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Research Project: Intervention Strategies to Respond, Control, and Eradicate Foot-and-Mouth Disease Virus (FMDV)

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Title: Phylogeography as a proxy for population connectivity for spatial modeling of foot-and-mouth disease outbreaks in Vietnam

item GUNASEKARA, UMANGA - University Of Minnesota
item Bertram, Miranda
item PEREZ, ANDRES - University Of Minnesota
item Arzt, Jonathan
item VANDERWAAL, KIMBERLY - University Of Minnesota

Submitted to: Transboundary and Emerging Diseases
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/18/2023
Publication Date: 1/29/2023
Citation: Gunasekara, U., Bertram, M.R., Perez, A., Arzt, J., VanderWaal, K., 2023. Phylogeography as a proxy for population connectivity for spatial modeling of foot-and-mouth disease outbreaks in Vietnam. Transboundary and Emerging Diseases. 15(2).

Interpretive Summary: Foot-and-mouth disease (FMD) is a major limitation of food animal production in much of Asia and Africa. Because it is often difficult to trace how the disease spreads, scientists regularly test new modeling techniques to learn how to track and predict where the virus (disease) will go. The objective of this study was to investigate whether the behavior of FMD virus in Vietnam was predicted better by techniques that used only the location (geography) or combination of location and the genetic makeup of the viruses. Overall, the combined approach (called phylogeography) was superior. Understanding these new aspects of disease spread in Vietnam would help to control an FMD outbreak in USA.

Technical Abstract: Bayesian space-time regression models are helpful tools to describe and predict the number and distribution of infectious disease outbreaks, identify risk factors, and delineate high-risk areas for disease prevention or control. In these models, structured and unstructured spatial and temporal effects account for various forms of non-independence amongst case counts reported across spatial units. For example, structured spatial effects are used to capture correlations in case counts amongst neighboring provinces that may stem from shared risk factors or population connectivity. For highly mobile populations, spatial adjacency is an imperfect measure of population connectivity due to frequent long-distance movements. In many instances, we lack data on host movement and population connectivity, hindering the application of space-time risk models that inform disease control efforts. Phylogeographic models that infer routes of viral dissemination across a region could serve as a proxy for historical patterns of population connectivity. The objective of this study was to investigate whether the effects of population connectivity in space-time regressions of case counts were better captured by spatial adjacency or by inferences from phylogeographic analyses. To compare these two approaches, we used foot-and-mouth disease virus (FMDV) in Vietnam as an example. We explored whether the distribution of reported clinical FMD outbreaks across space and time was better explained by models that incorporate population connectivity based upon FMDV movement (inferred by discrete phylogeographic analysis) as opposed to spatial adjacency and showed that the best-fit model utilized phylogeographic-based connectivity. Therefore, accounting for virus movement through phylogeographic analysis serves as a superior proxy for population connectivity in spatial-temporal risk models when movement data are not available. This approach may contribute to the design of surveillance and control activities in countries in which movement data are lacking or insufficient.