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ORIGINAL RESEARCH article

Front. Public Health
Sec. Infectious Diseases: Epidemiology and Prevention
Volume 12 - 2024 | doi: 10.3389/fpubh.2024.1410824
This article is part of the Research Topic Exploring the Dynamics of Emerging and Re-emerging Diseases: From Origins to Impact View all 7 articles

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

Provisionally accepted
  • 1 Bloomberg School of Public Health, Johns Hopkins University, Baltimore, United States
  • 2 School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, United States
  • 3 Virginia Tech, Blacksburg, United States

The final, formatted version of the article will be published soon.

    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.

    Keywords: infectious disease epidemiology, Human mobility, mathematical modeling, Outbreaks, respiratory disease

    Received: 01 Apr 2024; Accepted: 03 Jul 2024.

    Copyright: © 2024 Cai, Spencer and Ruktanonchai. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Nick Ruktanonchai, Virginia Tech, Blacksburg, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.