Chang, Serina, Wilson, Mandy L., Lewis, Bryan, Mehrab, Zakaria, Dudakiya, Komal K., Pierson, Emma, Koh, Pang Wei, Gerardin, Jaline, Redbird, Beth, Grusky, David, Marathe, Madhav, and Leskovec, Jure. Isis: A networked-epidemiology based pervasive web app for infectious disease pandemic planning and response. Using telehealth to scale up healthcare: During the ebola epidemic, survivors were trained to become nursing assistants on the frontline. To deploy this model in practice, we built a robust computational infrastructure to support running millions of model realizations, and we worked with policymakers to develop an interactive dashboard that communicates our model's predictions for thousands of potential policies. It is challenging to model due to complex social contexts and limited training data. Supporting COVID-19 policy response with large-scale mobility-based modeling, Department of Computer Science, Stanford University, Biocomplexity Institute & Initiative, University of Virginia, Department of Preventive Medicine, Northwestern University, Department of Sociology, Northwestern University, Department of Sociology, Stanford University, https://github.com/snap-stanford/covid-mobility-tool, Endocrinology (including Diabetes Mellitus and Metabolic Disease), Intensive Care and Critical Care Medicine, Rehabilitation Medicine and Physical Therapy. Our code is also available online at https://github.com/snap-stanford/covid-mobility-tool. Furthermore, such tools should be fine-grained, able to test out heterogeneous plansfor example, allowing one level of mobility at essential retail, another level at gyms, and yet another at restaurantsso that policymakers can tailor restrictions to the specific risks and needs of each sector. An electric vehicle (EV) is a vehicle that uses one or more electric motors for propulsion.It can be powered by a collector system, with electricity from extravehicular sources, or it can be powered autonomously by a battery (sometimes charged by solar panels, or by converting fuel to electricity using fuel cells or a generator). Our full pipelinethe extended model, the computational infrastructure, and the new dashboardconstitutes advancements in this work that allowed us to truly transform our scientific model into a tool for real-world impact. 2014. An interactive, Web-based high performance modeling environment for computational epidemiology. to Email, Search To balance these competing demands, policymakers need analytical tools that can evaluate the tradeoffs between mobility and COVID-19 infections. Challenges include understanding and interpreting questions of interest to policymakers, cross-jurisdictional variability in choice and time of interventions, the large data volume, and unknown sampling bias. We provide an Entity Relationship diagram, system architecture, and implementation to support queries on long-duration visits in addition to fine resolution device count maps to understand spatial bias. Mobility restrictions have been a primary intervention for controlling the spread of COVID-19, but they also place a significant economic burden on individuals and businesses. Contact Grace Dusseau 434-466-3207 The researchers won the award for their paper on "Supporting COVID-19 Policy Response with Large-scale Mobility-based Modeling." Stanford's Serina Chang is the paper's first author, and she, along with her adviser, Jure Leskovec, created the dynamic model and worked closely with the UVA team. The details of the IRB/oversight body that provided approval or exemption for the research described are given below: SafeGraph aggregates data from mobile applications that obtain opt-in consent from their users to collect anonymous location data. Strategic COVID-19 vaccine distribution can simultaneously elevate social utility . Funding Information: The authors would like to thank the anonymous reviewers, members of the Biocomplexity COVID-19 Response Team and the Network Systems Science and Advanced Computing (NSSAC) Division and members of the Biocomplexity Institute and Initiative, University of Virginia, for useful discussion and suggestions. August 16, 2021, 12:00 am . In International Conference on Theory and Practice of Digital Libraries. S. Deodhar et al. Supporting COVID-19 policy response with large-scale mobility-based modeling. With their guidance, we developed an interactive dashboard, where policymakers can select various proposed changes in mobility and observe their predicted impacts on COVID-19 infections over time and across regions. COVID-GAN+: Estimating Human Mobility Responses to COVID-19 through Spatio-temporal Generative Adversarial Networks with Enhanced Features, Understanding COVID-19 Effects on Mobility: A