Peste des Petits Ruminants (PPR) is a transboundary, highly contagious, and fatal disease of small ruminants. PPR causes global annual economic losses of between USD 1.5 and 2.0 billion across more than 70 affected countries. Despite the commercial availability of effective PPR vaccines, lack of financial and technical commitment to PPR control coupled with a dearth of refined PPR risk profiling data in different endemic countries has perpetuated PPR virus transmission. In Uganda, over the past 5 years, PPR has extended from northeastern Uganda (Karamoja) with sporadic incursions in other districts /regions. To identify disease cluster hotspot trends that would facilitate the design and implementation of PPR risk-based control methods (including vaccination), we employed the space-time cube approach to identify trends in the clustering of outbreaks in neighbouring space-time cells using confirmed PPR outbreak report data (2007-2020). We also used negative binomial and logistic regression models and identified high small ruminant density, extended road length, low annual precipitation and high soil water index as the most important drivers of PPR in Uganda. The study identified (with 90-99% confidence) five PPR disease hotspot trend categories across subregions of Uganda. Diminishing hotspots were identified in the Karamoja region whereas consecutive, sporadic, new and emerging hotspots were identified in central and southwestern districts of Uganda. Inter-district and cross-border small ruminant movement facilitated by longer road stretches and animal comingling precipitate PPR outbreaks as well as PPR virus spread from its initial Karamoja focus to the central and southwestern Uganda. There is therefore urgent need to prioritize considerable vaccination coverage to obtain the required herd immunity among small ruminants in the new hotspot areas to block transmission to further emerging hotspots. Findings of this study provide a basis for more robust timing and prioritization of control measures including vaccination.

African swine fever (ASF) is a viral disease of swine that has been present in the Russian Federation since 2007. Counts of ASF outbreaks reported in the Southern regions of the country (2007-2014) were aggregated to a grid of hexagons, and a zero-inflated Poisson model accounting for spatial dependence between hexagons was used to identify factors associated with the presence of ASF outbreaks and factors associated with the number of ASF reports in affected hexagons. Increasing density of pigs raised on low biosecurity farms was found to be positively associated with the probability of occurrence of at least one ASF outbreak in a hexagon and with the average number of reported ASF outbreaks amongst affected hexagons. Increasing human population density and increasing distance from the closest diagnostic laboratory were additional variables associated with number of reported ASF outbreaks amongst affected hexagons. The model was shown to have good predictive ability.