Spatial, seasonal and climatic predictive models of rift valley fever disease across Africa

David W. Redding*, Sonia Tiedt, Giovanni Lo Iacono, Bernard Bett, Kate E. Jones

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    33 Citations (Scopus)


    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 Nin˜ o 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 spaceand time-sensitive predictions to better direct future surveillance resources.

    Original languageEnglish
    Article number20160165
    JournalPhilosophical transactions of the Royal Society of London. Series B, Biological sciences
    Issue number1725
    Publication statusPublished - 2017

    Bibliographical note

    Funding Information:
    All authors were funded by the Ecosystem Services for Poverty Alleviation Programme (ESPA) (NE-J001570-1). The ESPA programme (Dynamic Drivers of Disease in Africa Consortium) is funded by the Department for International Development (DFID), the Economic and Social Research Council (ESRC) and the Natural Environment Research Council (NERC).

    Publisher Copyright:
    © 2017 The Author(s) Published by the Royal Society. All rights reserved.

    Copyright 2018 Elsevier B.V., All rights reserved.


    • Africa
    • Bayesian spatial model
    • Climatic oscillations
    • Integrated Laplace Approximations
    • Rift valley fever
    • Risk map


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