Network Analysis of Small Ruminant Movements in Uganda: Implications for Control of Transboundary Animal Diseases

  • 1Department of Veterinary Medicine, Institute of Virology, Freie Universität Berlin, Berlin, Germany.
  • 2Health Program, International Livestock Research Institute, Nairobi, Kenya.
  • 3Center for Animal Disease Modeling and Surveillance, Department of Medicine and Epidemiology, School of Veterinary Medicine, University of California, Davis, California, USA.
  • 4Department of Animal Health, Ministry of Agriculture, Animal Industry and Fisheries, Entebbe, Uganda.
  • 5Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, St. Paul, Minnesota, USA.
  • 6Department of Biomedical Laboratory Technology and Molecular Biology, College of Veterinary Medicine, Animal Resources and Biosecurity, Makerere University, Kampala, Uganda.
  • 7Department of Animal Breeding and Husbandry in the Tropics and Subtropics, University of Hohenheim, Stuttgart, Germany.

Abstract

Domestic animals are moved for reasons that are mutually beneficial to the animal and the farmer. Some examples include the need for fresh grazing grounds and watering points, or the need to access livestock markets for income to sustain farmers' livelihoods. However, livestock mobility is a key risk factor for the transmission of transboundary animal diseases. Contact tracing of individual animals and flocks is very challenging, especially in most low-income countries, due to a lack of efficient livestock traceability systems. Despite these challenges, low-income countries, such as Uganda, issue paper-based animal movement permits (AMPs) to ensure only clinically healthy animals are moved following a physical inspection. In this study, we used national approximately 9 years of (2012-2020) small ruminant movement data obtained from archived AMPs in Uganda to describe small ruminant movement networks. The movement networks were described using social network analysis (SNA) approaches implemented in R software to identify and visualize relationships between individual and groups districts in Uganda. Lira, Kaberamaido, Nabilatuk, Mbarara, Kiruhura, Kampala, and Wakiso were identified as districts with the highest degree (in and out-degree) and betweenness among other centrality measures. Our results suggest these districts could be the most important bridges connecting the various regions of the country. Tailoring control interventions to such districts with high incoming and high outgoing shipments, or bridges, would accelerate the nation's ability to timely detect outbreaks, prevent or mitigate further spread, and contain diseases in their original foci, respectively. We also identified areas for active surveillance, vaccination, quarantine, and biosecurity measures-staging depending on prevailing circumstances. These findings will be used to guide the national small ruminant infectious diseases control strategies and subsequently contribute to national and global initiatives, such as the 2030 Peste des petits ruminants (PPR) eradication program.

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