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

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.