Rift Valley fever (RVF), a mosquito-borne disease affecting ruminants and humans, is one of the most important viral zoonoses in Africa. The objective of the present study was to develop a geographic knowledge-based method to map the areas suitable for RVF amplification and RVF spread in four East African countries, namely, Kenya, Tanzania, Uganda and Ethiopia, and to assess the predictive accuracy of the model using livestock outbreak data from Kenya and Tanzania. Risk factors and their relative importance regarding RVF amplification and spread were identified from a literature review. A numerical weight was calculated for each risk factor using an analytical hierarchy process. The corresponding geographic data were collected, standardized and combined based on a weighted linear combination to produce maps of the suitability for RVF transmission. The accuracy of the resulting maps was assessed using RVF outbreak locations in livestock reported in Kenya and Tanzania between 1998 and 2012 and the ROC curve analysis. Our results confirmed the capacity of the geographic information system-based multi-criteria evaluation method to synthesize available scientific knowledge and to accurately map (AUC = 0.786; 95% CI [0.730–0.842]) the spatial heterogeneity of RVF suitability in East Africa. This approach provides users with a straightforward and easy update of the maps according to data availability or the further development of scientific knowledge.

SummaryThis study analysed the available data of seroprevalence to human influenza viruses in pigs in Cambodia using generalized linear mixed models in order to improve understanding of factors underlying the spread of human influenza viruses in Cambodian pigs. The associations between seroprevalence against seasonal H1N1 influenza virus in pigs and the population density of humans and pigs were not significant. However, a positive association between anti-H3 antibodies in pigs and the human population density was identified. In contrast, there was a negative association between seroprevalence of H3N2 in pigs and the pig population density. Our study has highlighted the difficulty in identifying epidemiological risk factors when a limited data set is used for analyses. We therefore provide recommendations on data collection for future epidemiological analyses that could be improved by collecting metadata related to the animals sampled. In addition, serosurveillance for influenza A viruses in pigs in high-risk areas or at slaughterhouses is recommended in resourcelimited countries.