Leptospirosis is a bacterial disease that affects both humans and animals, the occurrence of which increases markedly during and after heavy rainfall and flooding. The aim of this study was to determine the serological prevalence of leptospiral infection in livestock after a voluminous flood in 10 districts of the Malaysian state of Kelantan. In December 2014, Kelantan was hit by an extensive flood. A total of 1,728 serum samples were collected from livestock from the state, comprised of 1,024 from cattle, 366 from goats and 338 from sheep, and they were tested using the microscopic agglutination test (MAT). Altogether, 203 (11.75%; 203/1728; 95% CI: 10.20%–13.30%) of the tested sera were found to be positive serologically. Cattle had the highest prevalence of 14.16% (145/1024), while goats and sheep had 11.20% (41/366) and 5.03% (17/338) respectively. The most frequent serovars detected were Hardjo-bovis (3.70%; 64/1728), Hebdomadis (2.08%; 36/1728) and Pomona (1.04%; 18/1728). There was a statistically significant association (P < 0.05) between livestock that were exposed to the flood and seropositivity. This study showed that flood is a risk factor that can play a role in the epidemiology of leptospiral infection in livestock.

The objective of this study was to develop a model using equine data from geographically limited surveillance locations to predict risk categories for West Nile virus (WNV) infection in horses in all geographic locations across the province of Saskatchewan. The province was divided geographically into low-, medium-, or high-risk categories for WNV, based on available serology information from 923 horses obtained through 4 studies of WNV infection in horse populations in Saskatchewan. Discriminant analysis was used to build models using the observed risk of WNV in horses and geographic division-specific environmental data as well as to predict the risk category for all areas, including those beyond the surveillance zones. High-risk areas were indicated by relatively lower rainfall, higher temperatures, and a lower percentage of area covered in trees, water, and wetland. These conditions were most often identified in the southwest corner of the province. Environmental conditions can be used to identify those areas that are at highest risk for WNV. Public health managers could use prediction maps, which are based on animal or human information and developed from annual early season meteorological information, to guide ongoing decisions about when and where to focus intervention strategies for WNV.