Abstract
Understanding the emergence and subsequent spread of human infectious diseases is a critical global challenge, especially for high-impact zoonotic and vector-borne diseases. Global climate and land-use change are likely to alter host and vector distributions, but understanding the impact of these changes on the burden of infectious diseases is difficult. Here, we use a Bayesian spatial model to investigate environmental drivers of one of the most important diseases in Africa, Rift Valley fever (RVF). The model uses a hierarchical approach to determine how environmental drivers vary both spatially and seasonally, and incorporates the effects of key climatic oscillations, to produce a continental risk map of RVF in livestock (as a proxy for human RVF risk). We find RVF risk has a distinct seasonal spatial pattern influenced by climatic variation, with the majority of cases occurring in South Africa and Kenya in the first half of an El Niño year. Irrigation, rainfall and human population density were the main drivers of RVF cases, independent of seasonal, climatic or spatial variation. By accounting more subtly for the patterns in RVF data, we better determine the importance of underlying environmental drivers, and also make space- and time-sensitive predictions to better direct future surveillance resources.This article is part of the themed issue ‘One Health for a changing world: zoonoses, ecosystems and human well-being’.
Highlights
Rift Valley fever, risk map Authors for correspondence: Spatial, seasonal and climatic predictive models of Rift Valley fever disease across Africa
We find that Rift Valley fever (RVF) risk has distinct seasonal and climatic spatial patterns
We find that RVF risk increased with the presence of irrigation, a larger proportion of land under cultivation and a higher human population density (INLA regression model, n 1⁄4 976, wAIC 1⁄4 776.18)
Summary
Present address: School of Public Health, Faculty of Medicine, Imperial College London, London W2 1PG, UK. A Bayesian hierarchical spatial modelling approach is likely to be suited for EIDs due to patchy data coverage and complex emergence and transmission patterns One such disease, Rift Valley fever (RVF) has become one of the most important zoonoses of sub-Saharan Africa over the last century, causing devastating health and economic impacts on domestic ruminants and humans [15], and more recently causing serious epizootics outside Africa (Saudi Arabia and Yemen [16]). The development and survival rates of mosquito vectors are known to be highly dependent on temperature, and as mosquitoes require water bodies for larval development, rainfall, soil type, presence of irrigation and previous history of flooding can all be employed to capture the likelihood of a grid cell containing standing water [31] All of these variables represented synoptic data and, outbreaks occurring at the same locations had the same values irrespective of date of outbreak. We made full Bayesian predictions at 4160 points in a 18 grid across Africa for 12 different seasonal and climatological scenarios
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