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