Characterizing US Spatial Connectivity and Implications for Geographical Disease Dynamics and Metapopulation Modeling: Longitudinal Observational Study

Our work demonstrates that intercounty mobility was negligibly affected outside the lockdown period in April 2020, explaining the broad spatial distribution of COVID-19 outbreaks in the United States during the early phase of the pandemic. Such geographically dispersed outbreaks place a significant strain on national public health resources and necessitate complex metapopulation modeling approaches for predicting disease dynamics and control design. We thus inform the design of such metapopulation models to balance high disease predictability with low data requirements.

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Publication Details

Journal
JMIR Public Health and Surveillance
Published
2025-02-18
DOI
https://doi.org/10.2196/64914
Citations
10
Primary Topic
COVID-19 epidemiological studies
Type
article
Field-Weighted Citation Impact
12.31
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article

Characterizing US Spatial Connectivity and Implications for Geographical Disease Dynamics and Metapopulation Modeling: Longitudinal Observational Study

Shweta Bansal, Lucila G. Alvarez-Zuzek, Vittoria Colizza, Giulia Pullano
10 citations
JMIR Public Health and Surveillance
COVID-19 epidemiological studies
12.31
article

Characterizing US Spatial Connectivity and Implications for Geographical Disease Dynamics and Metapopulation Modeling: Longitudinal Observational Study

Shweta Bansal, Lucila G. Alvarez-Zuzek, Vittoria Colizza, Giulia Pullano
article en
10 citations

Abstract

Our work demonstrates that intercounty mobility was negligibly affected outside the lockdown period in April 2020, explaining the broad spatial distribution of COVID-19 outbreaks in the United States during the early phase of the pandemic. Such geographically dispersed outbreaks place a significant strain on national public health resources and necessitate complex metapopulation modeling approaches for predicting disease dynamics and control design. We thus inform the design of such metapopulation models to balance high disease predictability with low data requirements.

JMIR Public Health and SurveillanceVol. 11
Inserm (FR), Georgetown University (US), Fondazione Bruno Kessler (IT), Sorbonne Université (FR), Institut Pierre Louis d‘Épidémiologie et de Santé Publique (FR)
Partnerships for the goals
Openalex Percentile: Top 1%
COVID-19 epidemiological studies
12.31
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Characterizing US Spatial Connectivity and Implications for Geographical Disease Dynamics and Metapopulation Modeling: Longitudinal Observational Study — Shweta Bansal, Lucila G. Alvarez-Zuzek, et al. · JMIR Public Health and Surveillance (2025) | TGRS Research Map | TGRS