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bFsZeXosPQF

BNU_N-7EIR

KDD.org/2023/Workshop/epiDAMIK/Paper12/-/Official_Review

{"title": "Review of Data Collection, Management, Analysis and Decision Support During COVID-19: A Retrospective from The Ohio State University ", "review": "Summary: \nThis paper discusses the large undertaking of collecting, processing, and reporting COVID-19 data from the Ohio State University. This paper makes note of the challenges and missteps faced in data processing, and the lessons learned from this experience during the pandemic. \n\n\nClarity: This paper was clear and easy to follow. \n\nTo improve upon the clarity, I would suggest the following: \n\n--Ensure that the \u201caims\u201d listed are discussed in later sections in the paper. The first aim of \u201ctracking the positivity rate\u201d is not mentioned at any other point in the paper. It is not clear from this paragraph which positivity rate is being tracked (university affiliates?), and whether any weighting scheme was applied to the data. Similarly, the second aim is \u201ccontact tracing\u201d however there is no further discussion of how contact tracing was done and/or recorded. It is unclear whether this is really an aim of the data component, or whether this is considered too far downstream. If both of these were aspects of the data framework, they should be expanded on in the implementation section. \n\n--In the figure 2 schematic, it would be helpful to highlight the data processing / management steps or programs used to convert from the gold test results to the dashboard and contact tracing app. Additional details could be added to this figure.\n\n--The term, \u201chuman infrastructure\u201d is bolded in section 7.4, yet this term is not defined. It would be beneficial to define what this term means in the context of this paper, as this term may not be familiar to many readers. \n\nMinor comments on clarity: \n\n\n--In the \u201cimplementation section\u201d and in figure 2, a number of abbreviations are used that are never spelled out. Writing out these abbreviations would clarify the paper and the data schematic (e.g., IDM, SIS, STFP, TDAI).\n\n\n--The discussion surrounding issues with salesforce data is unclear. The authors mention \u201cuser generated heterogeneity\u201d and \u201cversion control issues\u201d, however the link between those issues and what is causing gaps in data is not fully apparent. \n\n--Figure 1 is not mentioned at all in the paper. It would be useful to include a discussion of who is using / viewing the dashboard and how frequently it was used. That would give an indication of how the data was being used by the community / decision makers at OSU. \n\n\n\nOriginality: The work is original in that it is the only paper to describe the data-driven processes occurring at the Ohio State University. However, many of the points made are not unique, and seem to highlight issues with this data management system. Lessons such as the need to \u201cminimize manual data entry\u201d, work with experts in \u201cevery relevant domain\u201d, and following ethical guidelines regarding data privacy are not ideas original to this project. To highlight the originality of this work, it would be helpful to have a small review of literature section that discusses how this project improves or differs from similar undertakings at large universities. \n\n\n\nSignificance: The significance of this paper could be improved by including more actionable messages to future data systems and teams. Significance could also be improved by noting how this data was used for decision making. One of the goals listed in section 3 is to \u201csupport daily policy decisions\u201d. However, throughout the paper there is little indication of how the data that has been acquired, processed and presented informs decision making. Including additional examples of how this data was used would be very beneficial. The significance would also be boosted by discussing how individual COVID-19 testing data was integrated (if at all) with wastewater data and/or genomic data to inform university policy. \n\n\n\nPros:\n\n--Well written paper\n\n--Concisely and clearly presents aims of a data-driven framework\n\n--Clearly explains many of the pitfalls that can occur in data management, and acknowledges that during the pandemic, some best-practices (such as recording all steps along the way) were not followed due to the need to provide numbers to decision makers.\n\n-- Provides nice examples of when microtrends were useful.\n\n\n\nCons:\n\n--Paper does not provide many actionable steps for using data, or a data-driven approach. Instead, rather broad generalizations are made as to what would be useful (e.g., less manual data entry). \n\n--The implementation section is not informative enough. It would be beneficial to provide more information about the programs used for sorting data, and for moving from one health system to another. \n\n--The figures presented seem disconnected from the text of the paper. Figure 1 should be discussed in the paper, and figure 2 should be expanded to be more descriptive. \n", "rating": "3: Marginally above acceptance threshold", "confidence": "3: The reviewer is fairly confident that the evaluation is correct"}

review

2023

KDD.org/2023/Workshop/epiDAMIK

Pandemic Data Collection, Management, Analysis and Decision Support: A Large Urban University Retrospective

["Namrata Banerji", "Steve Chang", "Andrew Perrault", "Tanya Berger-Wolf", "Mikkel Quam"]

