risk-based ring vaccination strategy in which individuals in a ring are prioritized based on their relative infection risks. Assuming the risk of transmission by contact type is known and a fixed supply of vaccine doses is available on each day, we compared this strategy to ring vaccination without prioritization and randomized vaccination. We find that risk-based ring vaccination offers a substantial advantage over standard ring vaccination when the number of doses are limited, including reducing the daily infected count and death count, and shifting the pandemic peak by a considerable amount of time. We believe that control policies based on estimated risk can often offer significant benefits without increasing the burden of administering the policy by an unacceptable amount. | ["agent-based modeling", "ring vaccination", "Ebola", "public health"] | https://openreview.net/forum?id=N0qlvDjnEv | https://openreview.net/pdf?id=N0qlvDjnEv | https://openreview.net/forum?id=N0qlvDjnEv¬eId=Ivf4OF0X2I |
xdAE5_pGjn | N0qlvDjnEv | KDD.org/2023/Workshop/epiDAMIK/Paper7/-/Official_Review | {"title": "Risk-based ring vaccination appears promising. Need more analysis to test robustness (to demographic patterns) and generality (to other infectious diseases)", "review": "The paper explores preliminary work for risk-based ring vaccination as an intervention to control spread of infectious diseases given limited resources. Authors consider the specific case study of Ebola vaccination and compare with multiple protocols: random-vaccination, no-prioritization ring vaccination, full ring vaccination. Simulations show that risk-based ring vaccination is shown to be promising to achieve full-ring benefits with significantly fewer resources. However, significantly more experiments are need to make strong and reliable inferences. \n\nSome comments/questions for the authors to think about:\n1. Risk-based ring vaccination seems very sensitive to mobility patterns and presence of NPIs (like lockdowns). Does this only work when communities are sparse and isolated or with active mobility patterns. How do you form a ring then? Authors should simulate with real-scale populations, with dynamic movement patterns and calibrate with real-world data sources before making interventional claims. How was this model calibrated?\n2. Compare with other non-ring based resource-limited vaccination strategies. For instance, during COVID-19: some govts delayed 2nd dose of the COVID-19 vaccine to prioritize first doses; and prioritized high-risk age groups when the supply was limited, such as [1]. Is risk-based ring vaccination better than these methods? Maybe interesting to study in the next paper.\n3. Does risk-based ring vaccination also generalize to other infections like COVID-19/Flu which spread at mass scale or is only good when infectious are more localized to smaller communities, like Ebola. Would be important to analyze and clarify this distinction.\n4. Finally, a \"somewhat\" similar concept explored in 106 canada neighborhoods during COVID-19 alpha variant, as in [2]. With the alpha variant, most infections were with <18 yr olds but vaccines were not authorized yet. So, authorities vaccinated parents of children at greater risk from COVID-19 since vaccine was not authorized for children yet. Is this a form of risk-based ring vaccination? The idea is very intuitive, so i am curious to know if such risk-based rings have been explored previously.\n\nI would encourage the authors to think about some of these concerns, if they are selected to present at the workshop. I am also okay if the paper is accepted since it is a non-archival workshop and would make for good discussion.\n\n[1]: https://www.bmj.com/content/373/bmj.n1087\n[2]: https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2788978", "rating": "2: Marginally below acceptance threshold", "confidence": "4: The reviewer is confident but not absolutely certain that the evaluation is correct"} | review | 2023 | KDD.org/2023/Workshop/epiDAMIK | Risk-Based Ring Vaccination: A Strategy for Pandemic Control and Vaccine Allocation | ["Dinh Song An Nguyen", "Marie-Laure Charpignon", "Kathryn L Schaber", "Maimuna S. Majumder", "Andrew Perrault"] | Throughout an infectious disease crisis, resources that can be used to slow and prevent spread are often scarce or expensive. Designing control policies to optimally allocate these resources to maximize objectives is challenging. Here, we study the case of ring vaccination, a strategy that is used to control the spread of infection by vaccinating the contacts of identified infected individuals and their contacts of contacts. Using agent-based modeling to simulate an Ebola outbreak, we introduce a risk-based ring vaccination strategy in which individuals in a ring are prioritized based on their relative infection risks. Assuming the risk of transmission by contact type is known and a fixed supply of vaccine doses is available on each day, we compared this strategy to ring vaccination without prioritization and randomized vaccination. We find that risk-based ring vaccination offers a substantial advantage over standard ring vaccination when the number of doses are limited, including reducing the daily infected count and death count, and shifting the pandemic peak by a considerable amount of time. We believe that control policies based on estimated risk can often offer significant benefits without increasing the burden of administering the policy by an unacceptable amount. | ["agent-based modeling", "ring vaccination", "Ebola", "public health"] | https://openreview.net/forum?id=N0qlvDjnEv | https://openreview.net/pdf?id=N0qlvDjnEv | https://openreview.net/forum?id=N0qlvDjnEv¬eId=xdAE5_pGjn |
2Bqw6JYNKG | Ql4CuaB3-D | KDD.org/2023/Workshop/epiDAMIK/Paper9/-/Official_Review | {"title": "Interesting problem.. parts of approach unclear", "review": "The paper aims to learn contact tracing policies by bridging SL and RL. In implementation, a CNN is used to estimate the probability of infectiousness for each individual in a cluster, and this output, along with cluster-wide statistics, serves as the state for the RL agent which learns a cluster-level lockdown policy. \nBriefly, first data is sampled from the simulator under arbitrary control policies to predict infectiousness of agents. Once predictor is trained, how the outputs are used to define state of the RL agent to learn cluster-level policies. The cluster sizes are from 8 to 32. The policy is learned over a space of 5 discrete actions over an objective to minimize (number of transmission days + non-effective quarantine + costs).\n\nThe problem of interest is very impact and the idea of using RL to learn NPI policies is exciting. However, the current formulation and assumptions seems a bit unrealistic and results are also not very encouraging. I would suggest authors to revisit the experiment design and then resubmit a manuscript. \n\nSome comments and questions to think about:\n1. The SL training setup seems highly unrealistic since it uses ground truth not available in the real world. How can the exact infection probability of an individual estimated for ground truth? How will this approach generalize? Even results in Table 5 mean that \" SL outputs could be mis-calibrated\". Also, it is less intuitive to learn input state parameters for an RL agent when data can be approximated from the the environment (since was used for SL ground-truth)?\n2. Effect of decisions on cluster sizes will depend on their relative scale w.r.t the size of the total population. if clusters are as small as 8, 16 or 32 people -- it will be very tough to observe distinction between individual people and clusters. To make claims for real policy decision making: clusters should at-least be a census block [or county] and the simulation should analyze how these variables change with scale and mobility across clusters. What is the size of the total population considered? This was not evident from experiments. \n\nI think these are sensitive problems with far-reaching implications. More research needs to be done before claims are put out into the world.", "rating": "2: Marginally below acceptance threshold", "confidence": "4: The reviewer is confident but not absolutely certain that the evaluation is correct"} | review | 2023 | KDD.org/2023/Workshop/epiDAMIK | Using Reinforcement Learning for Multi-Objective Cluster-Level NPI Optimization | ["Xueqiao Peng", "Jiaqi Xu", "Xi Chen", "Dinh Song An Nguyen", "Andrew Perrault"] | Non-pharmaceutical interventions (NPIs) play a critical role in the defense against emerging pathogens. Among these interventions, familiar measures such as travel bans, event cancellations, social distancing, curfews, and lockdowns have become integral components of our response strategy. Contact tracing is especially widely adopted. However, the optimization of contact tracing involves navigating various trade-offs, including the simultaneous goals of minimizing virus transmission and reducing costs. Reinforcement learning (RL) techniques provides a promising avenue to model intricate decision-making processes and optimize policies to achieve specific objectives, but even modern deep RL techniques struggle in the high dimensional partially observable problem setting presented by contact tracing. We propose a novel RL approach to optimize a multi-objective infectious disease control policy that combines supervised learning with RL, allowing us to capitalize on the strengths of both techniques. Through extensive experimentation and evaluation, we show that our optimized policy surpasses the performance of five benchmark policies. | ["reinforcement", "npi optimization", "interventions", "contact", "npis", "critical role", "defense", "pathogens", "familiar measures"] | https://openreview.net/forum?id=Ql4CuaB3-D | https://openreview.net/pdf?id=Ql4CuaB3-D | https://openreview.net/forum?id=Ql4CuaB3-D¬eId=2Bqw6JYNKG |
9xxjrp7gXOo | Ql4CuaB3-D | KDD.org/2023/Workshop/epiDAMIK/Paper9/-/Official_Review | {"title": "Review", "review": "### Summary\n\nThis work proposes to use reinforcement learning for optimizing multi-objective infectious disease control policy in a branching process environment. In the approach, this paper uses a convolutional neural network to estimate the probability of infectiousness for each individual in a cluster and use the outputs as the state of the RL agent. This work evaluates the proposed approach in a branching process simulated for SARS-CoV-2 and compares the approach with baseline policies. The baselines include thresholding, Symptom-Based Quarantine, 14-Day Quarantine, and no quarantine. The results show that the proposed approach achieves higher objective values than the baselines across multiple parameter settings.\n\n### Weaknesses\n\n- The environment setup needs to be further explained. For example, it would be better to provide formal definitions of the branching process environment, including the states and necessary parameters. Moreover, the example illustrated in Figure 2 is confusing. For example, it would be better to explain what factors cause the state changes in different clusters.\n- Further discussion and comparison with related work need to be incorporated. It would be better to provide a more detailed discussion with related work, especially previous decision-making methods or RL methods for optimizing intervention policy. For example, the related work of RLGN [1]. It would be better to compare such methods in the experiments. Moreover, the motivation for using branching processes and cluster-based view needs to be further elaborated. \n- More details in the experiments need to be included. For example, the detailed setup of the branching processes for SARS-CoV-2 and its hyper-parameter settings and the details of how training examples are generated. Including such details help better interpret the results of the comparison. \n\n[1] Eli Meirom, Haggai Maron, Shie Mannor, and Gal Chechik. 2021. Controlling graph dynamics with reinforcement learning and graph neural networks. In International Conference on Machine Learning. PMLR, 7565\u20137577\n\n", "rating": "3: Marginally above acceptance threshold", "confidence": "4: The reviewer is confident but not absolutely certain that the evaluation is correct"} | review | 2023 | KDD.org/2023/Workshop/epiDAMIK | Using Reinforcement Learning for Multi-Objective Cluster-Level NPI Optimization | ["Xueqiao Peng", "Jiaqi Xu", "Xi Chen", "Dinh Song An Nguyen", "Andrew Perrault"] | Non-pharmaceutical interventions (NPIs) play a critical role in the defense against emerging pathogens. Among these interventions, familiar measures such as travel bans, event cancellations, social distancing, curfews, and lockdowns have become integral components of our response strategy. Contact tracing is especially widely adopted. However, the optimization of contact tracing involves navigating various trade-offs, including the simultaneous goals of minimizing virus transmission and reducing costs. Reinforcement learning (RL) techniques provides a promising avenue to model intricate decision-making processes and optimize policies to achieve specific objectives, but even modern deep RL techniques struggle in the high dimensional partially observable problem setting presented by contact tracing. We propose a novel RL approach to optimize a multi-objective infectious disease control policy that combines supervised learning with RL, allowing us to capitalize on the strengths of both techniques. Through extensive experimentation and evaluation, we show that our optimized policy surpasses the performance of five benchmark policies. | ["reinforcement", "npi optimization", "interventions", "contact", "npis", "critical role", "defense", "pathogens", "familiar measures"] | https://openreview.net/forum?id=Ql4CuaB3-D | https://openreview.net/pdf?id=Ql4CuaB3-D | https://openreview.net/forum?id=Ql4CuaB3-D¬eId=9xxjrp7gXOo |
