Asymmetric host movement reshapes local disease dynamics in metapopulations

Research output: Contribution to journalArticlepeer-review

Abstract

Understanding how the movement of individuals affects disease dynamics is critical to accurately predicting and responding to the spread of disease in an increasingly interconnected world. In particular, it is not yet known how movement between patches affects local disease dynamics (e.g., whether pathogen prevalence remains steady or oscillates through time). Considering a set of small, archetypal metapopulations, we find three surprisingly simple patterns emerge in local disease dynamics following the introduction of movement between patches: (1) movement between identical patches with cyclical pathogen prevalence dampens oscillations in the destination while increasing synchrony between patches; (2) when patches differ from one another in the absence of movement, adding movement allows dynamics to propagate between patches, alternatively stabilizing or destabilizing dynamics in the destination based on the dynamics at the origin; and (3) it is easier for movement to induce cyclical dynamics than to induce a steady-state. Considering these archetypal networks (and the patterns they exemplify) as building blocks of larger, more realistically complex metapopulations provides an avenue for novel insights into the role of host movement on disease dynamics. Moreover, this work demonstrates a framework for future predictive modelling of disease spread in real populations.

Original languageEnglish (US)
Article number9365
JournalScientific reports
Volume12
Issue number1
DOIs
StatePublished - Dec 2022

Bibliographical note

Funding Information:
We would like to thank Montserrat Torremorell and Cesar Corzo for conceptual discussions and feedback on early drafts of this work, as well as José Lourenço for helpful methodological discussions. This work was supported by the CVM Research Office UMN Ag Experiment Station General Research Funds, National Science Foundation DEB-2030509, and a UMN Office of Academic Clinical Affairs COVID-19 Rapid Response Grant.

Funding Information:
We would like to thank Montserrat Torremorell and Cesar Corzo for conceptual discussions and feedback on early drafts of this work, as well as José Lourenço for helpful methodological discussions. This work was supported by the CVM Research Office UMN Ag Experiment Station General Research Funds, National Science Foundation DEB-2030509, and a UMN Office of Academic Clinical Affairs COVID-19 Rapid Response Grant.

Publisher Copyright:
© 2022, The Author(s).

PubMed: MeSH publication types

  • Journal Article
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

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