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An update on the phylogeography and dynamics of pool 4 FMDV spread in East and Horn of Africa
  • Dennis Makau,
  • Jonathan Arzt,
  • Kim VanderWaal
Dennis Makau
The University of Tennessee Knoxville

Corresponding Author:dennmak003@gmail.com

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Jonathan Arzt
USDA-ARS Plum Island Animal Disease Center Branch
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Kim VanderWaal
University of Minnesota Twin Cities
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Abstract

Foot-and-mouth disease (FMD), a highly contagious viral infection affecting cloven-hoofed animals, has significant implications for global livestock production and trade. In this study we aimed to characterize and describe dispersal patterns and factors affecting pool 4 serotypes of FMD viruses (FMDV) in the East and Horn of Africa. The study area included 12 countries i.e., Sudan, South Sudan, Eritrea, Djibouti, Ethiopia, Somalia (Horn of Africa) and Kenya, Uganda, Tanzania, Rwanda, Burundi, Malawi (East Africa); 1423 VP1 sequence data were used (224 serotype A, 593 serotype O, 310 SAT1 and 296 SAT2) obtained from GenBank. Using continuous and discrete space phylogeographic models in BEAST, we assessed viral dispersal, population dynamics, direction and velocity modeled against environmental, human and livestock demographic and trade data as raster files. We observed a rise in accessible sequences in the last decade, signifying enhanced surveillance and research endeavors but emphasizing the need for rigorous analyses to address biases, ensuring comprehensive data collection for precise phylogeographic inference, and highlighting the importance of genomic surveillance given the geographical imbalance pre-1970. Higher precipitation correlated with increased dispersal velocity for certain serotypes, while elevation influenced the direction of viral spread. Proximity to human and livestock populations i.e., urbanization and agricultural activities also influenced spatial transmission dynamics. We identified distinct viral clusters with Kenya and Sudan as major sources for intercountry spread in the East and Northern regions, respectively. Regional collaboration, data sharing and targeted surveillance, informed by genomic data and environmental factors, can aid in early outbreak detection and management.