The COVID-19 pandemic has disrupted the world. During this crisis, data has emerged as a critical resource for understanding, monitoring, and mitigating the impact of the disease. We present The Ohio State University's data-driven framework for comprehensive monitoring of the COVID-19 pandemic. We discuss the challenges associated with data collection, investigate the roles and limitations of data analysis in supporting intervention choice and implementation strategies amid the complexities of the pandemic as it unfolded. Balancing privacy, consent, and transparency and ensuring the responsible handling of sensitive information is crucial in maintaining public trust. We examine privacy-preserving techniques, ethical frameworks, and legal regulations aimed at safeguarding individuals' rights while harnessing the power of data. In our experience, conscientious data architecture provided a foundation for meaningful ethical applications of data products, which not only helped mitigate the current crisis, but also can provide valuable insights for better addressing future public health emergencies.

["datasets", "public health", "data management", "ethics"]

https://openreview.net/forum?id=BNU_N-7EIR

https://openreview.net/pdf?id=BNU_N-7EIR

https://openreview.net/forum?id=BNU_N-7EIR&noteId=bFsZeXosPQF

LtzEQpWylBm

BNU_N-7EIR

KDD.org/2023/Workshop/epiDAMIK/Paper12/-/Official_Review

{"title": "OSU Covid-19 Data Retrospective", "review": "# Quality\nThe paper is well written and provides a comprehensive retrospective of OSU's pandemic response\n\n# Clarity\nThe paper seems to offer examples rather than comprehensive descriptions of data. Understandably difficult to cover everything, but in a retrospective like this, comprehensive analysis is going to be more useful.\n\n\n# Originality\nSimilar pandemic response retrospectives exist for other institutions, while interesting seeing OSU's work, originality is low.\n\n\n# Significance\nWhile a good snapshot of the work that occurred at a large scale public university, I feel the lack of originality reduces the overall significance.\n\n\nThe paper offers unique and detailed insight into the Ohio State pandemic response process and data collection, detailing the successes and failures of different applications and the lessons that the university leadership learned in the application of these policies. The lessons detailed would be applicable to another pandemic situation should one arise, making iterations on this faster and producing more useful insights more quickly.\n\nThe paper itself, while interesting to read and learn from, lacks large unique insights, rather agreeing with many other retrospectives with minor shifts in lessons and policies.\n\nNOTE: it looks like sections 6/7 are incorrectly labeled (section 7 uses a \\section{} tag rather than a \\subsection{} tag)\n", "rating": "2: Marginally below acceptance threshold", "confidence": "3: The reviewer is fairly confident that the evaluation is correct"}

review

2023

KDD.org/2023/Workshop/epiDAMIK

Pandemic Data Collection, Management, Analysis and Decision Support: A Large Urban University Retrospective

["Namrata Banerji", "Steve Chang", "Andrew Perrault", "Tanya Berger-Wolf", "Mikkel Quam"]

The COVID-19 pandemic has disrupted the world. During this crisis, data has emerged as a critical resource for understanding, monitoring, and mitigating the impact of the disease. We present The Ohio State University's data-driven framework for comprehensive monitoring of the COVID-19 pandemic. We discuss the challenges associated with data collection, investigate the roles and limitations of data analysis in supporting intervention choice and implementation strategies amid the complexities of the pandemic as it unfolded. Balancing privacy, consent, and transparency and ensuring the responsible handling of sensitive information is crucial in maintaining public trust. We examine privacy-preserving techniques, ethical frameworks, and legal regulations aimed at safeguarding individuals' rights while harnessing the power of data. In our experience, conscientious data architecture provided a foundation for meaningful ethical applications of data products, which not only helped mitigate the current crisis, but also can provide valuable insights for better addressing future public health emergencies.

["datasets", "public health", "data management", "ethics"]

https://openreview.net/forum?id=BNU_N-7EIR

https://openreview.net/pdf?id=BNU_N-7EIR

https://openreview.net/forum?id=BNU_N-7EIR&noteId=LtzEQpWylBm

mYUxvVthARd

BNU_N-7EIR

KDD.org/2023/Workshop/epiDAMIK/Paper12/-/Official_Review

{"title": "Good work for data monitoring and collection", "review": "The paper presents a specific diagram of COVID-19 data tracking, monitoring, and collection. The work is practically meaningful and valuable to various communities for future studies:\n\n1. The paper presents a clear and comprehensive process from collecting test samples to final dashboard exhibitions, which provides valuable paradigm experience for data collecting and processing, particularly for college and education communities.\n\n2. The collected data are valuable for public policy and AI modeling communities. For instance, the work uses Wifi data for individual monitoring and contact tracing, which may help establish contact networks and provide a better understanding of how disease can spread within schools. \n\nDespite the meanings of the data collection process, we wish to understand more about the collected data. For instance, it would be good to include non-private or non-sensitive statistical analysis and visualization of the data for the presentation or the final paper. ", "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

