Ecological and Anthropogenic Spatial Gradients Shape Patterns of Dispersal of Foot-and-Mouth Disease Virus in Uganda

Anna Munsey, Frank Norbert Mwiine, Sylvester Ochwo, Lauro Velazquez-Salinas, Zaheer Ahmed, Luis L. Rodriguez, Elizabeth Rieder, Andres Perez, Kimberly Vanderwaal

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Using georeferenced phylogenetic trees, phylogeography allows researchers to elucidate interactions between environmental heterogeneities and patterns of infectious disease spread. Concordant with the increasing availability of pathogen genetic sequence data, there is a growing need for tools to test epidemiological hypotheses in this field. In this study, we apply tools traditionally used in ecology to elucidate the epidemiology of foot-and-mouth disease virus (FMDV) in Uganda. We analyze FMDV serotype O genetic sequences and their corresponding spatiotemporal metadata from a cross-sectional study of cattle. We apply step selection function (SSF) models, typically used to study wildlife habitat selection, to viral phylogenies to show that FMDV is more likely to be found in areas of low rainfall. Next, we use a novel approach, a resource gradient function (RGF) model, to elucidate characteristics of viral source and sink areas. An RGF model applied to our data reveals that areas of high cattle density and areas near livestock markets may serve as sources of FMDV dissemination in Uganda, and areas of low rainfall serve as viral sinks that experience frequent reintroductions. Our results may help to inform risk-based FMDV control strategies in Uganda. More broadly, these tools advance the phylogenetic toolkit, as they may help to uncover patterns of spread of other organisms for which genetic sequences and corresponding spatiotemporal metadata exist.

Original languageEnglish (US)
Article number524
Issue number5
StatePublished - May 2022

Bibliographical note

Funding Information:
Funding: Cooperative Biological Engagement Program of the United States Department of Defense, Defense Threat Reduction Agency [agreement #8802]; Agricultural Research Service, United States Department of Agriculture [CRIS project #1940-32000-061-00D]; National Institute of Food and Agriculture, United States Department of Agriculture [grant #2016-38420-25288].

Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.


  • disease ecology
  • livestock markets
  • molecular epidemiology
  • regression models
  • spatial analysis


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