YsYOhfnQ7D | Ql4CuaB3-D | KDD.org/2023/Workshop/epiDAMIK/Paper9/-/Official_Review | {"title": "Good paper with compelling results, could benefit from more intuition for methodological choices", "review": "This paper describes a novel RL approach to optimizing infection disease control policy. The proposed method combines supervised learning with RL and shows strong performance compared to baseline policies. \n\nPositives: \n+ The branching-process formulation is well-described and intuitive\n+ The need for an estimate of the probability of infection is well-motivated \n+ The experiments are extensive and clearly show the strengths (and limitations) of the proposed RLSL framework \n+ Overall, the paper is clear and easy to follow\n\n\nPlaces for Improvements \n- The intuition of the representation and state space for both the SL and RL settings could be improved. Currently, it seems as though many representations were tested and this one was eventually chosen. Were they tested on validation data? More information about how these representations are necessary\n- Why was PPO chosen for the RL policy? Were other RL techniques considered? This choice could benefit from more justification\n- How hyperparameters were chosen should be discussed more. Currently, it almost seems as though the architecture and hyperparameters for the 2D CNN were chosen based on the same set in which policies were evaluated, which would be problematic \n- As mentioned in the paper, calibration of the SL estimates is critical for the threshold-based approach. The authors should consider calibrating these probabilities and evaluating the calibration error in some way, to see if it can improve all methods, especially the threshold-based baseline ", "rating": "4: Good paper, accept", "confidence": "4: The reviewer is confident but not absolutely certain that the evaluation is correct"} | review | 2023 | KDD.org/2023/Workshop/epiDAMIK | Using Reinforcement Learning for Multi-Objective Cluster-Level NPI Optimization | ["Xueqiao Peng", "Jiaqi Xu", "Xi Chen", "Dinh Song An Nguyen", "Andrew Perrault"] | Non-pharmaceutical interventions (NPIs) play a critical role in the defense against emerging pathogens. Among these interventions, familiar measures such as travel bans, event cancellations, social distancing, curfews, and lockdowns have become integral components of our response strategy. Contact tracing is especially widely adopted. However, the optimization of contact tracing involves navigating various trade-offs, including the simultaneous goals of minimizing virus transmission and reducing costs. Reinforcement learning (RL) techniques provides a promising avenue to model intricate decision-making processes and optimize policies to achieve specific objectives, but even modern deep RL techniques struggle in the high dimensional partially observable problem setting presented by contact tracing. We propose a novel RL approach to optimize a multi-objective infectious disease control policy that combines supervised learning with RL, allowing us to capitalize on the strengths of both techniques. Through extensive experimentation and evaluation, we show that our optimized policy surpasses the performance of five benchmark policies. | ["reinforcement", "npi optimization", "interventions", "contact", "npis", "critical role", "defense", "pathogens", "familiar measures"] | https://openreview.net/forum?id=Ql4CuaB3-D | https://openreview.net/pdf?id=Ql4CuaB3-D | https://openreview.net/forum?id=Ql4CuaB3-D¬eId=YsYOhfnQ7D |
F8k2r_jshnG | fhxHhXTnHc | KDD.org/2023/Workshop/epiDAMIK/Paper5/-/Official_Review | {"title": "Detecting vaccine intent from user search behavior", "review": "The authors study the problem of detecting vaccine intent from Bing search query log data. Briefly (as I understand their method) their goal is to take a query + click graph and label it with whether it represents vaccine intent or not and then use the results of this classification to estimate the number of vaccines that will be administered in a particular zip code tabulation area. To do so, the authors use Mechanical Turk to label an initial set of query-URL click pairs and then apply semi-supervised learning techniques to grow this set of labels. Pretraining in the form of initializing the model to minimize an auxiliary loss is applied to states with less data. The resulting classifier is evaluated to be highly effective at detecting vaccine intent. Then, a bias correction is performed to go from Bing user counts to population counts, as the usage of Bing is not uniform across states. The estimates the authors develop are highly correlated with CDC-reported vaccine counts, but more granular and do not have a reporting delay.\n\nThe paper is of high quality, generally clear, makes methodological innovations, and likely to be of wide interest.\n\nMinor comments:\n- Section 3 para 1\u2014fairly important to include the precise criteria for inclusion (at least in Appendix)\n- Giving some overview of the challenge of detecting intent from queries would be helpful for those who have not worked with this kind of data before. For example, in 3.1, the phrase \u201ccovid vaccine New York\u201d is mentioned as suggestive but not unambiguous enough. But it is not clear what is missing from this. Is it that the location named is not specific enough? Or is covid vaccine + location always too ambiguous?\n- How were URLs presented to the annotators? Did they see just the URL or did they see the page it led to?\n\nThings that came to mind:\n- Accuracy of intent classification across time\u2014I believe this is not reported anywhere. This is a pretty important question given the Google Flu Trends experience.\n- Connect vaccine intent queries to queries about symptoms, e.g., does experiencing symptoms motivate people to seek vaccine information?", "rating": "5: Top 50% of accepted papers, clear accept", "confidence": "4: The reviewer is confident but not absolutely certain that the evaluation is correct"} | review | 2023 | KDD.org/2023/Workshop/epiDAMIK | Accurate Measures of Vaccination and Concerns of Vaccine Holdouts from Web Search Logs | ["Serina Chang", "Adam Fourney", "Eric Horvitz"] | To design effective vaccine policies, policymakers need detailed data about who has been vaccinated, who is holding out, and why. However, existing data in the US are insufficient: reported vaccination rates are often delayed or missing, and surveys of vaccine hesitancy are limited by high-level questions and self-report biases. Here, we show how large-scale search engine logs and machine learning can be leveraged to fill these gaps and provide novel insights about vaccine intentions and behaviors. First, we develop a vaccine intent classifier that can accurately detect when a user is seeking the COVID-19 vaccine on search. Our classifier demonstrates strong agreement with CDC vaccination rates, with correlations above 0.86, and estimates vaccine intent rates to the level of ZIP codes in real time, allowing us to pinpoint more granular trends in vaccine seeking across regions, demographics, and time. To investigate vaccine hesitancy, we use our classifier to identify two groups, vaccine early adopters and vaccine holdouts. We find that holdouts, compared to early adopters matched on covariates, are 69% more likely to click on untrusted news sites. Furthermore, we organize 25,000 vaccine-related URLs into a hierarchical ontology of vaccine concerns, and we find that holdouts are far more concerned about vaccine requirements, vaccine development and approval, and vaccine myths, and even within holdouts, concerns vary significantly across demographic groups. Finally, we explore the temporal dynamics of vaccine concerns and vaccine seeking, and find that key indicators emerge when individuals convert from holding out to preparing to accept the vaccine. | ["COVID-19", "vaccination", "health behaviors", "misinformation", "search logs", "graph machine learning"] | https://openreview.net/forum?id=fhxHhXTnHc | https://openreview.net/pdf?id=fhxHhXTnHc | https://openreview.net/forum?id=fhxHhXTnHc¬eId=F8k2r_jshnG |
kTKHFRaH2I | fhxHhXTnHc | KDD.org/2023/Workshop/epiDAMIK/Paper5/-/Official_Review | {"title": "Measuring vaccine intent using web search data", "review": "The main contribution of this paper is a COVID-19 vaccine-intent classifier that can potentially give an accurate measure of vaccine hesitancy in an individual by analyzing the search history. This classifier is trained on search queries and website clicks from Bing search logs and annotated using Amazon Mechanical Turk. \n\nAnother contribution is an ontology of website URLs which consists of 25,000 vaccine-related URLs, organized into eight top categories to 36 subcategories to 156 low-level URL clusters. They combine this ontology with their vaccine-intent classifier and got improved performance.\n\nThe classifier correlates with the CDC vaccination data in the sense that states having high vaccination rates have low vaccine-hesitancy and states having low vaccination rates have high vaccine-hesitancy.\n\nOne weakness is that they cap their analysis till August 2021, since the FDA approved booster shots in September and their method cannot distinguish between vaccine seeking for the primary series versus boosters. But it still would have been interesting to see how the classifier performs beyond August 2021. Also it is not clear how this method will perform with other vaccines that are not as popular as the COVID-19 vaccine.\n\nBut overall the contribution is nice and I think it should be accepted.", "rating": "4: Good paper, accept", "confidence": "3: The reviewer is fairly confident that the evaluation is correct"} | review | 2023 | KDD.org/2023/Workshop/epiDAMIK | Accurate Measures of Vaccination and Concerns of Vaccine Holdouts from Web Search Logs | ["Serina Chang", "Adam Fourney", "Eric Horvitz"] | To design effective vaccine policies, policymakers need detailed data about who has been vaccinated, who is holding out, and why. However, existing data in the US are insufficient: reported vaccination rates are often delayed or missing, and surveys of vaccine hesitancy are limited by high-level questions and self-report biases. Here, we show how large-scale search engine logs and machine learning can be leveraged to fill these gaps and provide novel insights about vaccine intentions and behaviors. First, we develop a vaccine intent classifier that can accurately detect when a user is seeking the COVID-19 vaccine on search. Our classifier demonstrates strong agreement with CDC vaccination rates, with correlations above 0.86, and estimates vaccine intent rates to the level of ZIP codes in real time, allowing us to pinpoint more granular trends in vaccine seeking across regions, demographics, and time. To investigate vaccine hesitancy, we use our classifier to identify two groups, vaccine early adopters and vaccine holdouts. We find that holdouts, compared to early adopters matched on covariates, are 69% more likely to click on untrusted news sites. Furthermore, we organize 25,000 vaccine-related URLs into a hierarchical ontology of vaccine concerns, and we find that holdouts are far more concerned about vaccine requirements, vaccine development and approval, and vaccine myths, and even within holdouts, concerns vary significantly across demographic groups. Finally, we explore the temporal dynamics of vaccine concerns and vaccine seeking, and find that key indicators emerge when individuals convert from holding out to preparing to accept the vaccine. | ["COVID-19", "vaccination", "health behaviors", "misinformation", "search logs", "graph machine learning"] | https://openreview.net/forum?id=fhxHhXTnHc | https://openreview.net/pdf?id=fhxHhXTnHc | https://openreview.net/forum?id=fhxHhXTnHc¬eId=kTKHFRaH2I |