Pandemic Data Collection, Management, Analysis and Decision Support: A Large Urban University Retrospective

["Namrata Banerji", "Steve Chang", "Andrew Perrault", "Tanya Berger-Wolf", "Mikkel Quam"]

The COVID-19 pandemic has disrupted the world. During this crisis, data has emerged as a critical resource for understanding, monitoring, and mitigating the impact of the disease. We present The Ohio State University's data-driven framework for comprehensive monitoring of the COVID-19 pandemic. We discuss the challenges associated with data collection, investigate the roles and limitations of data analysis in supporting intervention choice and implementation strategies amid the complexities of the pandemic as it unfolded. Balancing privacy, consent, and transparency and ensuring the responsible handling of sensitive information is crucial in maintaining public trust. We examine privacy-preserving techniques, ethical frameworks, and legal regulations aimed at safeguarding individuals' rights while harnessing the power of data. In our experience, conscientious data architecture provided a foundation for meaningful ethical applications of data products, which not only helped mitigate the current crisis, but also can provide valuable insights for better addressing future public health emergencies.

["datasets", "public health", "data management", "ethics"]

https://openreview.net/forum?id=BNU_N-7EIR

https://openreview.net/pdf?id=BNU_N-7EIR

https://openreview.net/forum?id=BNU_N-7EIR&noteId=mYUxvVthARd

cT3TaN8D9er

BNU_N-7EIR

KDD.org/2023/Workshop/epiDAMIK/Paper12/-/Official_Review

{"title": "Good experience sharing but not a well-formed research paper", "review": "Summary Of The Review:\n\nThis paper discusses the role of data in managing the COVID-19 pandemic, focusing on its collection, management, analysis, and application in decision-making. The authors present The Ohio State University's data-driven framework for monitoring the pandemic, including data sources, methods, and technologies used for case finding, contact tracing, and visualization. They discuss challenges such as privacy concerns, data quality, and the need for harmonization across different sources. The paper also explores ethical considerations in data usage during the pandemic. The authors highlight the importance of data architecture, teamwork, and ethical frameworks in addressing public health emergencies. The paper concludes with key takeaways and lessons learned for future public health emergencies.\n\nPros: \n1. The authors possess extensive knowledge and experience in managing COVID-19 at The Ohio State University, providing valuable insights and practical examples that can benefit other systems.\n\n2. The paper offers a comprehensive discussion of the university's policies.\n\nCons:\n1. Lack of references and comparisons\n\nThe paper lacks citations to substantiate and compare the findings and approaches presented, which is a notable deficiency. For instance, it is essential to clarify the definition of the \"positivity rate\" in this paper and provide a rationale for its use, which would benefit from external support. Additionally, the paper displays \"R(t) Numbers for Ohio\" in Figure 1, but fails to mention or discuss this important metric in the text, warranting a comprehensive review of the relevant literature to enhance its explanation. Similarly, the utilization of terms like \"gold table\" and \"'gold' data of people\" could be less perplexing if supported by appropriate references.\n\nFurthermore, considering the abundance of existing pandemic surveillance systems, it would be advantageous to examine their operational mechanisms. This examination would enable the identification and comparison of the strengths and weaknesses of the system presented in the paper.\n\n2. Ambiguous statements \n\nSeveral statements in the paper lack clarity and precision, leading to confusion among readers. Additionally, the paper fails to provide a comprehensive summary of the data entries presented in the tables, leaving readers unsure about the specific information contained within them. Moreover, the paper lacks explicit descriptions of tasks, analyses, or well-defined evaluations, despite drawing conclusions and using phrases such as \u201cNo improvement in the accuracy of the analysis of the effect of masking in a given setting\u201d and \u201cAfter careful consideration, it was agreed that singling out a group was often not enough of a value addition or could do more harm than good.\u201d\n\nHere are additional instances of imprecise terminology lacking explicit definitions or thorough evaluations of their scope.\n\nSection 5.1:\n\n\u201cTypographical errors or null values in this identifier column resulted in a non negligible shift in **the summary statistics**, given the **enormous number** of tests conducted. Once the problem had been identified, there was **joint effort to clean it up**, combining **more than four** data streams and reducing the number of unidentified tests to **a number** that would not change **the inference**. Yet, there were still **a few** individually unidentifiable entries in the datasets, albeit **not enough a number** to raise a concern. Minimizing manual entry to data sources can reduce such issues by **a considerable amount**.\u201d\n\nSection 5.2:\n\n\u201cThe data had been migrated from the old table to the new one in theory, but in part **user generated heterogeneity**, as well as version control issues in the HelpSpot source data meant there continued to be **gaps** in the data ingested by Health Cloud (Salesforce) which do not have simple workarounds for analysis of all variables. We maintain **several tables** for the test information storage, but there are **inconsistencies** across those tables. More than one tables exist mainly because we derived simpler versions of tables with many columns that are not relevant for **day-to-day analysis**.\u201d\n\nSection 7.2\n\n\u201c**One group** may in fact be more often in situations involving exposure to infectious persons, or engaged in more risky behavior than others, as we **occasionally** discovered from data analysis. However, available policy level changes **may not have been feasible solutions** and were not always ultimately enacted.\u201d\n\n3. Unaddressed privacy concerns\n\nThe authors argued that they would \u201cexamine privacy-preserving techniques\u201d and \u201csecurity and privacy remained strict requirements\u201d. While in section 7.2, the author also said \u201cwhile it is within the rights of the university to use the WiFi access point information to \u201cfollow\" an individual or to understand who is within the same room, such information has a high \u2019icky factor\u2019 and should be used sparingly.\u201d Despite this, \u201cit was decided to use WiFi data in aggregate to assess population movements rather than individuals\u2019 proximity to other individuals\u201d. Furthermore, the data is \u201cshared\u201d; \u201chealth data were collected and subsequently shared only to the extent they would have \u2019meaningful use\u201d. It would be useful to clarify who it was shared to, what was shared, what training team members had, and describe in more detail the type of data that is collected and disseminated from tracking student\u2019s WiFi locations, seemingly without their knowledge or permission. \n\n4. Other minor problems\n\nFigure 1 in the paper was presented without any accompanying explanations, leading to confusion among readers. The lack of clarification makes it difficult to comprehend the purpose and significance of the \"Personal Protective Equipment\" and \"Enhanced Cleaning\" sections, both of which are represented by equal green circles in the figure.\n\nAlso, \u201cBehavior over analytics\u201c should be section 6.1 rather than section 7.\n\nIn general, the paper offers valuable experience in data management during the COVID-19 pandemic. However, there are several areas that require improvement. First, the paper should include more references and comparisons to support its findings and approaches. Additionally, the analysis section would benefit from a more detailed explanation of the methodologies employed. The clarity and logical presentation of results and takeaways also need to be enhanced. Furthermore, the paper should address privacy concerns associated with the data management practices discussed.", "rating": "1: Ok but not good enough - rejection", "confidence": "3: The reviewer is fairly confident that the evaluation is correct"}

