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Title: Interplay of global multi-scale human mobility, social distancing, government interventions, and COVID-19 dynamics
This work quanti es mobility changes observed during the di erent phases of the pandemic world-wide at multiple resolutions { county, state, country { using an anonymized aggregate mobility map that captures population ows between geographic cells of size 5 km2. As we overlay the global mobility map with epidemic incidence curves and dates of government interventions, we observe that as case counts rose, mobility fell and has since then seen a slow but steady increase in ows. Further, in order to understand mixing within a region, we propose a new metric to quantify the e ect of social distancing on the basis of mobility.Taking two very di erent countries sampled from the global spectrum, We analyze in detail the mobility patterns of the United States (US) and India. We then carry out a counterfactual analysis of delaying the lockdown and show that a one week delay would have doubled the reported number of cases in the US and India. Finally, we quantify the e ect of college students returning back to school for the fall semester on COVID-19 dynamics in the surrounding community. We employ the data from a recent university outbreak (reported on August 16, 2020) to infer possible Re values and mobility ows combined with daily prevalence data and census data to obtain an more » estimate of new cases that might arrive on a college campus. We nd that maintaining social distancing at existing levels would be e ective in mitigating the extra seeding of cases. However, potential behavioral change and increased social interaction amongst students (30% increase in Re ) along with extra seeding can increase the number of cases by 20% over a period of one month in the encompassing county. To our knowledge, this work is the rst to model in near real-time, the interplay of human mobility, epidemic dynamics and public policies across multiple spatial resolutions and at a global scale. « less
Authors:
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Award ID(s):
1633028 1443054 1916805 1918656
Publication Date:
NSF-PAR ID:
10213762
Journal Name:
medRxiv
Sponsoring Org:
National Science Foundation
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