Foot-and-mouth disease (FMD), a highly contagious viral infection affecting cloven-hoofed animals, has significant implications for global livestock production and trade. In this study we aimed to characterize and describe dispersal patterns and factors affecting pool 4 serotypes of FMD viruses (FMDV) in the East and Horn of Africa. The study area included 12 countries i.e., Sudan, South Sudan, Eritrea, Djibouti, Ethiopia, Somalia (Horn of Africa) and Kenya, Uganda, Tanzania, Rwanda, Burundi, Malawi (East Africa); 1423 VP1 sequence data were used (224 serotype A, 593 serotype O, 310 SAT1 and 296 SAT2) obtained from GenBank. Using continuous and discrete space phylogeographic models in BEAST, we assessed viral dispersal, population dynamics, direction and velocity modeled against environmental, human and livestock demographic and trade data as raster files. We observed a rise in accessible sequences in the last decade, signifying enhanced surveillance and research endeavors but emphasizing the need for rigorous analyses to address biases, ensuring comprehensive data collection for precise phylogeographic inference, and highlighting the importance of genomic surveillance given the geographical imbalance pre-1970. Higher precipitation correlated with increased dispersal velocity for certain serotypes, while elevation influenced the direction of viral spread. Proximity to human and livestock populations i.e., urbanization and agricultural activities also influenced spatial transmission dynamics. We identified distinct viral clusters with Kenya and Sudan as major sources for intercountry spread in the East and Northern regions, respectively. Regional collaboration, data sharing and targeted surveillance, informed by genomic data and environmental factors, can aid in early outbreak detection and management.

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.

Foot-and-mouth disease (FMD) is endemic in India, where circulation of serotypes O, A and Asia 1 is frequent. In the past two decades, many of the most widespread and significant FMD lineages globally have emerged from the South Asia region. Here, we provide an epidemiological assessment of the ongoing mass vaccination programs in regard to post-vaccination monitoring and outbreak occurrence. The objective of this study was to quantify the spatiotemporal dynamics of FMD outbreaks and to assess the impact of the mass vaccination program between 2008 to 2016 with available antibody titer data from the vaccination monitoring program, alongside other risk factors that facilitate FMD spread in the country. We first conducted a descriptive analysis of epidemiological outcomes of governmental vaccination programs in India, focusing on antibody titer data from >1 million animals sampled as part of pre- and post-vaccination monitoring and estimates of standardized incidence ratios calculated from reported outbreaks per state/administrative unit. The percent of animals with inferred immunological protection (based on ELISA) was highly variable across states, but there was a general increase in the overall percent of animals with inferred protection through time. In addition, the number of outbreaks in a state was negatively correlated with the percent of animals with inferred protection. Because standardized incidence ratios of outbreaks were heterogeneously distributed over the course of eight years, we analyzed the distribution of reported FMD outbreaks using a Bayesian space-time model to map high-risk areas. This model demonstrated a ~50% reduction in the relative risk of outbreaks in states that were part of the vaccination program. In addition, states that did not have an international border experienced reduced risk of FMD outbreaks. These findings help inform risk-based control strategies for India as the country progresses towards reducing reported clinical disease.