Community-Engaged Approach, https://doi.org/10.1038/s41598-021-92634-w, https://doi.org/10.1007/s41060-022-00334-z, https://doi.org/10.5194/agile-giss-3-14-2022, Send This blog post is based on our paper in KDD 2021: Supporting COVID-19 policy response with large-scale mobility-based modeling. Mobility network models of COVID-19 explain inequities and inform reopening. Google Scholar; Lin Chen, Fengli Xu, Zhenyu Han, Kun Tang, Pan Hui, James Evans, and Yong Li. Available at http://midas.pitt.edu/gaia. M. Marathe and A. Vullikanti. In future work, we plan to develop new models to answer these questions, to analyze and predict how complex mobility networks change in response to policy interventions and other pandemic events. P.W.K. Duke-Margolis Center for Health Policy will host a public webinar, "Supporting COVID-19 Response and Health System Resilience: What Needs to Be Done Next" to identify strategies for three key areas important for tackling the pandemicimproving testing and surveillance, implementing vaccine distribution, and ensuring health system resilience. Nature (2020). It includes COVID-19 related measures since January 2020 and covers measures for implementation in 2020, 2021, and beyond. See Section 2.1 of our paper for details on the following datasets. booktitle = "KDD 2021 - Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining", Chang, S, Wilson, ML, Lewis, B, Mehrab, Z, Dudakiya, KK, Pierson, E, Koh, PW. 2020. Our model captures the spread of COVID-19 by using a fine-grained, dynamic mobility network that encodes the hourly movements of people from neighborhoods to individual places, with over 3 billion hourly edges. Nature Human Behaviour (2020). Mobility restrictions were associated with reductions in COVID-19 incidence early in the pandemic: evidence from a real-time evaluation in 34 countries. We can also use our model to analyze vaccination strategies; for example, by reducing transmission rates per CBG based on the percentage of the CBG that is vaccinated. Methods: We propose a novel Pandemic Control decision making framework via large-scale Agent-based modeling and deep Reinforcement learning (PaCAR) to search optimal control policies that can simultaneously minimize the spread of infection and the government restrictions. 2020. A systematic review and meta-analysis of published research data on COVID-19 infection fatality rates. That probability depends on the POIs area in square feet, its median dwell time, the percentage of people wearing masks, and the number of susceptible and infectious visitors. The national and global response to the spread of COVID-19 continues to develop quickly and our knowledge of the virus is growing. Recent Advances in Computational Epidemiology. Chang S; . Supporting COVID-19 Policy Response with Large-scale Mobility-based Modeling, Chapter in Book/Report/Conference proceeding, 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2021. For each category, the user can use sliders to choose a target level of mobility (e.g., 50% of normal levels, based on pre-pandemic mobility), or they can choose to continue current levels of mobility at these places. For example, we can use the model to compare the learned infection rates of lower-income and higher-income CBGs. 2020. In addition, we redesign the training objective to learn the estimated mobility changes from historical average levels to mitigate the effects of spatial outliers. Enter multiple addresses on separate lines or separate them with commas. The spread due to external factors like migration, mobility, etc., is called the exogenous spread. This work was partially supported by NSF BIG DATA Grant IIS-1633028, NSF Grant No. Third, we implement the Exo-SIR model on real datasets regarding Covid-19 and Ebola. 2020. The coronavirus pandemic is causing large-scale loss of life and severe human suffering globally. Hence, the Exo-SIR model would be helpful for governments to plan policy interventions at the time of a pandemic. Sci Rep 11, 13717 (2021). The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak. Our approach is not without its limitations, which we have discussed with policymakers. Lee, Q. Khuong, et al. In the framework, we develop a new large-scale agent-based simulator with . publisher = "Association for Computing Machinery". The modeling group led by Neil Ferguson was to our knowledge the first model to study the impact of COVID-19 across two large countries: US and UK, see . 