review

2023

KDD.org/2023/Workshop/epiDAMIK

Pandemic Data Collection, Management, Analysis and Decision Support: A Large Urban University Retrospective

["Namrata Banerji", "Steve Chang", "Andrew Perrault", "Tanya Berger-Wolf", "Mikkel Quam"]

The COVID-19 pandemic has disrupted the world. During this crisis, data has emerged as a critical resource for understanding, monitoring, and mitigating the impact of the disease. We present The Ohio State University's data-driven framework for comprehensive monitoring of the COVID-19 pandemic. We discuss the challenges associated with data collection, investigate the roles and limitations of data analysis in supporting intervention choice and implementation strategies amid the complexities of the pandemic as it unfolded. Balancing privacy, consent, and transparency and ensuring the responsible handling of sensitive information is crucial in maintaining public trust. We examine privacy-preserving techniques, ethical frameworks, and legal regulations aimed at safeguarding individuals' rights while harnessing the power of data. In our experience, conscientious data architecture provided a foundation for meaningful ethical applications of data products, which not only helped mitigate the current crisis, but also can provide valuable insights for better addressing future public health emergencies.

["datasets", "public health", "data management", "ethics"]

https://openreview.net/forum?id=BNU_N-7EIR

https://openreview.net/pdf?id=BNU_N-7EIR

https://openreview.net/forum?id=BNU_N-7EIR&noteId=cT3TaN8D9er

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&noteId=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&noteId=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&noteId=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&noteId=170KJ-xfkY