ZvkFh9VHIQ | fhxHhXTnHc | KDD.org/2023/Workshop/epiDAMIK/Paper5/-/Official_Review | {"title": "The authors did an in-depth analysis of the search logs related to the vaccines to detect an individual\u2019s vaccine intent and further discovered insights on the behavioral difference of (i) early vaccine adaptors and (ii) vaccine-resistant groups. Overall, the paper is well written, is original, and would help the community to understand behavioral patterns from the web search logs.", "review": "Summary (Long)\n- The authors did an in-depth analysis of the search logs related to the vaccines to detect an individual\u2019s vaccine intent and further discovered insights on the behavioral difference of (i) early vaccine adaptors and (ii) vaccine-resistant groups. Their pipeline of the vaccine intent classifier includes finding top candidates for user URLs, using personalized PageRank, followed by annotation via crowdsourcing, and expanding URLs via GNNs. They also prepared an ontology of vaccine concerns by applying a community detection algorithm. Though some of the decisions of model choices are not well justified, overall, the paper is well written, is original, and would help the community to understand behavioral patterns from the web search logs.\n\nStrong points (Pros)\n- Overall, their method could fill the gaps in understanding individual vaccine intentions and behaviors through web search logs.\n- Their vaccine intention classifier design is well-motivated, easy to follow, and performs well at 0.9 AUC.\n- Authors did an in-depth study on this problem and provided enough details and additional analyses in the appendix. \n\nWeak points (Cons)\n- The evaluation of their vaccine intention classifier is insufficient, especially because their model is not compared with other baseline methods. If there are no direct methods to evaluate, the authors should do some literature review on somewhat relevant papers that uses search logs in predictive modeling and have those as a set of baselines to compare the performance of the method.\n- Design decisions of their modeling are often not justified. E.g., in section 3.1, the authors chose to use personalized page rank as it is a common technique for seed expansion methods. In fact, seed set expansion itself is a well-studied problem, and there exist many more methods developed for this problem in the past decade. I\u2019d suggest authors review state-of-the-art methods in the seed set expansion problem and explore some other methods in their pipeline. Some examples are:\n - Whang, Joyce Jiyoung, David F. Gleich, and Inderjit S. Dhillon. \"Overlapping community detection using seed set expansion.\" Proceedings of the 22nd ACM international conference on Information & Knowledge Management. 2013.\n - Li Y, He K, Bindel D, Hopcroft JE. Uncovering the small community structure in large networks: A local spectral approach. In Proceedings of the 24th international conference on world wide web 2015 May 18 (pp. 658-668).\n- Authors claim that vaccine concerns differ significantly within holdouts. If this is true, I am worried that the performance of the \u2018binary classifier,\u2019 the vaccine intention classifier, may be suboptimal because there could be a large variance in those in holdouts. In such cases treating the problem as clustering and finding the clusters of holdouts with similar vaccine concerns may make more sense. \n\nMinor comments\n- In the abstract, please provide some details about your claims. E.g. the first claim is \u2018vaccine intent classifier that can accurately detect \u2026\u2019 \u2013 here, please provide how accurate it was. Also, in the abstract \u2018\u2026 find that key indicators emerge\u2026\u2019 \u2013 please list the indicators (maybe provide the most important ones).\n- The captions for the tables should be placed on the top of the table, not below the table.\n- Please justify the usage of CNNs for capturing textual information in the queries and URLs.\n- Please justify using the Louvain algorithm for the community detection problem in section 5. \n- There's a typo in section 3.1 . Please change S-PRR to S-PPR\n", "rating": "5: Top 50% of accepted papers, clear accept", "confidence": "5: The reviewer is absolutely certain that the evaluation is correct and very familiar with the relevant literature"} | review | 2023 | KDD.org/2023/Workshop/epiDAMIK | Accurate Measures of Vaccination and Concerns of Vaccine Holdouts from Web Search Logs | ["Serina Chang", "Adam Fourney", "Eric Horvitz"] | To design effective vaccine policies, policymakers need detailed data about who has been vaccinated, who is holding out, and why. However, existing data in the US are insufficient: reported vaccination rates are often delayed or missing, and surveys of vaccine hesitancy are limited by high-level questions and self-report biases. Here, we show how large-scale search engine logs and machine learning can be leveraged to fill these gaps and provide novel insights about vaccine intentions and behaviors. First, we develop a vaccine intent classifier that can accurately detect when a user is seeking the COVID-19 vaccine on search. Our classifier demonstrates strong agreement with CDC vaccination rates, with correlations above 0.86, and estimates vaccine intent rates to the level of ZIP codes in real time, allowing us to pinpoint more granular trends in vaccine seeking across regions, demographics, and time. To investigate vaccine hesitancy, we use our classifier to identify two groups, vaccine early adopters and vaccine holdouts. We find that holdouts, compared to early adopters matched on covariates, are 69% more likely to click on untrusted news sites. Furthermore, we organize 25,000 vaccine-related URLs into a hierarchical ontology of vaccine concerns, and we find that holdouts are far more concerned about vaccine requirements, vaccine development and approval, and vaccine myths, and even within holdouts, concerns vary significantly across demographic groups. Finally, we explore the temporal dynamics of vaccine concerns and vaccine seeking, and find that key indicators emerge when individuals convert from holding out to preparing to accept the vaccine. | ["COVID-19", "vaccination", "health behaviors", "misinformation", "search logs", "graph machine learning"] | https://openreview.net/forum?id=fhxHhXTnHc | https://openreview.net/pdf?id=fhxHhXTnHc | https://openreview.net/forum?id=fhxHhXTnHc¬eId=ZvkFh9VHIQ |
170KJ-xfkY | fhxHhXTnHc | KDD.org/2023/Workshop/epiDAMIK/Paper5/-/Official_Review | {"title": "Very well-motivated problem; well-designed computational study of health policy ", "review": "The paper proposes and implements a framework for fine-grained estimation of vaccination rates across geographical locations, vaccine holdouts, and the behavior of vaccine holdouts over time. The authors leverage a combination of search engine query data, aggregate vaccination rates, census data, and news reliability ratings (i.e., Newsguard) for their method. This is a particularly challenging problem due lags in vaccination reporting and self-reporting biases, especially among holdouts. The authors demonstrate that their vaccine intent classifier performs well and correlates with CDC vaccination rates, and conduct a fine-grained analysis of concerns among vaccine holdouts over time. \n\nThe real-world impact and applicability of this paper is obvious to me. The authors select a very topical and compelling area (COVID-19 vaccination hesitancy) as well. Although my experience is primarily computational, the results seem grounded in vaccine policymaking objectives/priorities as well. This work further provides a template that could be potentially adapted to other policy rollouts both retrospectively (e.g., ACA rollout) and the future, provided that the requisite data sources are available.\n\nThe comparison of query data between different sources (Bing vs. Google) also addressed my biggest concern \u2014 i.e., how representative is the population studied. I also found the breakdowns of vaccine intent by demographic to be very compelling (Fig. 6c, and A5).\n\nA few questions about the method:\n* Since there are so many steps, the pipeline for generating vaccine intent labels seems susceptible to error propagation (i.e., if there is a systematic bias in the human annotators, or earlier in the pipeline) since it depends on the quality of data collected\u2014\u00a0what checks, in addition to those mentioned in the paper (some human evaluation & comparison of Google vs. Bing query data), were done for systematic biases/other pitfalls at each stage of the pipeline? \n* It is slightly unclear to me how negative vaccine intent examples were labeled. Is this based on the human annotation method in Sec. 3.2 (i.e., <3 positive annotations), followed by GNN-based label-propagation + spies? What if we label vaccine intent using a simple majority vote method (i.e., 2-1 is sufficient) at the human annotator phase? Are queries that have nothing to do with COVID-19 or vaccinations ever included as negative examples?\n\n\u2028Some further questions about the results:\n* In Fig. 6a, some counties are shown in white. Is this because the sample size is too small to generate an estimate of vaccine intent?\nThe authors choose Newsguard as a provider of news reliability ratings; however, such ratings are inherently dependent on the rating provider\u2019s specific methodology (i.e., who decides who is more reliable in an increasingly polarized news environment). Are there alternate providers of trust ratings, and are the results robust to such changes?\n* How were the URL clusters validated? How was model selection (i.e., Louvain over LDA) performed? What is the definition of a \u201cremarkably coherent cluster?\u201d While all of the results look believable, I would have liked to see some measurement of cluster quality here (although this is difficult to do objectively) in addition to the qualitative analysis. Or, is there a human-annotator based way to partially validate these clusters?\n* I don\u2019t know that \u201cHoldouts appear like early adopters\u201d is the correct framing towards the end of Sec. 5 \u2014 I would expect 7d to look much flatter (vertically) if that were the case, which is true for a few of the bars, but instead I mostly notice the reversal. So it seems like the correct conclusion is that some holdouts\u2019 concerns dramatically shift w.r.t. early adopters at some point, while others converge towards early adopters\u2019 concerns. The reversal trend is probably the most interesting piece in my opinion. \n\nAdditional breakdowns of the results that I would find interesting:\n* Stratification by area deprivation index, tribal vs. non-tribal, rural vs. urban (Pop/sq. m. is a proxy), access to healthcare (e.g., # of pharmacies offering the vaccine per capita/within 1h) \n\nI also wanted to raise a potential ethical consideration for future work \u2014 due to the cross-platform aggregation of data required, the potential for privacy violations due to invasive behavioral interventions or discrimination should be considered in my opinion \u2014 for example, targeting specific users for misinformation, vaccine providers/pharmacies engaging in implicit adverse selection by targeting specific segments, or discriminatory labor practices based on vaccine status. One could replace the word \u201cvaccine\u201d with \u201chealth\u201d for similar studies on health policy as well. \n\nSince this study largely consists of retrospective data analysis, the risk to users\u2019 privacy is very small at this stage. While I think the authors exercised due diligence in data ethics via IRB approval, anonymization, dissociation from specific user accounts/profiles, ZIP-level granularity, and ensuring no linkage to other products is possible, I am wondering about the potential for actors that do not exercise the same standards of diligence as the authors to harm users\u2019 privacy. I.e., could a bad actor copy this code and engage in behavioral interventions/discriminatory practices, and what safeguards, computational, legal, or otherwise, exist to mitigate any such threats? \n\nOverall, I think the authors did develop a rigorous and well-motivated method for classifying vaccine intent via a multi-stage pipeline featuring regex queries, URL identification via a combination of PPR, human annotation, a GNN, and the Spy technique from PU learning. The fine-grained analysis of the model's predictions then provide insights into vaccine hesitancy rates, and how concerns of vaccine holdouts change over time. I find that this is already a well-motivated, clear, and well-written computational study of vaccination policy, and addressing the above would simply strengthen the work further in my opinion.", "rating": "5: Top 50% of accepted papers, clear accept", "confidence": "4: The reviewer is confident but not absolutely certain that the evaluation is correct"} | review | 2023 | KDD.org/2023/Workshop/epiDAMIK | Accurate Measures of Vaccination and Concerns of Vaccine Holdouts from Web Search Logs | ["Serina Chang", "Adam Fourney", "Eric Horvitz"] | To design effective vaccine policies, policymakers need detailed data about who has been vaccinated, who is holding out, and why. However, existing data in the US are insufficient: reported vaccination rates are often delayed or missing, and surveys of vaccine hesitancy are limited by high-level questions and self-report biases. Here, we show how large-scale search engine logs and machine learning can be leveraged to fill these gaps and provide novel insights about vaccine intentions and behaviors. First, we develop a vaccine intent classifier that can accurately detect when a user is seeking the COVID-19 vaccine on search. Our classifier demonstrates strong agreement with CDC vaccination rates, with correlations above 0.86, and estimates vaccine intent rates to the level of ZIP codes in real time, allowing us to pinpoint more granular trends in vaccine seeking across regions, demographics, and time. To investigate vaccine hesitancy, we use our classifier to identify two groups, vaccine early adopters and vaccine holdouts. We find that holdouts, compared to early adopters matched on covariates, are 69% more likely to click on untrusted news sites. Furthermore, we organize 25,000 vaccine-related URLs into a hierarchical ontology of vaccine concerns, and we find that holdouts are far more concerned about vaccine requirements, vaccine development and approval, and vaccine myths, and even within holdouts, concerns vary significantly across demographic groups. Finally, we explore the temporal dynamics of vaccine concerns and vaccine seeking, and find that key indicators emerge when individuals convert from holding out to preparing to accept the vaccine. | ["COVID-19", "vaccination", "health behaviors", "misinformation", "search logs", "graph machine learning"] | https://openreview.net/forum?id=fhxHhXTnHc | https://openreview.net/pdf?id=fhxHhXTnHc | https://openreview.net/forum?id=fhxHhXTnHc¬eId=170KJ-xfkY |
