Integrating genomic evolution and reported outbreak occurrence in foot-and-mouth disease in Asia: Cross-border lineage connectivity and exploratory prediction of reported outbreak occurrence.

Foot-and-mouth disease (FMD) remains endemic across much of Asia and continues to threaten livestock production, trade, and transboundary disease control. To clarify how viral evolution, geographic connectivity, and reported outbreak occurrence can be interpreted at the regional scale, we analyzed 489 Asian foot-and-mouth disease virus (FMDV) complete genomes retrieved from GenBank using phylogenetic, molecular clock, recombination, and fixed-tree Bayesian stochastic search variable selection (BSSVS) approaches, and compared these results with exploratory presence-background modeling implemented with the MaxEnt algorithm using FAO EMPRES-i + outbreak records. Serotype O was the most frequently represented serotype, whereas covariation-aware time-scaled analyses produced the highest substitution rate point estimate for serotype A. Separate RDP5 recombination screening detected putative recombination signals most often among Indian isolates. Fixed-tree BSSVS identified associations among sampled countries supported by the model; these associations were not interpreted as direct transmission routes or movement direction. The selected occurrence model based on reported outbreak records included climatic and livestock density predictors, with annual mean temperature, cattle density, and minimum temperature of the coldest month contributing most strongly within the evaluated predictor set. Comparison of genomic and reported-occurrence results showed overlapping signals in some regions and contrasting patterns in others, indicating that sampled lineage associations and reported outbreak occurrence did not always coincide. Overall, this exploratory framework poses interesting questions about how FMDV evolutionary patterns in Asia can be interpreted alongside reported outbreak occurrence and provides a hypothesis-generating basis for future genomic sampling, outbreak-metadata improvement, and surveillance validation with denser serotype-specific and movement and control data.