Pullano, Giulia

English

ISU ReD: Research and eData

Presentation for the Symposium on BEER November 2023Characterizing US spatial connectivity during theearly phase of the COVID pandemic: implications formetapopulation modelingGiulia Pullano 1,*, Lucila G. Alvarez-Zuzek 2, Vittoria Colizza 3, Shweta Bansal 11Department of Biology, Regents Hall, Georgetown University, 37th and O Streets NW, Washington, DC,20057-1229, USA2Fondazione Bruno Kessler, Trento, Italy3 INSERM, Sorbonne Université, Institut Pierre Louis d’Epidémiologie et de Santé Publique IPLESP, F75012,Paris, Francegiulia.pullano@georgetown.eduIn March2020, US states issued local social distancing recommendations to fight COVID-19. Although localepidemic activity was partially mitigated, SARS-CoV-2 diffused geographically in a rapid manner. Theimpact of control policies on meso-scale and long-range human mobility, and the optimal implement scaleof such policies, remains unclear. This lack of understanding hampers our ability to effectively model themetapopulation dynamics of disease. To address this problem, we use high-resolution human mobility datameasured via mobile app usage. Computing the daily coupling network between US counties, we observe thatintercounty connectivity was largely unperturbed by mobility restrictions, both during first lockdowns (Spring2020) and when social distancing was generally recommended (Fall 2020). Spatial clustering analysis revealsthat most daily mobility fluxes are within US states. We identify an average of 104 time-independent clustersclosely aligned with US state boundaries, providing a spatio-temporal characteristic scale for US intercountyconnectivity. We hypothesize that these findings can inform population structure for metapopulation diseasemodels. To test our hypothesis, we integrate mobility data into a metapopulation model to reproduce thenational invasion of the first wave of SARS-CoV-2 in the US. We find that while having access to county-level, daily mobility data best captures the spatial invasion of disease, static mobility data aggregatedto the scale of mobility data-based clusters also performs well to capture spatial disease diffusion. Ourwork demonstrates that intercounty mobility was negligibly affected outside of the Spring 2020, explainingthe broad spatial distribution of COVID-19 outbreaks in the US during the early phase of the pandemic.Such geographically dispersed outbreaks strain national public health resources, and necessitate complexmetapopulation modeling approaches for predicting disease dynamics and control design. We thus informthe design of such metapopulation models to balance high disease predictability with low data requirements.

Characterizing US spatial connectivity during the early phase of the COVID pandemic: implications for metapopulation modeling

https://ir.library.illinoisstate.edu/cgi/viewcontent.cgi?filename=0&article=1738&context=beer&type=additional

Characterizing US spatial connectivity during the early phase of the COVID pandemic: implications for metapopulation modeling

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