TY - GEN
T1 - Supporting COVID-19 Policy Response with Large-scale Mobility-based Modeling
AU - Chang, Serina
AU - Wilson, Mandy L.
AU - Lewis, Bryan
AU - Mehrab, Zakaria
AU - Dudakiya, Komal K.
AU - Pierson, Emma
AU - Koh, Pang Wei
AU - Gerardin, Jaline
AU - Redbird, Beth
AU - Grusky, David
AU - Marathe, Madhav
AU - Leskovec, Jure
N1 - 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. 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.
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.
Publisher Copyright:
© 2021 ACM.
PY - 2021/8/14
Y1 - 2021/8/14
N2 - 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.
AB - 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.
KW - epidemiological modeling
KW - large-scale data
KW - policy tools
UR - http://www.scopus.com/inward/record.url?scp=85114925479&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85114925479&partnerID=8YFLogxK
U2 - 10.1145/3447548.3467182
DO - 10.1145/3447548.3467182
M3 - Conference contribution
AN - SCOPUS:85114925479
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 2632
EP - 2642
BT - KDD 2021 - Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
PB - Association for Computing Machinery
T2 - 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2021
Y2 - 14 August 2021 through 18 August 2021
ER -