4H1jTVEwu_K

N0qlvDjnEv

KDD.org/2023/Workshop/epiDAMIK/Paper7/-/Official_Review

{"title": "Review of risk-based ring vaccination", "review": "In this paper, the authors investigate a risk-based ring vaccination strategy. Ring vaccination is a vaccine allocation strategy that vaccinates the contacts and contacts-of-contacts of an infected case. Here, the authors use an agent-based model to simulate an Ebola outbreak and test a variant of ring vaccination that prioritizes individuals within the contact-of-contact network with the highest risk (with risks estimated from the model). They show through their simulations that risk-based ring vaccination is significantly more effective than ring vaccination without prioritization, especially when more doses (100 or 200) of the vaccine are available.\n\nStrengths\n+ Risk-based ring vaccination is a nice idea and well-motivated\n+ The authors clearly demonstrate the effectiveness of this strategy through simulations\n+ The model is largely motivated by prior literature and uses parameters from prior work\n\nWeaknesses\n- The results feel almost like a foregone conclusion given the model, since they use risks from the model to decide which individuals to prioritize. It would be useful to establish, especially through mathematical analysis if possible, if we should be \"surprised\" by the results, or the settings that must hold true for risk-based to be significantly more effective. \n- A lot of design decisions are made within the model, eg, levels of contact and types of contact within households/across households, disease parameters, etc. While it helps that parameters were mostly set based on prior literature, it would be useful to conduct sensitivity analyses to see how model results vary based on the decisions made.\n- Unclear if authors were the first to do risk-based ring vaccination. Also, unclear how realistic this model is in real life, since their simulation uses the individual's \"real\" risk from the model to determine prioritization. In reality, it seems hard already to get an infected person's contacts and contacts-of-contacts; would be even harder to know levels of contact/risk between all these people.", "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&noteId=4H1jTVEwu_K

bnqE38fHCY3

N0qlvDjnEv

KDD.org/2023/Workshop/epiDAMIK/Paper7/-/Official_Review

{"title": "Review of the paper on risk-based rink vaccination", "review": "The paper is written and explained very well. The authors have employed agent-based simulation, incorporating six distinct states and separate sampling for household and non-household contacts. The authors have introduced risk based ring vaccination and showed that it is more effective compared to the random allocation and ring allocation. Furthermore, the authors have provided insightful suggestions for potential future research directions, all of which are highly intriguing and would greatly enhance the existing work.\n\nThe assumptions regarding within-household contact appear logical, while estimates for non-household contact draw from a social contact pattern study conducted in Malawi. It's important to talk about why these assumptions and estimates are important and how they affect the proposed vaccine strategy.\n\nIt would be interesting to discuss the C.I. patterns shown in Figure 1. Specifically, we could look at whether the variability decreases for certain vaccine allocation strategies after a certain number of days. Notably, in Figure 1(b), why does the curve based on ring vaccination exhibit such a narrow range between 80 and 100 (around 90-95)?\n\n\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

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&noteId=bnqE38fHCY3

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&noteId=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&noteId=xdAE5_pGjn

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&noteId=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&noteId=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&noteId=tpCKdIce6f

277PStg6hh

qkDCSV-RMt

KDD.org/2023/Workshop/epiDAMIK/Paper1/-/Official_Review

{"title": "Review of paper 1", "review": "This paper aims to study tapering trajectories for patients on long-term opioid therapy. They use longitudinal health data from United HealthGroup and identify 33,620 patients who underwent dose tapering. They apply spectral clustering (a variant by John et al., 2019) to cluster the patients, using variables including their age, gender, monthly average opioid dose, mean baseline dose, tapering trajectory, and adverse events pre-tapering and after tapering initiation. They find 10 clusters and focus on the three largest ones, which exhibit different tapering trajectories and slightly different adverse outcomes, while looking mostly similar on baseline characteristics.\n\nStrengths\n+ The problem is an important one to understand, i.e., the risks and benefits of dose tapering\n+ The dataset is strong and appropriate for the study - they are able to identify over 33,000 patients with dose tapering and longitudinal data\n+ It is interesting that there are different tapering trajectories discovered\n\nWeaknesses/suggestions\n- The goal of the work seems to be 1) to identify common tapering trajectories, 2) to learn the relationship between those trajectories and adverse outcomes. It's not clear to me, then, why clustering on all the variables \u2013 the baseline characteristics, the dose trajectory, and adverse events \u2013 is the right method here. Instead, would it make more sense to do something like, only cluster on dose trajectory, in order to answer (1), and then to fit a model to say, controlling for baseline characteristics, what is the effect of this kind of trajectory on adverse events, in order to answer (2)?\n- The comparison of adverse events across clusters is also difficult to interpret without confidence intervals (Table 3)", "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

Spectral Clustering Identifies High-risk Opioid Tapering Trajectories Associated with Adverse Events

["MONIKA RAY", "Joshua J. Fenton", "Patrick Romano"]

