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Using real-time modelling to inform the 2017 Ebola outbreak response in DR Congo

Author

Listed:
  • R. Thompson

    (University of Oxford)

  • W. Hart

    (University of Oxford)

  • M. Keita

    (Regional Office for Africa
    University of Geneva)

  • I. Fall

    (World Health Organization)

  • A. Gueye

    (Regional Office for Africa)

  • D. Chamla

    (Regional Office for Africa)

  • M. Mossoko

    (Hygiene and Prevention)

  • S. Ahuka-Mundeke

    (Institut National de Recherche Biomédicale)

  • J. Nsio-Mbeta

    (Hygiene and Prevention)

  • T. Jombart

    (Imperial College)

  • J. Polonsky

    (University of Geneva)

Abstract

Important policy questions during infections disease outbreaks include: i) How effective are particular interventions?; ii) When can resource-intensive interventions be removed? We used mathematical modelling to address these questions during the 2017 Ebola outbreak in Likati Health Zone, Democratic Republic of the Congo (DRC). Eight cases occurred before 15 May 2017, when the Ebola Response Team (ERT; co-ordinated by the World Health Organisation and DRC Ministry of Health) was deployed to reduce transmission. We used a branching process model to estimate that, pre-ERT arrival, the reproduction number was $$R=1.49$$ R = 1.49 (95% credible interval $$({{{{\mathrm{0.67,2.81}}}}})$$ ( 0.67, 2.81 ) ). The risk of further cases occurring without the ERT was estimated to be 0.97 (97%). However, no cases materialised, suggesting that the ERT’s measures were effective. We also estimated the risk of withdrawing the ERT in real-time. By the actual ERT withdrawal date (2 July 2017), the risk of future cases without the ERT was only 0.01, indicating that the ERT withdrawal decision was safe. We evaluated the sensitivity of our results to the estimated $$R$$ R value and considered different criteria for determining the ERT withdrawal date. This research provides an extensible modelling framework that can be used to guide decisions about when to relax interventions during future outbreaks.

Suggested Citation

  • R. Thompson & W. Hart & M. Keita & I. Fall & A. Gueye & D. Chamla & M. Mossoko & S. Ahuka-Mundeke & J. Nsio-Mbeta & T. Jombart & J. Polonsky, 2024. "Using real-time modelling to inform the 2017 Ebola outbreak response in DR Congo," Nature Communications, Nature, vol. 15(1), pages 1-7, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-49888-5
    DOI: 10.1038/s41467-024-49888-5
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    References listed on IDEAS

    as
    1. Kris V Parag & Christl A Donnelly & Rahul Jha & Robin N Thompson, 2020. "An exact method for quantifying the reliability of end-of-epidemic declarations in real time," PLOS Computational Biology, Public Library of Science, vol. 16(11), pages 1-21, November.
    2. Nina Haug & Lukas Geyrhofer & Alessandro Londei & Elma Dervic & Amélie Desvars-Larrive & Vittorio Loreto & Beate Pinior & Stefan Thurner & Peter Klimek, 2020. "Ranking the effectiveness of worldwide COVID-19 government interventions," Nature Human Behaviour, Nature, vol. 4(12), pages 1303-1312, December.
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