cqVYZkoZHY | PhAOtEHLo1 | KDD.org/2023/Workshop/epiDAMIK/Paper3/-/Official_Review | {"title": "Consistent Comparison of Symptom-based Methods for COVID-19 Infection Detection", "review": "This paper compares the accuracy of many methods that detect COVID-19-positive cases. Most of these methods either propose simple rules or build machine learning models that determine a COVID-19-positive case based on certain individual attributes.\n\nIt is not entirely clear but I believe the authors train the ML based models on the same dataset (UMD-CTIS Survey) by splitting it randomly and using 80% for training and 20% for evaluating the performance. I do not understand exactly why this particular method of comparison is desirable.", "rating": "3: Marginally above acceptance threshold", "confidence": "1: The reviewer's evaluation is an educated guess"} | review | 2023 | KDD.org/2023/Workshop/epiDAMIK | Consistent Comparison of Symptom-based Methods for COVID-19 Infection Detection (Extended Abstract) | ["Jes\u00fas Rufino", "Juan Marcos Ramirez", "Jos\u00e9 Aguilar", "Carlos Baquero", "Jaya Champati", "Davide Frey", "Rosa Elvira Lillo", "Antonio Fernandez Anta"] | During the global pandemic crisis, several COVID-19 diagnosis methods based on survey information have been proposed with the purpose of providing medical staff with quick detection tools that allow them to efficiently plan the limited healthcare resources. In general, these methods have been developed to detect COVID-19-positive cases from a particular combination of self-reported symptoms. In addition, these methods have been evaluated using datasets extracted from different studies with different characteristics. On the other hand, the University of Maryland, in partnership with Facebook, launched the Global COVID-19 Trends and Impact Survey (UMD-CTIS), the largest health surveillance tool to date that has collected information from 114 countries/territories from April 2020 to June 2022. This survey collected information on various individual features including gender, age groups, self-reported symptoms, isolation measures, and mental health status, among others. In this paper, we compare the performance of different COVID-19 diagnosis methods using the information collected by UMD-CTIS, for the years 2020 and 2021, in six countries: Brazil, Canada, Israel, Japan, Turkey, and South Africa. The evaluation of these methods with homogeneous data across countries and years provides a solid and consistent comparison among them. | ["COVID-19 diagnosis", "F1-score", "light gradient boosting machine", "logistic regression", "rule-based methods."] | https://openreview.net/forum?id=PhAOtEHLo1 | https://openreview.net/pdf?id=PhAOtEHLo1 | https://openreview.net/forum?id=PhAOtEHLo1¬eId=cqVYZkoZHY |
QaeeF0OdX2Y | PhAOtEHLo1 | KDD.org/2023/Workshop/epiDAMIK/Paper3/-/Official_Review | {"title": "This paper uses the UMD-CTIS dataset to evaluate the existing symptom-based detection methods.", "review": "This paper uses the UMD-CTIS dataset to evaluate the existing symptom-based detection methods.\n\nGoodness:\n1. The study covers many detection methods (10+) from three categories (rule-based methods, logistic regression-based methods, and tree-based methods), which gives an overview of the existing symptom-based detection methods.\n2. The evaluation in both the 2020 period and 2021 period allows us to explore the influence of vaccines in symptom detection, which is especially useful since vaccines may make COVID-infected patients show fewer symptoms, which influences the detection method performance.\n\nWeakness:\n1. The result section only includes the table explanation and lists the performance of different methods, while a more detailed explanation of why some methods are better and the takeaways are missing.\n2. The evaluation metric now is only the F1 score. More metrics are useful to better evaluate the difference between each method. Besides, in such detection problems, a high recall is usually more important than precision. More discussions can focus on this point.", "rating": "3: Marginally above acceptance threshold", "confidence": "5: The reviewer is absolutely certain that the evaluation is correct and very familiar with the relevant literature"} | review | 2023 | KDD.org/2023/Workshop/epiDAMIK | Consistent Comparison of Symptom-based Methods for COVID-19 Infection Detection (Extended Abstract) | ["Jes\u00fas Rufino", "Juan Marcos Ramirez", "Jos\u00e9 Aguilar", "Carlos Baquero", "Jaya Champati", "Davide Frey", "Rosa Elvira Lillo", "Antonio Fernandez Anta"] | During the global pandemic crisis, several COVID-19 diagnosis methods based on survey information have been proposed with the purpose of providing medical staff with quick detection tools that allow them to efficiently plan the limited healthcare resources. In general, these methods have been developed to detect COVID-19-positive cases from a particular combination of self-reported symptoms. In addition, these methods have been evaluated using datasets extracted from different studies with different characteristics. On the other hand, the University of Maryland, in partnership with Facebook, launched the Global COVID-19 Trends and Impact Survey (UMD-CTIS), the largest health surveillance tool to date that has collected information from 114 countries/territories from April 2020 to June 2022. This survey collected information on various individual features including gender, age groups, self-reported symptoms, isolation measures, and mental health status, among others. In this paper, we compare the performance of different COVID-19 diagnosis methods using the information collected by UMD-CTIS, for the years 2020 and 2021, in six countries: Brazil, Canada, Israel, Japan, Turkey, and South Africa. The evaluation of these methods with homogeneous data across countries and years provides a solid and consistent comparison among them. | ["COVID-19 diagnosis", "F1-score", "light gradient boosting machine", "logistic regression", "rule-based methods."] | https://openreview.net/forum?id=PhAOtEHLo1 | https://openreview.net/pdf?id=PhAOtEHLo1 | https://openreview.net/forum?id=PhAOtEHLo1¬eId=QaeeF0OdX2Y |
tpCKdIce6f | PhAOtEHLo1 | KDD.org/2023/Workshop/epiDAMIK/Paper3/-/Official_Review | {"title": "Consistent Comparison of Symptom-based Methods for COVID-19 Infection Detection- Review", "review": "In this work, the authors perform a consistent comparison of the different COVID-19 active case detection methods from the dataset constructed from the UMD-CTIS survey. The authors primarily implemented 3 broad types of detections, namely rule-based methods, logistic regression techniques and tree-based machine learning methods. F-1 score is used as the evaluation metric and the experiments were performed on the data from Brazil, Canada, Israel, Japan, Turkey and South Africa for the years 2020 and 2021.\nSome of the comments for this work are as follows:\n+ The work divides the data only in terms of years (2020 & 2021). However, a more important experiement would be to evaluate the models on the following scenarios:\n 1. Beginning of COVID where information about the disease was not well known vs the time when we got a lot of info about COVID.\n 2. Vaccines available vs not available. \n 3. Different COVID variants (alpha, beta, etc).\n 4. Model performance based on different mobility restrictions taking place (lockdowns, restricted international travel, etc) \n+ AUC-ROC can also be considered to be an important evaluation metric to compare the performance of the different models.", "rating": "4: Good paper, accept", "confidence": "3: The reviewer is fairly confident that the evaluation is correct"} | review | 2023 | KDD.org/2023/Workshop/epiDAMIK | Consistent Comparison of Symptom-based Methods for COVID-19 Infection Detection (Extended Abstract) | ["Jes\u00fas Rufino", "Juan Marcos Ramirez", "Jos\u00e9 Aguilar", "Carlos Baquero", "Jaya Champati", "Davide Frey", "Rosa Elvira Lillo", "Antonio Fernandez Anta"] | During the global pandemic crisis, several COVID-19 diagnosis methods based on survey information have been proposed with the purpose of providing medical staff with quick detection tools that allow them to efficiently plan the limited healthcare resources. In general, these methods have been developed to detect COVID-19-positive cases from a particular combination of self-reported symptoms. In addition, these methods have been evaluated using datasets extracted from different studies with different characteristics. On the other hand, the University of Maryland, in partnership with Facebook, launched the Global COVID-19 Trends and Impact Survey (UMD-CTIS), the largest health surveillance tool to date that has collected information from 114 countries/territories from April 2020 to June 2022. This survey collected information on various individual features including gender, age groups, self-reported symptoms, isolation measures, and mental health status, among others. In this paper, we compare the performance of different COVID-19 diagnosis methods using the information collected by UMD-CTIS, for the years 2020 and 2021, in six countries: Brazil, Canada, Israel, Japan, Turkey, and South Africa. The evaluation of these methods with homogeneous data across countries and years provides a solid and consistent comparison among them. | ["COVID-19 diagnosis", "F1-score", "light gradient boosting machine", "logistic regression", "rule-based methods."] | https://openreview.net/forum?id=PhAOtEHLo1 | https://openreview.net/pdf?id=PhAOtEHLo1 | https://openreview.net/forum?id=PhAOtEHLo1¬eId=tpCKdIce6f |
xb6stKM-O3T | u9zVZTg_Ky | KDD.org/2023/Workshop/epiDAMIK/Paper13/-/Official_Review | {"title": "This paper proposes a physics-informed neural network PINN to estimate time-varying parameters for SEIRD compartmental models as well as demonstrate learning the complex dynamics of disease and forecasting accurately.", "review": "The paper is of good quality, clear, and well-written. The authors clearly explained their motivation, addressed the shortcomings of previous methods, and touch upon all the necessary architectures. I would like to bring up a few important points that require attention.\n\n1. I'm a bit confused about the design of the constant variables $\\alpha$ and $\\epsilon$. In order to obtain a positive value, $\\alpha$ is set to a positive multiplication of a hyperbolic tangent function. However, some further clarification would be greatly appreciated.\n\n2. I highly appreciate the way all the methodologies are being explained with proper figures and equations.\n\n3. Based on the data presented in Figure 4, the figures depicting the data fitting during training are in perfect alignment with the observed data points. I am curious if dropouts were utilized in the model and, if not, whether overfitting occurred. It would be greatly appreciated if the authors could provide their insight on this matter.\n\n4. Based on Figure 5, the forecasting accuracy of $I, R, D$ for three different months appears to be relatively consistent when compared to actual observations.\n\n5. One of the pros of this paper is that the authors discussed the main limitation of PINNs and how requirement of prior knowledge could be a constraint while solving problems and potentially may impact accuracy if underlying epidemiological laws are poorly understood or data inconsistencies exist. \n\nThis paper on PINNs for infectious diseases is commendable, delivering accurate weekly forecasting results. While other studies have explored physics-informed neural networks in various compartmental models, such as SIR, SIRS, and SEIRM, this research stands out by successfully delivering on its initial claims.", "rating": "4: Good paper, accept", "confidence": "5: The reviewer is absolutely certain that the evaluation is correct and very familiar with the relevant literature"} | review | 2023 | KDD.org/2023/Workshop/epiDAMIK | Physics-informed neural networks integrating compartmental model for analyzing COVID-19 transmission dynamics | ["Xiao Ning", "Yongyue Wei", "Feng Chen"] | Modelling and predicting the behaviour of infectious diseases is essential for early warning and evaluating the most effective interventions to prevent significant harm. Compartmental models produce a system of ordinary differential equations (ODEs) that are renowned for simulating the transmission dynamics of infectious diseases. However, the parameters in compartmental models are often unknown, and they can even change over time in the real world, making them difficult to determine. This paper proposes an advanced artificial intelligence approach based on physics-informed neural networks (PINNs) to estimate time-varying parameters from given data for the compartmental model. Our proposed PINNs approach captures the complex dynamics of COVID-19 by integrating a modified Susceptible-Exposed-Infectious-Recovered-Death (SEIRD) compartmental model with deep neural networks. The experimental findings on synthesized data have demonstrated that our method robustly and accurately learns the dynamics and forecasts future states. Moreover, as more data becomes available, our proposed PINNs approach can be successfully extended to other regions and infectious diseases. | ["Compartmental models", "COVID-19 transmission", "Physics-informed neural networks", "Forward-inverse problem"] | https://openreview.net/forum?id=u9zVZTg_Ky | https://openreview.net/pdf?id=u9zVZTg_Ky | https://openreview.net/forum?id=u9zVZTg_Ky¬eId=xb6stKM-O3T |