National opioid prescribing guidelines and related quality measures have stimulated changes in opioid prescribing. Studies have shown that rapid dose tapering may be associated with increased opioid-related and mental health events in some patient groups. However, we do not know enough about the trajectories of dose tapering implemented in clinical practice, and how heterogeneous populations of patients respond to different treatments. Our aim was to examine prescribed opioid doses in a large, longitudinal, clinically diverse, national population of opioid-dependent patients with either Medicare or commercial insurance. We performed phenotype clustering to identify unsuspected, novel patterns in the data. In a longitudinal cohort (2008-2018) of 113,618 patients from the OptumLabs Data Warehouse with 12 consecutive months at a high, stable mean opioid dose ($\geq$50 morphine milligram equivalents), we identified 30,932 patients with one dose tapering phase that began at the first 60-day period with $\geq$15\% reduction in average daily dose across overlapping 60-day windows through seven months of follow-up. We applied spectral clustering as we preferred an assumption-free approach with no apriori information being imposed. Spectral clustering identified several cluster-cohorts, with three that included over 98\% of the sample. These three clusters were similar in baseline characteristics, but differed markedly in the magnitude, velocity, duration, and endpoint of tapering. The cluster-cohort characterised by moderately rapid, steady tapering, most often to an end opioid dose of zero, had excess drug-related events, mental health events, and deaths, compared with a cluster characterised by very slow, steady tapering with long-term opioid maintenance. Moderately rapid tapering to discontinuation may be associated with higher risk than slow tapering with longer-term maintenance of opioid analgesia. Furthermore, several clusters highlighted a cohort that had complete taper reversals indicating a treatment failure as the tapering was not maintained. Our findings suggests that identifying subtle yet clinically meaningful patterns in opioid prescribing data, such as patterns within the dose trajectories, can highlight the distinct characteristics separating subpopulations.

["high dose opioids", "spectral clustering", "patient subpopulations", "personalised medicine", "healthcare", "opioid crisis", "phenotype clustering"]

https://openreview.net/forum?id=qkDCSV-RMt

https://openreview.net/pdf?id=qkDCSV-RMt

https://openreview.net/forum?id=qkDCSV-RMt&noteId=277PStg6hh

pIWDwAVUjoy

qkDCSV-RMt

KDD.org/2023/Workshop/epiDAMIK/Paper1/-/Official_Review

{"title": "An interesting approach that may require more supporting analysis", "review": "In this paper, the authors studied the problem of identifying meaningful clinical patterns among patients who had been prescribed opioids and subsequently had the doses reduced over different lengths of time. Overall the paper has several strong aspects\n- It was able to identify several dosage patterns that maybe of interest towards clinical determination\n- The initial analysis seems to point towards differing health outcomes for patients with slow vs rapid tapering (see more below)\n- The paper covered sufficient details about cohort characteristics to let the reviewers judge the impact of the findings\n\nHowever, from a health economic outcome research aspect, the paper is currently at an early stage and may need further followups to support the validity of the identified patterns. The authors have acknowledged the limitation of not considering other factors that may capture the intent to reduce/increase dosing. However, this is a key aspect that may need to be validated, perhaps with certain assumptions such as IPW, to satisfy the significance of the findings. Further, the authors may want to considering survival analysis methods, especially with the possibility of right censored events, to further analyze the clinical outcomes of the identified cohorts. \n\nOverall, this papers has certain promises but may be improved upon from a modeling and analysis aspect.", "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

Spectral Clustering Identifies High-risk Opioid Tapering Trajectories Associated with Adverse Events

["MONIKA RAY", "Joshua J. Fenton", "Patrick Romano"]

National opioid prescribing guidelines and related quality measures have stimulated changes in opioid prescribing. Studies have shown that rapid dose tapering may be associated with increased opioid-related and mental health events in some patient groups. However, we do not know enough about the trajectories of dose tapering implemented in clinical practice, and how heterogeneous populations of patients respond to different treatments. Our aim was to examine prescribed opioid doses in a large, longitudinal, clinically diverse, national population of opioid-dependent patients with either Medicare or commercial insurance. We performed phenotype clustering to identify unsuspected, novel patterns in the data. In a longitudinal cohort (2008-2018) of 113,618 patients from the OptumLabs Data Warehouse with 12 consecutive months at a high, stable mean opioid dose ($\geq$50 morphine milligram equivalents), we identified 30,932 patients with one dose tapering phase that began at the first 60-day period with $\geq$15\% reduction in average daily dose across overlapping 60-day windows through seven months of follow-up. We applied spectral clustering as we preferred an assumption-free approach with no apriori information being imposed. Spectral clustering identified several cluster-cohorts, with three that included over 98\% of the sample. These three clusters were similar in baseline characteristics, but differed markedly in the magnitude, velocity, duration, and endpoint of tapering. The cluster-cohort characterised by moderately rapid, steady tapering, most often to an end opioid dose of zero, had excess drug-related events, mental health events, and deaths, compared with a cluster characterised by very slow, steady tapering with long-term opioid maintenance. Moderately rapid tapering to discontinuation may be associated with higher risk than slow tapering with longer-term maintenance of opioid analgesia. Furthermore, several clusters highlighted a cohort that had complete taper reversals indicating a treatment failure as the tapering was not maintained. Our findings suggests that identifying subtle yet clinically meaningful patterns in opioid prescribing data, such as patterns within the dose trajectories, can highlight the distinct characteristics separating subpopulations.