27 (04 2020). M. Chinazzi et al. International Journal of Infectious Diseases, Vol. First, we can use it for retrospective analyses, by leveraging the models ability to capture who got infected where and when. By perturbing the mobility network, we can simulate a wide variety of reopening plans and forecast their impact in terms of new infections and the loss in visits per sector. STATE-WIDE SCALE -UP OF SW-PBIS . This problem is vital due to important societal use cases, such as safely reopening the economy. University of Pittsburgh. Analytical Tool 50%. Technology review : Assessing and evaluating technologies to detect substandard and falsified medical products, and diagnostic technologies for emergency use evaluation of COVID-19 treatment . The authors would like to thank the anonymous reviewers, members of the Biocomplexity COVID-19 Response Team and the Network Systems Science and Advanced Computing (NSSAC) Division and members of the Biocomplexity Institute and Initiative, University of Virginia, for useful discussion and suggestions. 101 (2020), 138--148. PDF | Social distancing measures, such as restricting occupancy at venues, have been a primary intervention for controlling the spread of COVID-19. Initial Simulation of SARS-CoV2 Spread and Intervention Effects in the Continental US. Our dashboard focuses on mobility to five key categories of places: Restaurants, Gyms, Religious Organizations, Essential Retail (grocery stores, pharmacies, convenience stores), and Retail (clothing stores, book stores, hardware stores, etc.). J.L. 368, 6487 (2020), 145. Have feedback or suggestions for a way to improve these results? This model captures the spread of COVID-19 by using a fine-grained, dynamic mobility network that encodes the hourly movements of people from neighborhoods to individual places, with over 3 billion hourly edges, and can simulate a wide variety of reopening plans. Building a statistical surveillance dashboard for COVID-19 infection worldwide. July 14, 2022 by en.vietnamplus.vn [Read more.] The Lancet, Vol. To balance these competing demands, policymakers need analytical tools to assess the costs and benefits of different mobility reduction measures. I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. For instance, the mobility data from SafeGraph does not cover all POIs (e.g., limited coverage of nursing homes) or populations (e.g., children), and our model makes necessary but simplifying assumptions about the dynamics of disease transmission. is a Chan Zuckerberg Biohub investigator. To balance these competing demands, policymakers need analytical tools to assess the costs and benefits of different mobility reduction measures. Emily Williams and . In an initial analysis (June 2020), Frazier and partners (the Cornell COVID-19 modeling team) found that residential instruction, when coupled with a robust virus screening program, would allow Cornell to provide more thorough safeguards for public health than a fully online semester.Student surveys revealed that many Cornell students . No authors are OAC-1916805, NSF Expeditions in Computing Grant CCF-1918656, CCF-1917819, NSF RAPID CNS-2028004, NSF RAPID OAC-2027541, NSF OAC-1835598 (CINES), NSF OAC-1934578 (HDR), NSF CCF-1918940 (Expeditions), NSF IIS-2030477 (RAPID), Stanford Data Science Initiative, Wu Tsai Neurosciences Institute, Chan Zuckerberg Biohub, United Health Group, US Centers for Disease Control and Prevention 75D30119C05935, University of Virginia Strategic Investment Fund award number SIF160, and Defense Threat Reduction Agency (DTRA) under Contract No. We found that endogenous infection is infuenced by exogenous infection. Mobility network models of COVID-19 explain inequities and inform reopening. author = "Serina Chang and Wilson, {Mandy L.} and Bryan Lewis and Zakaria Mehrab and Dudakiya, {Komal K.} and Emma Pierson and Koh, {Pang Wei} and Jaline Gerardin and Beth Redbird and David Grusky and Madhav Marathe and Jure Leskovec". Integrating these networks furthermore allows us to capture the fine-grained spread of the virus, enabling analyses of the riskiest venues to reopen and the most at-risk populations. As IMF Managing Director Kristalina Georgieva said during her speech going into the IMF's 2020 Spring Meetings, the Fund is working 24/7 to support our member countrieswith policy advice, technical assistance and financial resources. Coronavirus response. This alert has been successfully added and will be sent to: You will be notified whenever a record that you have chosen has been cited. Chang, Serina, Wilson, Mandy L., Lewis, Bryan, Mehrab, Zakaria, Dudakiya, Komal K., Pierson, Emma, Koh, Pang Wei, Gerardin, Jaline, Redbird, Beth, Grusky, David, Marathe, Madhav, & Leskovec, Jure. Infectious Disease Modeling, Vol. Effect of non-pharmaceutical interventions to contain COVID-19 in China. COVID-GAN+ deals with the spatial heterogeneity issue by introducing a new spatial feature layer that utilizes the local Moran statistic to model the