HHtLa2J7QT | u9zVZTg_Ky | KDD.org/2023/Workshop/epiDAMIK/Paper13/-/Official_Review | {"title": "Interesting Idea That Would Benefit From Better Clarity and Justification ", "review": "This paper proposes the use of physics-informed neural networks (PINNs) to estimate time-varying parameters of ODEs to model transmission dynamics for infectious diseases. \n\nPositives: \n+ The idea of modeling the transmission dynamics through a SEIRD model with PINNs is an interesting idea and contribution \n+ The authors contain sufficient background on related work in order to present their contribution, and how their method is formed \n+ The epidemiological analysis throughout the results is much appreciated and provides a deeper appreciation of many of the results obtained \n+ The authors do a great job of contextualizing results with respect to the policies enacted in Italy during the beginning of the global pandemic. This contextualization really helps in understanding the learned trends for the time-varying parameters, and for R_t. \n\nPieces That Could Be Improved: \n- Grammar and writing clarity throughout the manuscript could be improved significantly. For example, the description of the compartmental model in section 2.1 could be significantly improved for clarity, as it is currently difficult to fully understand the different parameters of the model. This is an issue throughout the paper and makes the paper hard to follow \n- More information about how evaluation is performed should be provided. As it is written, it is unclear if different data were used for training the models and evaluation (in fact, currently, it seems they are the same data). This could pose an issue with proper validation.\n- It is not clear whether the reported MAE, RMSE, and MAPE results are sufficiently strong. It would be beneficial to see more baselines to see if the proposed PINN is actually performing well, such as if traditional NNs (such as recurrent neural networks) that only forecast I, R, D without modeling the ODEs perform worse.\n- There should be more ablations to understand if their proposed changes to the model actually result in meaningful changes. For example, is the PINN-based activation functions for alpha and epsilon meaningful? And do the two models for the ODE and the time-varying parameters of the simulation make a difference compared to one shared model? As these points were not well-motivated in the methods, it would be useful to see their importance in the real experimental results. \n", "rating": "2: Marginally below acceptance threshold", "confidence": "4: The reviewer is confident but not absolutely certain that the evaluation is correct"} | review | 2023 | KDD.org/2023/Workshop/epiDAMIK | Physics-informed neural networks integrating compartmental model for analyzing COVID-19 transmission dynamics | ["Xiao Ning", "Yongyue Wei", "Feng Chen"] | Modelling and predicting the behaviour of infectious diseases is essential for early warning and evaluating the most effective interventions to prevent significant harm. Compartmental models produce a system of ordinary differential equations (ODEs) that are renowned for simulating the transmission dynamics of infectious diseases. However, the parameters in compartmental models are often unknown, and they can even change over time in the real world, making them difficult to determine. This paper proposes an advanced artificial intelligence approach based on physics-informed neural networks (PINNs) to estimate time-varying parameters from given data for the compartmental model. Our proposed PINNs approach captures the complex dynamics of COVID-19 by integrating a modified Susceptible-Exposed-Infectious-Recovered-Death (SEIRD) compartmental model with deep neural networks. The experimental findings on synthesized data have demonstrated that our method robustly and accurately learns the dynamics and forecasts future states. Moreover, as more data becomes available, our proposed PINNs approach can be successfully extended to other regions and infectious diseases. | ["Compartmental models", "COVID-19 transmission", "Physics-informed neural networks", "Forward-inverse problem"] | https://openreview.net/forum?id=u9zVZTg_Ky | https://openreview.net/pdf?id=u9zVZTg_Ky | https://openreview.net/forum?id=u9zVZTg_Ky¬eId=HHtLa2J7QT |
SooiU6_zYjK | u9zVZTg_Ky | KDD.org/2023/Workshop/epiDAMIK/Paper13/-/Official_Review | {"title": "Review ", "review": "### Summary\n\nThis work studies using physics-informed neural networks to estimate the unknown parameters of epidemic compartmental models. To achieve this, this work first proposes an extended counterpart model, named SEIRD, to model the dynamics of the COVID-19 pandemic, which takes the Death (D) counts into consideration. This paper then posits that the parameters for the pandemic in different phases are dynamic. Therefore, this paper proposes a graph neural network to fit the reported cases and epidemic model parameters simultaneously. In experiments, this work evaluates the proposed GNN on the reported cases from Italy. The results show that the model is able to fit the reported cases and generate corresponding epidemic model parameters. Lastly, the model is applied to forecast the infected cases. The results show that the model predicts the reported cases reasonably accurately, mostly achieving within 20% relative absolute error. \n\n### Strengths\n\n- This paper designs a physics-informed neural network to fit case counts and the parameters in the epidemic model simultaneously and considers the dynamics of the epidemic model parameters. \n- The empirical study on the reported cases from Italy shows that model fits the reported cases accurately and generates meaningful model parameters. \n\n### Weaknesses\n\n- It would be interesting to connect the intepretation of estimated epidemic parameters to the intervention policicies. Figure 3 shows the intervention policies conducted by the government during the pandemic. I wondering whether the estimated parameters can be incorporated to explain the effect of each intervention policy. Can the local changes of estimated parameters be interpreted corresponding to the application of the intervention policy. \n- The proposed methods needs to be described in more details. For example, in Figure 2, there is a automatic differentiation step to convert the estimated cases to its differentiation. It would be helpful to describe how this step is conducted, since it connects the two conterparts of the neural networks. \n- Experimental setup is not well described. For example, in data fitting experiments in Section 3.3.1, it would be better for the reader to interprete the results, if the authors can explain the data splitting for fitting the model to the data. Would different data splitting leads to different results? \n- Comparison with related baselines needs to be incorporated. This paper shows the error of the model regarding forecasting the reported cases of the pandemic. However, the comparison with related baselines, such as other PINN or time series prediction methods, would be helpful to assess to the effect of the proposed model. \n- Discussion of related work is missing. It would be better to provide a more detailed discussion of previous epidemic models and phisics-informed neural networks. \n\n", "rating": "3: Marginally above acceptance threshold", "confidence": "4: The reviewer is confident but not absolutely certain that the evaluation is correct"} | review | 2023 | KDD.org/2023/Workshop/epiDAMIK | Physics-informed neural networks integrating compartmental model for analyzing COVID-19 transmission dynamics | ["Xiao Ning", "Yongyue Wei", "Feng Chen"] | Modelling and predicting the behaviour of infectious diseases is essential for early warning and evaluating the most effective interventions to prevent significant harm. Compartmental models produce a system of ordinary differential equations (ODEs) that are renowned for simulating the transmission dynamics of infectious diseases. However, the parameters in compartmental models are often unknown, and they can even change over time in the real world, making them difficult to determine. This paper proposes an advanced artificial intelligence approach based on physics-informed neural networks (PINNs) to estimate time-varying parameters from given data for the compartmental model. Our proposed PINNs approach captures the complex dynamics of COVID-19 by integrating a modified Susceptible-Exposed-Infectious-Recovered-Death (SEIRD) compartmental model with deep neural networks. The experimental findings on synthesized data have demonstrated that our method robustly and accurately learns the dynamics and forecasts future states. Moreover, as more data becomes available, our proposed PINNs approach can be successfully extended to other regions and infectious diseases. | ["Compartmental models", "COVID-19 transmission", "Physics-informed neural networks", "Forward-inverse problem"] | https://openreview.net/forum?id=u9zVZTg_Ky | https://openreview.net/pdf?id=u9zVZTg_Ky | https://openreview.net/forum?id=u9zVZTg_Ky¬eId=SooiU6_zYjK |
ZTv2PLP1JmS | u9zVZTg_Ky | KDD.org/2023/Workshop/epiDAMIK/Paper13/-/Official_Review | {"title": "Interesting but not significant", "review": "Review Summary:\n\nThis paper presents an approach utilizing physics-informed neural networks (PINNs) to estimate time-varying parameters in compartmental models for infectious diseases. The authors successfully integrate the SEIRD model with deep neural networks to capture the dynamics of COVID-19, demonstrating proficient learning and accurate future state predictions using the PINNs approach. The results showcase the potential applicability of this method to various regions and infectious diseases. Nonetheless, the absence of comparative analysis with existing methods and the suboptimal forecasting performance depicted in Figure 7 and Table 1 raise notable concerns. Comparable studies (references [1] and [2] which are both compartmental model + deep neural networks for COVID-19 dynamics) have achieved superior performance with simpler compartmental models, specifically the SIRD model instead of the SEIRD model. Additional empirical evidence or theoretical support is imperative to substantiate the significance of this work.\n\nPros:\n1. Introduction of an advanced artificial intelligence approach based on physics-informed neural networks for estimating time-varying parameters in compartmental models.\n2. Integration of the SEIRD model with deep neural networks to capture the complex dynamics of COVID-19.\n3. The potential applicability of the proposed approach to other regions and infectious diseases.\n\nCons:\n1. Lack of comparison with existing methods and inadequate rationale behind the proposed model.\n2. Suboptimal performance was observed in the forecasting results presented in Figure 7 and Table 1.\n3. Similar studies (e.g., references [1] and [2]) have achieved superior performance.\nFor instance, in [1], the mean absolute error (MAE) of parameter I for 3-day forecasting is reported as 29.57. In [2], the MAE of I for 3-day forecasting is documented as 251.73 and 200.24. Conversely, in this work, the MAE of I for 3-day forecasting is significantly larger, ranging from 5411 to 1352.\n\nWithout substantial empirical evidence or theoretical support to establish the significance of this work, I am inclined to believe that its quality and significance may not meet the criteria for acceptance.\n\n[1] Ning, Xiao, et al. \"Epi-DNNs: Epidemiological priors informed deep neural networks for modeling COVID-19 dynamics.