Phylogeography as a Proxy for Population Connectivity for Spatial Modeling of Foot-and-Mouth Disease Outbreaks in Vietnam

• Published in: Viruses 2023-01, Vol.15 (2), p.388
• Main Authors: Gunasekara, Umanga, Bertram, Miranda R, Van Long, Nguyen, Minh, Phan Quang, Chuong, Vo Dinh, Perez, Andres, Arzt, Jonathan, VanderWaal, Kimberly
• Format: Article
• Language: English
• Subjects: Agriculture; Animals; Bayes Theorem; Bayesian analysis; Bayesian space-time models; Cattle; Connectivity; Disease control; Disease Outbreaks; Epidemics; Farmers; Foot & mouth disease; Foot-and-mouth disease; Foot-and-Mouth Disease - epidemiology; Genomes; Hogs; host movement; Infectious diseases; INLA; Livestock; Mathematical models; Medical geography; Medical research; Medicine, Experimental; mobility; Movements; outbreak risk; Outbreaks; Pathogens; phylodynamics; Phylogeny; Phylogeography; Provinces; Regression analysis; Risk factors; Statistical models; Statistics; Surveillance; Vietnam; Vietnam - epidemiology
• ISSN: 1999-4915; 1999-4915
• DOI: 10.3390/v15020388

Bayesian space-time regression models are helpful tools to describe and predict the distribution of infectious disease outbreaks and to delineate high-risk areas for disease control. In these models, structured and unstructured spatial and temporal effects account for various forms of non-independence amongst case counts across spatial units. Structured spatial effects capture correlations in case counts amongst neighboring provinces arising from shared risk factors or population connectivity. For highly mobile populations, spatial adjacency is an imperfect measure of connectivity due to long-distance movement, but we often lack data on host movements. Phylogeographic models inferring routes of viral dissemination across a region could serve as a proxy for 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) outbreak data from across Vietnam as an example. We identified that accounting for virus movement through phylogeographic analysis serves as a better proxy for population connectivity than spatial adjacency in spatial-temporal risk models. This approach may contribute to design surveillance activities in countries lacking movement data.