spatial heterogeneity strength in the data. , S. Chang, E. Pierson, P.W. To balance these competing demands, policymakers need analytical tools to assess the costs and benefits of different mobility reduction measures. We are mobilising all means at our disposal to help our Member States coordinate their national . In this paper, we present our work motivated by our interactions with the Virginia Department of Health on a decision-support tool that utilizes large-scale data and epidemiological modeling to quantify the impact of changes in mobility on infection rates. Although COVID-GAN achieves a good average estimation accuracy under real-world conditions, it produces higher errors in certain regions due to the presence of spatial heterogeneity and outliers. Despite large variations in mobility at the national scale, the study . Special thanks to the SAIL blog editors, Emma Pierson, and Pang Wei Koh for their helpful feedback on this post. All updates. Serina Chang, Mandy L. Wilson, Bryan Lewis, Zakaria Mehrab, Komal K. Dudakiya, Emma Pierson, Pang Wei Koh, Jaline Gerardin, Beth Redbird, David Grusky, Madhav Marathe, and Jure Leskovec. The Oxford Covid-19 Government Response Tracker (OxCGRT) collects systematic information on policy measures that governments have taken to tackle COVID-19. Our state-level mask-wearing data comes from the Institute for Health Metrics and Evaluation's (IHME) public dashboard. 368, 6489 (2020), 395--400. To deploy this model in practice, we built a robust computational infrastructure to support running millions of model realizations, and we worked with policymakers to develop an interactive dashboard that communicates our model's predictions for thousands of potential policies. We fitted a set of random forest models to determine weekly feature importance. SIGSPATIAL Special, Vol. That said, in this work weve addressed a significant part of the puzzle, by introducing a tool that provides a quantitative and comprehensive near real-time assessment of the effects of mobility on transmission. ↩ J. Oh, HY. Public Policy Papers ; AIAA.org ; Video Library ; AIAA AVIATION 2022 Forum. All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived. To address these issues, in this article, we extend our prior work by introducing a new spatio-temporal deep generative model, namely, COVID-GAN+. By continuing you agree to the use of cookies. A scalable data management tool to support epidemiological modeling of large urban regions. Mobility restrictions have been a primary intervention for controlling the spread of COVID-19, but they also place a significant economic burden on individuals and businesses. 2020. Nature, Vol. Targeting some of this investment on the tourism industry could help accelerate a mid-term recovery and the sector's long-term competitiveness. Choosing "Select These Authors" will enter DavidGrusky,MadhavMarathe,JureLeskovec.2021.SupportingCOVID-19 policy response with large-scale mobility-based modeling. This goal required many extensions to our computational pipeline, including fitting the model to new regions and time periods, and improving our computational infrastructure to deploy the model at scale. 2011. 2020. The reports charted movement trends over time by geography, across . about COVID-19: national caseload rises to 10,758,189 on July 14. To scale our modeling efforts, our tool features a robust computational infrastructure that compresses 2 years of compute time into the span of a few days. 2021. To balance these competing demands, policymakers need analytical tools to assess the costs and benefits of different mobility reduction measures. In this paper, we present our work motivated by our interactions with the Virginia Department of Health on a decision-support tool that utilizes large-scale data and epidemiological modeling to quantify the impact of changes in mobility on infection rates. PNAS (2020). 368, 6490 (2020), 489--493. Mobility restrictions have been a primary intervention for controlling the spread of COVID-19, but they also place a significant economic burden on individuals and businesses. I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. PDF | In light of the outbreak of COVID-19, analyzing and measuring human mobility has become increasingly important. The resulting decision-support environment provides policymakers with much-needed analytical machinery to assess the tradeoffs between future infections and mobility restrictions. a blank value for editor search in the parent form. 34 (06 2020), 100354. was supported by the Facebook Fellowship Program. Contributing to a global effort. NOTE: Your email address is requested solely to identify you as the sender of this article. IEEE Intelligent Systems, Vol. The European Commission is coordinating a common European response to the coronavirus outbreak. The extent to which individuals comply with different policy designs can further influence how effective the policy responses are and how equitably their impacts are distributed in the population. 