\" Computers in biology and medicine 158 (2023): 106693.\n\n[2] Ning, Xiao, et al. \"Euler iteration augmented physics-informed neural networks for time-varying parameter estimation of the epidemic compartmental model.\" Frontiers in Physics 10 (2022): 1300.\n", "rating": "1: Ok but not good enough - rejection", "confidence": "4: The reviewer is confident but not absolutely certain that the evaluation is correct"} | review | 2023 | KDD.org/2023/Workshop/epiDAMIK | Physics-informed neural networks integrating compartmental model for analyzing COVID-19 transmission dynamics | ["Xiao Ning", "Yongyue Wei", "Feng Chen"] | Modelling and predicting the behaviour of infectious diseases is essential for early warning and evaluating the most effective interventions to prevent significant harm. Compartmental models produce a system of ordinary differential equations (ODEs) that are renowned for simulating the transmission dynamics of infectious diseases. However, the parameters in compartmental models are often unknown, and they can even change over time in the real world, making them difficult to determine. This paper proposes an advanced artificial intelligence approach based on physics-informed neural networks (PINNs) to estimate time-varying parameters from given data for the compartmental model. Our proposed PINNs approach captures the complex dynamics of COVID-19 by integrating a modified Susceptible-Exposed-Infectious-Recovered-Death (SEIRD) compartmental model with deep neural networks. The experimental findings on synthesized data have demonstrated that our method robustly and accurately learns the dynamics and forecasts future states. Moreover, as more data becomes available, our proposed PINNs approach can be successfully extended to other regions and infectious diseases. | ["Compartmental models", "COVID-19 transmission", "Physics-informed neural networks", "Forward-inverse problem"] | https://openreview.net/forum?id=u9zVZTg_Ky | https://openreview.net/pdf?id=u9zVZTg_Ky | https://openreview.net/forum?id=u9zVZTg_Ky¬eId=ZTv2PLP1JmS |
BrMpa2ZuaxJ | Unyf3QsNmx | KDD.org/2023/Workshop/epiDAMIK/Paper11/-/Official_Review | {"title": "Hierarchical Clustering and Multivariate Forecasting for Health Econometrics- Review", "review": "**Summary:**\n\nThis study uses clustering and time series forecasting to create retrospective scenarios for forecasting the long-term impact of changes in socio-economic indicators on health indicators. Firstly, the authors used hierarchal clustering to group countries based on socio-economic indicators based on the World Bank Health Statistics and Nutrition dataset. Following that, the authors performed time series analysis to predict the values of the different indicators using the multivariate prophet model on the countries which appear in one of the groups. This led to valuable insights about the dynamics in the future.\n\n**Strong Points:**\n\n- The clusters constructed are interesting. Cluster 1 seem to be a whole group of Eastern European Countries which are geographical neighbors. Cluster 3 countries are not geographically close but are developing nations. Cluster 4 & 5 seem to be African countries and so on...\n- The authors perform a thorough literature review which provides a good platform to evaluate the significance of this study.\n- This is a well written paper. The explanations provided are good, the figures are well made and it surely applies a variety of methods. This can surely add to the technical contributions of this work.\n\n**Weak Points:**\n\n- Why hierarchal clustering? Spectral clustering is also a good method, right? The authors need to mention their motivation behind using hierarchal clustering. \n- Retrospective Interpretations of clusters are needed as the relation in some of them are not that obvious. For example, what is the relation between the countries that appear in cluster 2? It's not that clear.\n- In Figure 4, some of the indicator forecasts have a high level of uncertainty more than the others. Exploring what is causing this is extremely valuable but is sadly missing. \n\n**Suggestions:**\n\n- I understand that logarithmic scaling gave better performance. However, one of the lim imitations mentioned of forecasting the Population Growth indicator could be easily done by using Min-Max scaling. By any means, the performance could have been reported.\n- What is the significance of a threshold of 0.815 in section 3.4? Was it used in prior works? ", "rating": "4: Good paper, accept", "confidence": "4: The reviewer is confident but not absolutely certain that the evaluation is correct"} | review | 2023 | KDD.org/2023/Workshop/epiDAMIK | Hierarchical Clustering and Multivariate Forecasting for Health Econometrics | ["Atika Rahman Paddo", "Sadia Afreen", "Saptarshi Purkayastha"] | Data science approaches in Health Econometrics and Public Health research are limited, with a lack of exploration of state-of-the-art computational methods. Recent studies have shown that neural networks and machine learning methods outperform traditional statistical methods in forecasting and time-series analysis. In this study, we demonstrate the use of unsupervised and supervised machine learning approaches to create "what-if" scenarios for forecasting the long-term impact of changes in socio-economic indicators on health indicators. These indicators include basic sanitation services, immunization, population ages, life expectancy, and domestic health expenditure. To begin, we utilized Hierarchical Cluster Analysis to group 131 countries into 9 clusters based on various indicators from the World Bank Health Statistics and Nutrition dataset. This step allowed us to create clusters of countries. In order to showcase the feasibility of our approach, we performed a time series analysis using multivariate prophet on the most significant features from a cluster consisting of Bahrain, Kuwait, Oman, Qatar, and Saudi Arabia. The study developed robust models (π
2 = 0.93+) capable of forecasting 11 health indicators up to 10 years into the future. By employing these "what-if" scenarios and forecasting models, policymakers and healthcare practitioners can make informed decisions and effectively implement targeted interventions to address health-related challenges. | ["Clustering", "forecasting", "health econometrics", "data science"] | https://openreview.net/forum?id=Unyf3QsNmx | https://openreview.net/pdf?id=Unyf3QsNmx | https://openreview.net/forum?id=Unyf3QsNmx¬eId=BrMpa2ZuaxJ |
irZpjz7H3fF | Unyf3QsNmx | KDD.org/2023/Workshop/epiDAMIK/Paper11/-/Official_Review | {"title": "Interesting analysis", "review": "## Clarity\n\nThis paper and proposed method is easy to follow.\n\n## Quality\n\nThe analysis is well-motivated and fully delivered the idea.\n\n## Originality\n\nThis is original work with interesting problem.\n\n## Significance\n\nThe work is significant.\n\n## Pros:\n\n- Well-written, and clearly delivers the ideas, proposed method, and results.\n \n- The analysis is interesting to me.\n \n- The authors are well aware of the limitations of the proposed method.\n \n\n## Cons:\n\n- The way authors get feature importance is not clear.\n \n- Authors may consider using different methods for multivariate time-series forecasting such as MLP, LSTM, \u2026\n \n- Authors did not include similar analysis for the univariate case to highlight the benefit of the multivariate model, although authors remove some features based on the performance of univariate models.\n \n- The variance for future forecasting results are high, then the conclusion is a bit uncertain (besides the mentioned factors like political changes, economic fluctuations, \u2026)", "rating": "4: Good paper, accept", "confidence": "3: The reviewer is fairly confident that the evaluation is correct"} | review | 2023 | KDD.org/2023/Workshop/epiDAMIK | Hierarchical Clustering and Multivariate Forecasting for Health Econometrics | ["Atika Rahman Paddo", "Sadia Afreen", "Saptarshi Purkayastha"] | Data science approaches in Health Econometrics and Public Health research are limited, with a lack of exploration of state-of-the-art computational methods. Recent studies have shown that neural networks and machine learning methods outperform traditional statistical methods in forecasting and time-series analysis. In this study, we demonstrate the use of unsupervised and supervised machine learning approaches to create "what-if" scenarios for forecasting the long-term impact of changes in socio-economic indicators on health indicators. These indicators include basic sanitation services, immunization, population ages, life expectancy, and domestic health expenditure. To begin, we utilized Hierarchical Cluster Analysis to group 131 countries into 9 clusters based on various indicators from the World Bank Health Statistics and Nutrition dataset. This step allowed us to create clusters of countries. In order to showcase the feasibility of our approach, we performed a time series analysis using multivariate prophet on the most significant features from a cluster consisting of Bahrain, Kuwait, Oman, Qatar, and Saudi Arabia. The study developed robust models (π
2 = 0.93+) capable of forecasting 11 health indicators up to 10 years into the future. By employing these "what-if" scenarios and forecasting models, policymakers and healthcare practitioners can make informed decisions and effectively implement targeted interventions to address health-related challenges. | ["Clustering", "forecasting", "health econometrics", "data science"] | https://openreview.net/forum?id=Unyf3QsNmx | https://openreview.net/pdf?id=Unyf3QsNmx | https://openreview.net/forum?id=Unyf3QsNmx¬eId=irZpjz7H3fF |
LhpGGbQT8JL | Unyf3QsNmx | KDD.org/2023/Workshop/epiDAMIK/Paper11/-/Official_Review | {"title": "Reject", "review": "In this paper, the authors used hierarchical clustering to group 131 countries into several clusters and then performed a time-series forecasting for the cluster consisting of several Middle Eastern countries. While forecasting socio-economic and health indicators is important for policymaking, the methods used in this study are relatively simple. I have a few concerns about the study and results.\n1. Both the clustering and forecasting methods are off-the-shelf approaches. It is not clear the methodological novelty of this study. For instance, time-series forecasting is widely used in other studies.\n2. There was no comparison of the forecasting method with other approaches. There should be more accurate forecasting methods, and the authors did not establish the advantage of the current method in this study.\n3. Lack of details. Many technical details were not provided in the manuscript. For instance, what features were included in the dataset? What additional variables were used in the multivariate forecasts, and how to select those variables? How did the authors select the prediction target variables in Table 3? Was it because the forecasting method worked better for those targets?\n4. In Eq. (3), the prediction of y(t) needs the input of exogenous variables in the future, which is not available when the forecast is generated. How to solve this issue? How to decide which exogenous variables to include?\n", "rating": "1: Ok but not good enough - rejection", "confidence": "4: The reviewer is confident but not absolutely certain that the evaluation is correct"} | review | 2023 | KDD.org/2023/Workshop/epiDAMIK | Hierarchical Clustering and Multivariate Forecasting for Health Econometrics | ["Atika Rahman Paddo", "Sadia Afreen", "Saptarshi Purkayastha"] | Data science approaches in Health Econometrics and Public Health research are limited, with a lack of exploration of state-of-the-art computational methods. Recent studies have shown that neural networks and machine learning methods outperform traditional statistical methods in forecasting and time-series analysis. In this study, we demonstrate the use of unsupervised and supervised machine learning approaches to create "what-if" scenarios for forecasting the long-term impact of changes in socio-economic indicators on health indicators. These indicators include basic sanitation services, immunization, population ages, life expectancy, and domestic health expenditure. To begin, we utilized Hierarchical Cluster Analysis to group 131 countries into 9 clusters based on various indicators from the World Bank Health Statistics and Nutrition dataset. This step allowed us to create clusters of countries. In order to showcase the feasibility of our approach, we performed a time series analysis using multivariate prophet on the most significant features from a cluster consisting of Bahrain, Kuwait, Oman, Qatar, and Saudi Arabia. The study developed robust models (π
2 = 0.93+) capable of forecasting 11 health indicators up to 10 years into the future. By employing these "what-if" scenarios and forecasting models, policymakers and healthcare practitioners can make informed decisions and effectively implement targeted interventions to address health-related challenges. | ["Clustering", "forecasting", "health econometrics", "data science"] | https://openreview.net/forum?id=Unyf3QsNmx | https://openreview.net/pdf?id=Unyf3QsNmx | https://openreview.net/forum?id=Unyf3QsNmx¬eId=LhpGGbQT8JL |
