# Exploring infectious disease spread as a function of seasonal and pandemic-induced changes in human mobility

## TL;DR

This paper explores how seasonal and pandemic-related changes in human mobility affect the spread of infectious diseases, using data from Virginia to identify high-risk areas and times.

## Contribution

The study introduces a novel analysis of how community responses and mobility patterns during the pandemic altered disease transmission risks in urban and rural areas.

## Key findings

- Rural areas became relatively higher risk during the pandemic due to community responses to SARS-CoV-2.
- September and January posed higher risks in counties with large student populations due to increased mobility.
- Urban areas were highest risk pre-pandemic, but this shifted during the pandemic.

## Abstract

Community-level changes in population mobility can dramatically change the trajectory of any directly-transmitted infectious disease, by modifying where and between whom contact occurs. This was highlighted throughout the COVID-19 pandemic, where community response and nonpharmaceutical interventions changed the trajectory of SARS-CoV-2 spread, sometimes in unpredictable ways. Population-level changes in mobility also occur seasonally and during other significant events, such as hurricanes or earthquakes. To effectively predict the spread of future emerging directly-transmitted diseases, we should better understand how the spatial spread of infectious disease changes seasonally, and when communities are actively responding to local disease outbreaks and travel restrictions.

Here, we use population mobility data from Virginia spanning Aug 2019-March 2023 to simulate the spread of a hypothetical directly-transmitted disease under the population mobility patterns from various months. By comparing the spread of disease based on where the outbreak begins and the mobility patterns used, we determine the highest-risk areas and periods, and elucidate how seasonal and pandemic-era mobility patterns could change the trajectory of disease transmission.

Through this analysis, we determine that while urban areas were at highest risk pre-pandemic, the heterogeneous nature of community response induced by SARS-CoV-2 cases meant that when outbreaks were occurring across Virginia, rural areas became relatively higher risk. Further, the months of September and January led to counties with large student populations to become particularly at risk, as population flows in and out of these counties were greatly increased with students returning to school.