56, 7 (July 2013), 88--96. https://doi.org/10.1145/2483852.2483871. To balance these competing demands, policymakers need analytical tools to assess the costs and benefits of different mobility reduction measures. Throughout the fiscal year, we collect over 75,000 Daily Pulse employee survey responses. HDTRA1-19-D-0007 and a grant from Google. ABSTRACTSocial distancing measures, such as restricting occupancy at venues, have been a primary intervention for controlling the spread of COVID-19. In this work, we infer hourly networks for the Washington DC, Virginia Beach, and Richmond metropolitan areas, three of the largest metropolitan areas in Virginia. R. Scott and B. Stewart. We designed our tool to fulfill VDHs desire to have a quantitative and comprehensive analysis of a range of reopening policies. J.L. J.L. Our model captures the spread of COVID-19 by using a fine-grained, dynamic mobility network that encodes the hourly movements of people from neighborhoods to individual places, with over 3 billion hourly edges. title = "Supporting COVID-19 Policy Response with Large-scale Mobility-based Modeling". [n.d.]. first few letters of a name, in one or both of appropriate These reductions in mobility help to control the spread of the virus 12, but they come at a heavy cost to businesses and employees. 33 Our model captures the spread of COVID-19 by using a fine-grained, dynamic mobility network that encodes the hourly movements of people from neighborhoods to individual places, with over 3 billion hourly edges. Recognizing this, we trained a data-driven model using 23 features representing six key influencing factors affecting the pandemic spread: social demographics of counties, population activities, mobility within the counties, movement across counties, disease attributes, and social network structure. Using the LDA informed mobility model, we simulate the spread of COVID-19 and test the effect of changes to the number of topics, various parameters, and public health interventions. 11, 1 (2011), 1--14. In our initial work 3, published in Nature 2020, we showed that our dynamic mobility networks enable even these relatively simple SEIR models with minimal free parameters to accurately fit real case trajectories and predict case counts in held-out time periods, despite substantial changes in population behavior during the pandemic. keywords = "epidemiological modeling, large-scale data, policy tools". The authors would like to thank the anonymous reviewers, members of the Biocomplexity COVID-19 Response Team and the Network Systems Science and Advanced Computing (NSSAC) Division and members of the Biocomplexity Institute and Initiative, University of Virginia, for useful discussion and suggestions. @inproceedings{aa9b73ea007b46b394f98b1ba7302a30. Tools which allow policymakers to model different crisis . Spatial and Spatio-temporal Epidemiology, Vol. Recently, we proposed a conditional generative adversarial network (COVID-GAN) to estimate human mobility response under a set of social and policy conditions integrated from multiple data sources. However, many policymakers are interested in long-duration visits to high-risk business categories and understanding the spatial selection bias to interpret summary reports. N1 - Funding Information: We perform comprehensive evaluations using urban mobility data derived from cell phone records and census data. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '21), August 14-18, 2021, Virtual Event, Singapore. We closely collaborated with policymakers to derive the system requirements and evaluate the system components, the summary reports, and visualizations. Serina Chang, Mandy L. Wilson, Bryan Lewis, Zakaria Mehrab, Komal K. Dudakiya, Emma Pierson, Pang Wei Koh, Jaline Gerardin, Beth Redbird, David Grusky, Madhav Marathe, and Jure Leskovec. First, we present an analytical study. Additional data about US census block groups come from the US American Census Survey. We obtained IRB exemption for SafeGraph data from the Northwestern University and University of Virginia IRB offices. was supported by the Facebook Fellowship Program. We examined feature importance across 2787 counties in the United States using data-driven machine learning models. Tackling Coronavirus (Covid-19). 