Q-Kz-5gCR64 | rMSlLb33Gb | KDD.org/2023/Workshop/epiDAMIK/Paper2/-/Official_Review | {"title": "Paper shows the efficacy of indirect surveys in the estimation of epidemic indicators in places where official figures are unreliable.", "review": "This paper tackles the problem of estimating snapshot Covid-19 incidence rates in locations where the official figures are believed to be unreliable. They utilize an indirect survey method to collect data from respondents which has the benefits of preserving their privacy and mitigating bias due to age or education level. They modify the Network Scale Up Method by fixing the number of close contacts in their survey. They validate their approach by estimating for the UK and Australia using the English version of the indirect survey and present results from China.\n\nI think this is well-written paper describing the methods, data collection strategy and prior related work in adequate detail. By comparing their estimates for the UK and Australia with the official figures, they show the validity of their estimates in China where the official figures might conceal the true rates of hospitalizations and mortality. The results are very interesting as they show a general agreement with the official vaccination rates while showing wide disparity in the estimates for deaths and cases.\n\nThe data pre-processing steps weeds out inconsistent and/or outlier responses. This whittles down the sample size from 1000 to 469. This affects the ability to reliably estimate for cities, especially considering the population size. I was wondering if there was a way to preserve some of the inconsistent responses by making expert adjustments and how that would affect the results?\n\nLastly, they compute the Cronbach's Alpha coefficient on the responses of the indirect surveys for the UK and Australia, which suggests that the indirect survey method is reliable. I believe the methods in this paper are well thought-out and the results are worth a close look. I await the outcome of their future work.", "rating": "4: Good paper, accept", "confidence": "3: The reviewer is fairly confident that the evaluation is correct"} | review | 2023 | KDD.org/2023/Workshop/epiDAMIK | A Snapshot of COVID-19 Incidence, Hospitalizations, and Mortality from Indirect Survey Data in China in January 2023 (Extended Abstract) | ["Juan Marcos Ramirez", "Sergio Diaz-Aranda", "Jose Aguilar", "Oluwasegun Ojo", "Rosa Elvira Lillo", "Antonio Fernandez Anta"] | The estimation of incidence has been a crucial component for monitoring COVID-19 dissemination. This has become challenging when official data are unavailable or insufficiently reliable. Hence, the implementation of efficient, inexpensive, and secure techniques that capture information about epidemic indicators is required. This study aims to provide a snapshot of COVID-19 incidence, hospitalizations, and mortality in different countries in January 2023. To this end, we collected data on the number of cases, deaths, vaccinations, and hospitalizations among the fifteen closest contacts to survey respondents. More precisely, indirect surveys were conducted for 100 respondents from Australia on 19 January 2023, 200 respondents from the UK on 19 January 2023, and 1,000 respondents from China between 18-26 January 2023. To assess the incidence of COVID-19, we used a modified version Network Scale-up Method (NSUM) that fixes the number of people in the contact network (reach). We have compared our estimates with official data from Australia and the UK in order to validate our approach. In the case of the vaccination rate, our approach estimates a very close value to the official data, and in the case of hospitalizations and deaths, the official results are within the confidence interval. Regarding the remaining variables, our approach overestimates the values obtained by the Our World in Data (OWID) platform but is close to the values provided by the Officer of National Statistics (ONS) in the case of the UK (within the confidence interval). In addition, Cronbach's alpha gives values that allow us to conclude that the reliability of the estimates in relation to the consistency of the answers is excellent for the UK and good for Australia. Following the same methodology, we have estimated the same metrics for different Chinese cities and provinces. It is worth noting that this approach allows quick estimates to be made with a reduced number of surveys to achieve a wide population coverage, preserving the privacy of the participants. | ["COVID-19", "incidence estimation", "indirect surveys", "NSUM"] | https://openreview.net/forum?id=rMSlLb33Gb | https://openreview.net/pdf?id=rMSlLb33Gb | https://openreview.net/forum?id=rMSlLb33Gb¬eId=Q-Kz-5gCR64 |
i7lQlnCLyYO | rMSlLb33Gb | KDD.org/2023/Workshop/epiDAMIK/Paper2/-/Official_Review | {"title": "Paper provides a succinct and accessible method to estimate disease incidence", "review": "### Summary\nThis paper seeks to improve disease incidence estimation methods using information from surveys about contacts, rather than the respondents direct experience. They can obtain much more information by asking about multiple individuals the respondent knows about rather than gather information about only one individual per survey. From this information, they use a modified network scale up method to determine estimated incidence for Australia, the UK, and China and use Cronbach\u2019s alpha to verify the reliability of their data. In addition, they thoroughly clean the data they obtain in order to get a better estimate.\n\n### Strengths\n- They compare with a range of locations for validation rather than relying on only one.\n- Their data-preprocessing and estimation methods are clear and well-explained.\n\n### Weaknesses\n- The authors do not discuss how the differences in region of respondents affects the estimates in other regions. For example, how do estimates based on the regions with many respondents perform for regions with a much lower response rate?\n- They do not discuss the impact of sample size. Can a study be performed where the sample size is discussed in the context of confidence and estimate performance? It may not be viable to study, but are there hypotheses on when the sample size is too large (i.e., the sets of 15 contacts begin to overlap resulting in over-counting)?\n\n### Suggestions\n- The sentence \u201c\u2026and hospitalizations among 15 of the closest contacts\u201d could use a bit more elaboration, such as \u201c\u2026closest contacts to survey respondents\u201d.\n- What are the results if the data was not pre-processed?\n\n### Minor\n- What is n on line 177?\n- The writing is imprecise in some places such as line 206, 224", "rating": "4: Good paper, accept", "confidence": "4: The reviewer is confident but not absolutely certain that the evaluation is correct"} | review | 2023 | KDD.org/2023/Workshop/epiDAMIK | A Snapshot of COVID-19 Incidence, Hospitalizations, and Mortality from Indirect Survey Data in China in January 2023 (Extended Abstract) | ["Juan Marcos Ramirez", "Sergio Diaz-Aranda", "Jose Aguilar", "Oluwasegun Ojo", "Rosa Elvira Lillo", "Antonio Fernandez Anta"] | The estimation of incidence has been a crucial component for monitoring COVID-19 dissemination. This has become challenging when official data are unavailable or insufficiently reliable. Hence, the implementation of efficient, inexpensive, and secure techniques that capture information about epidemic indicators is required. This study aims to provide a snapshot of COVID-19 incidence, hospitalizations, and mortality in different countries in January 2023. To this end, we collected data on the number of cases, deaths, vaccinations, and hospitalizations among the fifteen closest contacts to survey respondents. More precisely, indirect surveys were conducted for 100 respondents from Australia on 19 January 2023, 200 respondents from the UK on 19 January 2023, and 1,000 respondents from China between 18-26 January 2023. To assess the incidence of COVID-19, we used a modified version Network Scale-up Method (NSUM) that fixes the number of people in the contact network (reach). We have compared our estimates with official data from Australia and the UK in order to validate our approach. In the case of the vaccination rate, our approach estimates a very close value to the official data, and in the case of hospitalizations and deaths, the official results are within the confidence interval. Regarding the remaining variables, our approach overestimates the values obtained by the Our World in Data (OWID) platform but is close to the values provided by the Officer of National Statistics (ONS) in the case of the UK (within the confidence interval). In addition, Cronbach's alpha gives values that allow us to conclude that the reliability of the estimates in relation to the consistency of the answers is excellent for the UK and good for Australia. Following the same methodology, we have estimated the same metrics for different Chinese cities and provinces. It is worth noting that this approach allows quick estimates to be made with a reduced number of surveys to achieve a wide population coverage, preserving the privacy of the participants. | ["COVID-19", "incidence estimation", "indirect surveys", "NSUM"] | https://openreview.net/forum?id=rMSlLb33Gb | https://openreview.net/pdf?id=rMSlLb33Gb | https://openreview.net/forum?id=rMSlLb33Gb¬eId=i7lQlnCLyYO |
bXQdGqYlDN | rMSlLb33Gb | KDD.org/2023/Workshop/epiDAMIK/Paper2/-/Official_Review | {"title": "Review of a Paper on Estimating COVID-19 Snapshots: Strong Results, Need for Comparisons, and Requirement for Further Elaboration", "review": "Quality:\n\nThe quality of the paper is good overall. The authors present an approach to estimating COVID-19 snapshots using a modified Network Scale-up Method (NSUM) and validate their estimates against official data. The data preprocessing stage helps enhance the reliability of the collected data, and the privacy preservation aspect adds value to the study. However, there are some limitations, such as the lack of comparisons with other estimation methods or its limited generalizability.\n\nClarity:\n\nThe paper is generally well-written and presents the information in a clear manner but does have a few typos and items which could have been explained some more. The introduction provides adequate background information about the need for indirect survey methods and the challenges associated with official COVID-19 data. The methodology section explains the data preprocessing techniques well, but could benefit from further clarification. For example, the NSUM technique is only cited but not explained anywhere, and also the choice of setting ri=15 is not justified (why not 5 or 10?).\n\nOriginality:\n\nThe paper cites that the use of indirect surveys to estimate different variables using NSUM is not something new, it also cites that this has also been done for estimating different indicators during the COVID-19 pandemic. \n\nSignificance:\n\nThe significance of this work lies in its potential to provide valuable insights into COVID-19 indicators, especially in settings where official data is limited or unreliable. The indirect survey method offers a practical solution to estimate important epidemiological information, which can aid decision makers and researchers in understanding the spread of the disease and acting accordingly. The paper's comparison with official data and validation of estimates add credibility to its findings, further highlighting its significance.\n\nPros:\n\n- Justification for Indirect Surveys: The paper provides a strong rationale for using indirect surveys, highlighting privacy preservation and other benefits.\n\n- Validated and Discussed Results: The paper presents well-validated results and provides a comprehensive discussion of the findings.\nUse of Cronbach's Alpha Coefficient: The paper employs Cronbach's alpha coefficient, a reliable measure of internal consistency, enhancing the robustness of the analysis.\n\n- Acknowledgment of Sample Size Limitation: The paper recognizes the limitation of the sample size and discusses its potential impact on the accuracy and generalizability of the estimates.\n\n- Data Preprocessing Stage: The paper includes a well-described data preprocessing stage, which enhances the reliability and quality of the collected data.\n\n\nCons:\n\n- Need for Comparison to Validate Modifications and NSUM Choice: The paper should include a comparison with other methods to validate the modifications made and the selection of the Network Scale-up Method (NSUM).\n\n- Insufficient Elaboration on NSUM and Choice of \"ri\": The paper should provide more explanation and elaboration on NSUM and the selection of \"ri\" to improve reader understanding.\n\n- Limited Generalizability: The study's focus on a specific time period and a restricted set of countries (China, Australia, and the UK) limits the generalizability of the results to other countries and different time periods.\n\n- Few typos: 1- Typo in mortality rate, should be 0.72 not 0.22 based on table (line 218 right column). 