Supporting COVID-19 Policy Response with Large-scale Mobility-based Modeling

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; Jure Leskovec

doi:10.1145/3447548.3467182

Supporting COVID-19 Policy Response with Large-scale Mobility-based Modeling

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, Jure Leskovec

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

26 Scopus citations

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. 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. 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. 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. 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.

Original language	English (US)
Title of host publication	KDD 2021 - Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Publisher	Association for Computing Machinery
Pages	2632-2642
Number of pages	11
ISBN (Electronic)	9781450383325
DOIs	https://doi.org/10.1145/3447548.3467182
State	Published - Aug 14 2021
Event	27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2021 - Virtual, Online, Singapore Duration: Aug 14 2021 → Aug 18 2021

Publication series

Name	Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

Conference	27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2021
Country/Territory	Singapore
City	Virtual, Online
Period	8/14/21 → 8/18/21

Funding

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. This work was partially supported by NSF BIG DATA Grant IIS-1633028, NSF Grant No. 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, Defense Threat Reduction Agency (DTRA) under Contract No. HDTRA1-19-D-0007 and a grant from Google. S.C. was supported by an NSF Graduate Fellowship. P.W.K. was supported by the Facebook Fellowship Program. E.P. is supported by a Google Research Scholar award. J.L. is a Chan Zuckerberg Biohub investigator. Any opinions,ndings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reect the views of the funding agencies. 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.

Keywords

epidemiological modeling
large-scale data
policy tools

ASJC Scopus subject areas

Software
Information Systems

Access to Document

10.1145/3447548.3467182

Cite this

Chang, S., Wilson, M. L., Lewis, B., Mehrab, Z., Dudakiya, K. K., Pierson, E., Koh, P. W., Gerardin, J., Redbird, B., Grusky, D., Marathe, M., & Leskovec, J. (2021). Supporting COVID-19 Policy Response with Large-scale Mobility-based Modeling. In KDD 2021 - Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 2632-2642). (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). Association for Computing Machinery. https://doi.org/10.1145/3447548.3467182

Chang, Serina ; Wilson, Mandy L. ; Lewis, Bryan et al. / Supporting COVID-19 Policy Response with Large-scale Mobility-based Modeling. KDD 2021 - Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, 2021. pp. 2632-2642 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

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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. 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. 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. 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. 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.",

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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",

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Chang, S, Wilson, ML, Lewis, B, Mehrab, Z, Dudakiya, KK, Pierson, E, Koh, PW, Gerardin, J , Redbird, B, Grusky, D, Marathe, M & Leskovec, J 2021, Supporting COVID-19 Policy Response with Large-scale Mobility-based Modeling. in KDD 2021 - Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery, pp. 2632-2642, 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2021, Virtual, Online, Singapore, 8/14/21. https://doi.org/10.1145/3447548.3467182

Supporting COVID-19 Policy Response with Large-scale Mobility-based Modeling. / Chang, Serina; Wilson, Mandy L.; Lewis, Bryan et al.
KDD 2021 - Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, 2021. p. 2632-2642 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Pierson, Emma

AU - Koh, Pang Wei

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Chang S, Wilson ML, Lewis B, Mehrab Z, Dudakiya KK, Pierson E et al. Supporting COVID-19 Policy Response with Large-scale Mobility-based Modeling. In KDD 2021 - Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery. 2021. p. 2632-2642. (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). doi: 10.1145/3447548.3467182

Supporting COVID-19 Policy Response with Large-scale Mobility-based Modeling

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Publication series

Conference

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Keywords

ASJC Scopus subject areas

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