["high dose opioids", "spectral clustering", "patient subpopulations", "personalised medicine", "healthcare", "opioid crisis", "phenotype clustering"]

https://openreview.net/forum?id=qkDCSV-RMt

https://openreview.net/pdf?id=qkDCSV-RMt

https://openreview.net/forum?id=qkDCSV-RMt&noteId=pIWDwAVUjoy

JZmlmsSvTP-

qkDCSV-RMt

KDD.org/2023/Workshop/epiDAMIK/Paper1/-/Official_Review

{"title": "Spectral Clustering Identifies High-risk Opioid Tapering Trajectories Associated with Adverse Outcomes- Review", "review": "**Summary:**\nThis work explores the characteristics of the prescribed opioid doses in a diverse population of opioid-dependant patients with appropriate insurance and employer of licensed physicians. Namely, the work applies different clustering methods to identify patterns in the data. The dataset used in this work was from the claims data about patients with chronic pain obtained from the largest commercial insurance company and the largest private employer of physicians in the United States. The authors justify that spectral clustering might be a more suitable unsupervised claustering algorithm compared to other clustering methods and thus use that to construct clusters. The authors then explored the characteristics of the patients present in each of these clusters by defining counterfactual terms and other evaluation metrics. Notably, the work discovered different observations related to each of the clusters.\n\n**Strong Points:**\n+ The work provides valuable insights into the characteristics related to patient behaviour towards opioid prescriptions.\n+ The fact that the authors closely describe the remotely relevant prior study (GBTM) and accurately describe the distinctions between that study and the current work attest to the specific contribution of this work.\n+ The work provides extensive summaries of patients across the different clusters over pre-taper and taper initiation and post-taper timestamps. This provides important insights about characterizing patient dose trajectories.\n\n**Weak Points:**\n+ The spectral clustering algorithm used in this work (Spectrum) is a readily available R package. As the authors do not necessarily create this algorithm, it seems pointless to spend an entire subsection on the given algorithm as the characteristic comparison with other clustering algorithms is not unique as well. \n+ I am not sure if the number of clusters given in this work is just like making a mountain out of a molehill. This is because based on Figure 1, there seems to mainly be 2-3 broad clusters. Although the authors provide intuitions for the other clusters, it seems that clusters 3,4 and 5 have very similar trends based on Figure 2. It may be more informative if the authors provided the values of the Eigen-Gap\u00a0statistic for each value of K where K denotes the number of clusters. Maybe GBTM is not as much of an oversimplification as the authors make it to be.\n\n\n**Minor Suggestions:**\n+ Figure 1 needs to be broken down into 2 separate plots. 1 plot for the 3 main clusters and the other for the other clusters. Otherwise the smaller clusters are not even visible.\n+ Minor grammatical errors seem to be present, like in line 737, it should probable be \"Finally, the current data *does* not ...\". ", "rating": "3: Marginally above acceptance threshold", "confidence": "3: The reviewer is fairly confident that the evaluation is correct"}

review

2023

KDD.org/2023/Workshop/epiDAMIK

Spectral Clustering Identifies High-risk Opioid Tapering Trajectories Associated with Adverse Events

["MONIKA RAY", "Joshua J. Fenton", "Patrick Romano"]

National opioid prescribing guidelines and related quality measures have stimulated changes in opioid prescribing. Studies have shown that rapid dose tapering may be associated with increased opioid-related and mental health events in some patient groups. However, we do not know enough about the trajectories of dose tapering implemented in clinical practice, and how heterogeneous populations of patients respond to different treatments. Our aim was to examine prescribed opioid doses in a large, longitudinal, clinically diverse, national population of opioid-dependent patients with either Medicare or commercial insurance. We performed phenotype clustering to identify unsuspected, novel patterns in the data. In a longitudinal cohort (2008-2018) of 113,618 patients from the OptumLabs Data Warehouse with 12 consecutive months at a high, stable mean opioid dose ($\geq$50 morphine milligram equivalents), we identified 30,932 patients with one dose tapering phase that began at the first 60-day period with $\geq$15\% reduction in average daily dose across overlapping 60-day windows through seven months of follow-up. We applied spectral clustering as we preferred an assumption-free approach with no apriori information being imposed. Spectral clustering identified several cluster-cohorts, with three that included over 98\% of the sample. These three clusters were similar in baseline characteristics, but differed markedly in the magnitude, velocity, duration, and endpoint of tapering. The cluster-cohort characterised by moderately rapid, steady tapering, most often to an end opioid dose of zero, had excess drug-related events, mental health events, and deaths, compared with a cluster characterised by very slow, steady tapering with long-term opioid maintenance. Moderately rapid tapering to discontinuation may be associated with higher risk than slow tapering with longer-term maintenance of opioid analgesia. Furthermore, several clusters highlighted a cohort that had complete taper reversals indicating a treatment failure as the tapering was not maintained. Our findings suggests that identifying subtle yet clinically meaningful patterns in opioid prescribing data, such as patterns within the dose trajectories, can highlight the distinct characteristics separating subpopulations.