2- Variable naming is either not consistent or not explained sufficiently in equations 1 and 2, would be good to clarify here what the \"a\", \"b\", \"alpha\", and \"beta\" variables represent\n\n\nIn summary, this paper presents a good approach to estimate COVID-19 indicators using the Network Scale-up Method. While it has strong results, there are also limitations to consider. Further elaboration could address a lot of these limitations.", "rating": "2: Marginally below acceptance threshold", "confidence": "2: The reviewer is willing to defend the evaluation, but it is quite likely that the reviewer did not understand central parts of the paper"} | review | 2023 | KDD.org/2023/Workshop/epiDAMIK | A Snapshot of COVID-19 Incidence, Hospitalizations, and Mortality from Indirect Survey Data in China in January 2023 (Extended Abstract) | ["Juan Marcos Ramirez", "Sergio Diaz-Aranda", "Jose Aguilar", "Oluwasegun Ojo", "Rosa Elvira Lillo", "Antonio Fernandez Anta"] | The estimation of incidence has been a crucial component for monitoring COVID-19 dissemination. This has become challenging when official data are unavailable or insufficiently reliable. Hence, the implementation of efficient, inexpensive, and secure techniques that capture information about epidemic indicators is required. This study aims to provide a snapshot of COVID-19 incidence, hospitalizations, and mortality in different countries in January 2023. To this end, we collected data on the number of cases, deaths, vaccinations, and hospitalizations among the fifteen closest contacts to survey respondents. More precisely, indirect surveys were conducted for 100 respondents from Australia on 19 January 2023, 200 respondents from the UK on 19 January 2023, and 1,000 respondents from China between 18-26 January 2023. To assess the incidence of COVID-19, we used a modified version Network Scale-up Method (NSUM) that fixes the number of people in the contact network (reach). We have compared our estimates with official data from Australia and the UK in order to validate our approach. In the case of the vaccination rate, our approach estimates a very close value to the official data, and in the case of hospitalizations and deaths, the official results are within the confidence interval. Regarding the remaining variables, our approach overestimates the values obtained by the Our World in Data (OWID) platform but is close to the values provided by the Officer of National Statistics (ONS) in the case of the UK (within the confidence interval). In addition, Cronbach's alpha gives values that allow us to conclude that the reliability of the estimates in relation to the consistency of the answers is excellent for the UK and good for Australia. Following the same methodology, we have estimated the same metrics for different Chinese cities and provinces. It is worth noting that this approach allows quick estimates to be made with a reduced number of surveys to achieve a wide population coverage, preserving the privacy of the participants. | ["COVID-19", "incidence estimation", "indirect surveys", "NSUM"] | https://openreview.net/forum?id=rMSlLb33Gb | https://openreview.net/pdf?id=rMSlLb33Gb | https://openreview.net/forum?id=rMSlLb33Gb¬eId=bXQdGqYlDN |
OLCYPVZAmhN | rMSlLb33Gb | KDD.org/2023/Workshop/epiDAMIK/Paper2/-/Official_Review | {"title": "Review of the paper. ", "review": "This study presents estimates for COVID incidence cases, deaths, and vaccination rates based on a survey study. \n\nOverall, the paper is really good in quality, clarity, originality and significance. The paper is well-written; however, there are a few areas that the authors should address:\n\n1. In section 2.1, the authors conducted an online survey in Australia and the UK for validation. It would be beneficial for the authors to provide justification for selecting these specific countries. For example, they should explain why China was not included in the online survey.\n\n2. If space allows, it would be helpful to include a figure illustrating the skewness in the data, as discussed in Section 2.2. This figure could demonstrate the requirement of Medcouple statistics.\n\n3. Please include a reference for the Cronbach's alpha coefficient. This would provide readers with additional information and support the use of this measure.\n\n4. To enhance the paper's transparency, the authors should clarify how the 95% confidence interval (C.I.) was computed in Table 1 and Table 2. \n\n5. It is unclear how a small sample size, such as the one used for all the cities, can be utilized to derive the confidence interval and make any valid claims. \n\nBy addressing these points, the authors can further improve the clarity and comprehensiveness of their work.\n\n\n", "rating": "4: Good paper, accept", "confidence": "5: The reviewer is absolutely certain that the evaluation is correct and very familiar with the relevant literature"} | review | 2023 | KDD.org/2023/Workshop/epiDAMIK | A Snapshot of COVID-19 Incidence, Hospitalizations, and Mortality from Indirect Survey Data in China in January 2023 (Extended Abstract) | ["Juan Marcos Ramirez", "Sergio Diaz-Aranda", "Jose Aguilar", "Oluwasegun Ojo", "Rosa Elvira Lillo", "Antonio Fernandez Anta"] | The estimation of incidence has been a crucial component for monitoring COVID-19 dissemination. This has become challenging when official data are unavailable or insufficiently reliable. Hence, the implementation of efficient, inexpensive, and secure techniques that capture information about epidemic indicators is required. This study aims to provide a snapshot of COVID-19 incidence, hospitalizations, and mortality in different countries in January 2023. To this end, we collected data on the number of cases, deaths, vaccinations, and hospitalizations among the fifteen closest contacts to survey respondents. More precisely, indirect surveys were conducted for 100 respondents from Australia on 19 January 2023, 200 respondents from the UK on 19 January 2023, and 1,000 respondents from China between 18-26 January 2023. To assess the incidence of COVID-19, we used a modified version Network Scale-up Method (NSUM) that fixes the number of people in the contact network (reach). We have compared our estimates with official data from Australia and the UK in order to validate our approach. In the case of the vaccination rate, our approach estimates a very close value to the official data, and in the case of hospitalizations and deaths, the official results are within the confidence interval. Regarding the remaining variables, our approach overestimates the values obtained by the Our World in Data (OWID) platform but is close to the values provided by the Officer of National Statistics (ONS) in the case of the UK (within the confidence interval). In addition, Cronbach's alpha gives values that allow us to conclude that the reliability of the estimates in relation to the consistency of the answers is excellent for the UK and good for Australia. Following the same methodology, we have estimated the same metrics for different Chinese cities and provinces. It is worth noting that this approach allows quick estimates to be made with a reduced number of surveys to achieve a wide population coverage, preserving the privacy of the participants. | ["COVID-19", "incidence estimation", "indirect surveys", "NSUM"] | https://openreview.net/forum?id=rMSlLb33Gb | https://openreview.net/pdf?id=rMSlLb33Gb | https://openreview.net/forum?id=rMSlLb33Gb¬eId=OLCYPVZAmhN |
O4i2dKYWGf | rMSlLb33Gb | KDD.org/2023/Workshop/epiDAMIK/Paper2/-/Official_Review | {"title": "The paper proposes an indirect survey method and the Network Scale-up Method (NSUM) to estimate COVID-19 indicators. While the methodology and data preprocessing are explained well, the lack of detailed discussion on NSUM and omission of related works utilizing NSUM raise concerns about the novelty and completeness of the research.", "review": "In this paper's introduction, the challenge of obtaining reliable COVID-19 data is explained due to the need for target data and costly tests. To address this issue, the authors suggest utilizing an indirect survey method that involves a fixed number of participants. They also propose the NSUM method to estimate various epidemic indicators. However, I have noticed that the NSUM method for constructing a contact network in COVID-19 cases has been utilized before but is not mentioned in the related works.\n\nThe methodology used in the paper is reliable. The survey gathered data from 15 contacts of 1000 participants concerning various COVID-19 indicators. However, the paper did not specify how the participants were selected or if their contacts were mutually exclusive. For instance, if the participants were hospital staff, their contacts would differ from those who work remotely. The paper clearly explained the data preprocessing, utilizing inconsistency filters and univariate outlier detection to eliminate anomalies while accounting for skewed data. Nevertheless, the NSUM method was not extensively discussed.\n\nIn the methodology section, the researchers assessed their findings in the UK and Australia by contrasting them with the official results on the OWID platform. Although the mortality rate differed significantly, Cronbach\u2019s alpha score was high, indicating strong internal consistency. Notably, the vaccination rate result closely matched the actual value. In my opinion, the sample size should have been larger as the filters have significantly decreased the number of sample surveys available.\n\nThe paper is well-organized, with each section thoroughly explained. However, I noticed that the methodology of NSUM is missing, which plays a vital role in constructing the network graph and producing the results. I also came across some related works that utilize NSUM from survey data in COVID-19 cases, which were not included in the relevant work section. Although the problem they were addressing is undoubtedly significant, I remain somewhat skeptical about the novelty of this work.\n\nThis paper does not heavily rely on mathematical formulas or algorithms, making it less technical in nature. However, the equations presented in the paper seem to be accurate to my understanding.\n\nTo summarize, the paper is well-written and presents a clear problem definition. However, the methodology could use further development, and there is a lack of reference to recent work on the same problem.", "rating": "2: Marginally below acceptance threshold", "confidence": "4: The reviewer is confident but not absolutely certain that the evaluation is correct"} | review | 2023 | KDD.org/2023/Workshop/epiDAMIK | A Snapshot of COVID-19 Incidence, Hospitalizations, and Mortality from Indirect Survey Data in China in January 2023 (Extended Abstract) | ["Juan Marcos Ramirez", "Sergio Diaz-Aranda", "Jose Aguilar", "Oluwasegun Ojo", "Rosa Elvira Lillo", "Antonio Fernandez Anta"] | The estimation of incidence has been a crucial component for monitoring COVID-19 dissemination. This has become challenging when official data are unavailable or insufficiently reliable. Hence, the implementation of efficient, inexpensive, and secure techniques that capture information about epidemic indicators is required. This study aims to provide a snapshot of COVID-19 incidence, hospitalizations, and mortality in different countries in January 2023. To this end, we collected data on the number of cases, deaths, vaccinations, and hospitalizations among the fifteen closest contacts to survey respondents. More precisely, indirect surveys were conducted for 100 respondents from Australia on 19 January 2023, 200 respondents from the UK on 19 January 2023, and 1,000 respondents from China between 18-26 January 2023. To assess the incidence of COVID-19, we used a modified version Network Scale-up Method (NSUM) that fixes the number of people in the contact network (reach). We have compared our estimates with official data from Australia and the UK in order to validate our approach. In the case of the vaccination rate, our approach estimates a very close value to the official data, and in the case of hospitalizations and deaths, the official results are within the confidence interval. Regarding the remaining variables, our approach overestimates the values obtained by the Our World in Data (OWID) platform but is close to the values provided by the Officer of National Statistics (ONS) in the case of the UK (within the confidence interval). In addition, Cronbach's alpha gives values that allow us to conclude that the reliability of the estimates in relation to the consistency of the answers is excellent for the UK and good for Australia. Following the same methodology, we have estimated the same metrics for different Chinese cities and provinces. It is worth noting that this approach allows quick estimates to be made with a reduced number of surveys to achieve a wide population coverage, preserving the privacy of the participants. | ["COVID-19", "incidence estimation", "indirect surveys", "NSUM"] | https://openreview.net/forum?id=rMSlLb33Gb | https://openreview.net/pdf?id=rMSlLb33Gb | https://openreview.net/forum?id=rMSlLb33Gb¬eId=O4i2dKYWGf |