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Characterizing US Spatial Connectivity and Implications for Geographical Disease Dynamics and Metapopulation

Giulia Pullano¹, Lucila Gisele Alvarez-Zuzek², Vittoria Colizza³

¹Department of Biology, Georgetown University, 37th and O Streets NW, Washington, DC, 20057-1229, United States, 1 202 687 9256.

JMIR Public Health and Surveillance

|February 18, 2025

Summary

Human mobility patterns in the US remained stable, even during COVID-19, explaining widespread outbreaks. Static, clustered mobility data can effectively model disease spread.

Keywords:

COVID-19 SARS-CoV-2 SafeGraph US coronavirus digital health epidemic geographical disease dynamics health informatics human mobility infectious diseases mHealth metapopulation modeling mobile apps mobile health mobility data pandemic public health social distancing spatial connectivity spatio-temporal

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Area of Science:

Epidemiology
Network Science
Computational Biology

Background:

Human mobility is a key factor in infectious disease spread.
Social distancing policies were implemented early in the COVID-19 pandemic in the US.
Understanding mobility's role in disease diffusion is crucial due to data gaps.

Purpose of the Study:

To analyze how human mobility influences infectious disease spread at various scales within the US.
To investigate the impact of seasonality and behavioral shifts on mobility patterns.
To determine the geographic level at which mobility is homogeneous across the US.

Main Methods:

Analysis of high-resolution mobile app mobility data (SafeGraph Inc.) from 2019-2021.
Mapping daily connectivity between US counties to assess spatial clustering and temporal stability.
Integration into a spatially explicit transmission model to replicate SARS-CoV-2's first wave and assess mobility's impact.

Main Results:

Mobility patterns were stable from 2019-2021, with a notable decline in April 2020 due to lockdowns.
Intercounty connectivity remained seasonally stable and largely unaffected during the early COVID-19 phase.
Identified 104 stable geographic clusters of counties with strong internal connectivity, often crossing state boundaries.
County-level daily mobility data best captures disease invasion, while cluster-level static data also models spatial diffusion effectively.

Conclusions:

Intercounty mobility was minimally affected outside of the April 2020 lockdown, explaining the broad spatial distribution of early COVID-19 outbreaks.
Geographically dispersed outbreaks strain national public health resources and require complex metapopulation modeling.
Findings inform the design of metapopulation models for disease dynamics, balancing predictability with data requirements.