["high dose opioids", "spectral clustering", "patient subpopulations", "personalised medicine", "healthcare", "opioid crisis", "phenotype clustering"]

https://openreview.net/forum?id=qkDCSV-RMt

https://openreview.net/pdf?id=qkDCSV-RMt

https://openreview.net/forum?id=qkDCSV-RMt&noteId=JZmlmsSvTP-

9VUhOECGKJt

qkDCSV-RMt

KDD.org/2023/Workshop/epiDAMIK/Paper1/-/Official_Review

{"title": "Accept", "review": "In this study, the authors applied spectral clustering to identify high-risk opioid tapering trajectories associated with adverse outcomes using a large-scale dataset. The study addressed an important public health issue and discovered patterns of opioid tapering using unsupervised learning. The findings can support further studies on dose tapering patterns to inform future prescribing policies and clinical practice. A couple of areas can be improved in the current manuscript.\n1. More details should be provided on creating the similarity matrix and the Laplacian matrix. A few references were mentioned but without technical details.\n2. Variables in this study have different units and scales. How did the author deal with the different scales? Did it matter if those variables were standardized?\n3. Certain patients may use dose tapering strategies based on their health conditions. For instance, slower tapering may be used because the doctor believes the patient may have a higher risk of adverse outcomes under rapid tapering. In future studies, it would be better to control those factors to disentangle the impact of tapering patterns. A discussion on this point is warranted.\n", "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

Spectral Clustering Identifies High-risk Opioid Tapering Trajectories Associated with Adverse Events

["MONIKA RAY", "Joshua J. Fenton", "Patrick Romano"]

National opioid prescribing guidelines and related quality measures have stimulated changes in opioid prescribing. Studies have shown that rapid dose tapering may be associated with increased opioid-related and mental health events in some patient groups. However, we do not know enough about the trajectories of dose tapering implemented in clinical practice, and how heterogeneous populations of patients respond to different treatments. Our aim was to examine prescribed opioid doses in a large, longitudinal, clinically diverse, national population of opioid-dependent patients with either Medicare or commercial insurance. We performed phenotype clustering to identify unsuspected, novel patterns in the data. In a longitudinal cohort (2008-2018) of 113,618 patients from the OptumLabs Data Warehouse with 12 consecutive months at a high, stable mean opioid dose ($\geq$50 morphine milligram equivalents), we identified 30,932 patients with one dose tapering phase that began at the first 60-day period with $\geq$15\% reduction in average daily dose across overlapping 60-day windows through seven months of follow-up. We applied spectral clustering as we preferred an assumption-free approach with no apriori information being imposed. Spectral clustering identified several cluster-cohorts, with three that included over 98\% of the sample. These three clusters were similar in baseline characteristics, but differed markedly in the magnitude, velocity, duration, and endpoint of tapering. The cluster-cohort characterised by moderately rapid, steady tapering, most often to an end opioid dose of zero, had excess drug-related events, mental health events, and deaths, compared with a cluster characterised by very slow, steady tapering with long-term opioid maintenance. Moderately rapid tapering to discontinuation may be associated with higher risk than slow tapering with longer-term maintenance of opioid analgesia. Furthermore, several clusters highlighted a cohort that had complete taper reversals indicating a treatment failure as the tapering was not maintained. Our findings suggests that identifying subtle yet clinically meaningful patterns in opioid prescribing data, such as patterns within the dose trajectories, can highlight the distinct characteristics separating subpopulations.

["high dose opioids", "spectral clustering", "patient subpopulations", "personalised medicine", "healthcare", "opioid crisis", "phenotype clustering"]

https://openreview.net/forum?id=qkDCSV-RMt

https://openreview.net/pdf?id=qkDCSV-RMt

https://openreview.net/forum?id=qkDCSV-RMt&noteId=9VUhOECGKJt

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&noteId=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&noteId=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&noteId=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&noteId=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&noteId=O4i2dKYWGf