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Impacts of environmental and socio-economic factors on emergence and epidemic potential of Ebola in Africa

Author

Listed:
  • David W. Redding

    (University College London)

  • Peter M. Atkinson

    (Lancaster University)

  • Andrew A. Cunningham

    (Zoological Society of London, Regent’s Park)

  • Gianni Lo Iacono

    (University of Surrey)

  • Lina M. Moses

    (Tulane University)

  • James L. N. Wood

    (University of Cambridge)

  • Kate E. Jones

    (University College London
    Zoological Society of London, Regent’s Park)

Abstract

Recent outbreaks of animal-borne emerging infectious diseases have likely been precipitated by a complex interplay of changing ecological, epidemiological and socio-economic factors. Here, we develop modelling methods that capture elements of each of these factors, to predict the risk of Ebola virus disease (EVD) across time and space. Our modelling results match previously-observed outbreak patterns with high accuracy, and suggest further outbreaks could occur across most of West and Central Africa. Trends in the underlying drivers of EVD risk suggest a 1.75 to 3.2-fold increase in the endemic rate of animal-human viral spill-overs in Africa by 2070, given current modes of healthcare intervention. Future global change scenarios with higher human population growth and lower rates of socio-economic development yield a 1.63-fold higher likelihood of epidemics occurring as a result of spill-over events. Our modelling framework can be used to target interventions designed to reduce epidemic risk for many zoonotic diseases.

Suggested Citation

  • David W. Redding & Peter M. Atkinson & Andrew A. Cunningham & Gianni Lo Iacono & Lina M. Moses & James L. N. Wood & Kate E. Jones, 2019. "Impacts of environmental and socio-economic factors on emergence and epidemic potential of Ebola in Africa," Nature Communications, Nature, vol. 10(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-12499-6
    DOI: 10.1038/s41467-019-12499-6
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    Cited by:

    1. Srinka Basu & Sugata Sen, 2023. "COVID 19 Pandemic, Socio-Economic Behaviour and Infection Characteristics: An Inter-Country Predictive Study Using Deep Learning," Computational Economics, Springer;Society for Computational Economics, vol. 61(2), pages 645-676, February.
    2. Abu Bakkar Siddique & Kingsley E. Haynes & Rajendra Kulkarni & Meng-Hao Li, 2023. "Regional poverty and infection disease: early exploratory evidence from the COVID-19 pandemic," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 70(1), pages 209-236, February.
    3. Cristiano, S. & Ulgiati, S. & Gonella, F., 2021. "Systemic sustainability and resilience assessment of health systems, addressing global societal priorities: Learnings from a top nonprofit hospital in a bioclimatic building in Africa," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).

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