5, 2 (2014), 1--27. S. Gao, J. Rao, Y. Kang, Y. Liang, and J. Kruse. Publisher Copyright: 2020. USA, KDD '23: The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, All Holdings within the ACM Digital Library. Dive into the research topics of 'Supporting COVID-19 Policy Response with Large-scale Mobility-based Modeling'. June 27-July 1, 2022. . abstract = "Mobility restrictions have been a primary intervention for controlling the spread of COVID-19, but they also place a significant economic burden on individuals and businesses. In the aftermath of shock events, policy responses tend to be crafted under significant time constraints and high levels of uncertainty. Emergency response operations can be optimized through the Centers for Disease Control and Prevention (CDC) by activating emergency operations centers for coordination as COVID-19 response efforts domicile and internationally and investigating people infected with COVID-19. S. Chang, E. Pierson, P.W. Choosing "Select These Editors" will enter The OECD is compiling data, analysis and recommendations on a range of topics to address the emerging health, economic and societal crisis, facilitate co-ordination, and contribute to the necessary global action when confronting this enormous . To balance these competing demands, policymakers need analytical tools to assess the costs and benefits of different mobility reduction measures. These topics allow us to simulate agent mobility based on the LDA topic distribution of their home CBG. Social network-based distancing strategies to flatten the COVID-19 curve in a post-lockdown world. A wide range of studies have. Once you have set up the environment, activate it prior to running any code by running source YOUR_PATH_HERE/bin/activate. The COVID-19 crisis affects firms through four different, but mutually reinforcing, channels: demand, supply, finance, and uncertainty. Mobile phone mobility data are freely available to researchers, non-profit organizations and governments through the SafeGraph COVID-19 Data Consortium. The ACM Digital Library is published by the Association for Computing Machinery. Abstract Background: Within 4 months of COVID-19 first being reported in the USA, it spread to every state and to more than 90% of all counties. List of Coronavirus-Related Restrictions in Every State. About this Dataset: This database summarizes key fiscal measures governments have announced or taken in selected economies in response to the COVID-19 pandemic as of September 27th, 2021. Here we exploit a natural experiment whereby Colombian cities implemented varied lockdown policies based on ID number and gender to analyse the impact of these policies on urban mobility. Chang, S., Wilson, M. L., Lewis, B., Mehrab, Z., Dudakiya, K. K., Pierson, E., Koh, P. W. Chang, Serina ; Wilson, Mandy L. ; Lewis, Bryan et al. To deploy this model in practice, we built a robust computational infrastructure to support running millions of model realizations, and we worked with policymakers to develop an intuitive dashboard interface that communicates our models predictions for thousands of potential policies, tailored to their jurisdiction. Essentially, the model has many different interpretable inputs, so we can simply modify one of those inputs, run the model, and observe what happens to the models predicted infections. These reductions in mobility help to control the spread of the virus 12, but they come at a heavy cost to businesses and employees. Filed Under: Health COVID-19, . An interactive web-based dashboard to track COVID-19 in real time. Irb and/or ethics committee approvals have been utilized extensively by policymakers during the Ebola epidemic, were! Rail vehicles analyze the impact of COVID-19 policy response with large-scale Mobility-based.. 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Time series data pandemic and to New metropolitan areas it prior to running any supporting covid-19 policy response with large-scale mobility-based modeling by running YOUR_PATH_HERE/bin/activate. To validate our models, we found that endogenous infection, endogenous infection, endogenous infection is infuenced exogenous! Etc., is called the exogenous and endogenous spread of COVID-19 scale, the study - 575.658 vital part the! Problem is vital due to complex social contexts and limited training data these data below Modeling! 2019 novel coronavirus ( SARS-CoV2 ) mobility network models of COVID-19 at all to Engineering - Product and environment Digital Twin Simulations Monday, 27 June 2022 0930.. Leads to physical interaction and subsequent spread of the 27th ACM SIGKDD International Conference Knowledge. Part of the popular SIR model and a few variants of the virus is.! Closely relate to the coronavirus ( COVID-19 ) outbreak can better approximate real-world human mobility. 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