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Defining the relationship between infection prevalence and clinical incidence of Plasmodium falciparum malaria

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  • Ewan Cameron

    (Spatial Ecology and Epidemiology Group, University of Oxford, Tinbergen Building, Oxford OX1 3PS, UK)

  • Katherine E. Battle

    (Spatial Ecology and Epidemiology Group, University of Oxford, Tinbergen Building, Oxford OX1 3PS, UK)

  • Samir Bhatt

    (Spatial Ecology and Epidemiology Group, University of Oxford, Tinbergen Building, Oxford OX1 3PS, UK)

  • Daniel J. Weiss

    (Spatial Ecology and Epidemiology Group, University of Oxford, Tinbergen Building, Oxford OX1 3PS, UK)

  • Donal Bisanzio

    (Spatial Ecology and Epidemiology Group, University of Oxford, Tinbergen Building, Oxford OX1 3PS, UK)

  • Bonnie Mappin

    (Spatial Ecology and Epidemiology Group, University of Oxford, Tinbergen Building, Oxford OX1 3PS, UK)

  • Ursula Dalrymple

    (Spatial Ecology and Epidemiology Group, University of Oxford, Tinbergen Building, Oxford OX1 3PS, UK)

  • Simon I. Hay

    (Wellcome Trust Centre for Human Genetics, University of Oxford
    Institute for Health Metrics and Evaluation, University of Washington
    Fogarty International Center, National Institutes of Health)

  • David L. Smith

    (Spatial Ecology and Epidemiology Group, University of Oxford, Tinbergen Building, Oxford OX1 3PS, UK)

  • Jamie T. Griffin

    (MRC Centre for Outbreak Analysis and Modelling, Imperial College London)

  • Edward A. Wenger

    (Institute for Disease Modeling)

  • Philip A. Eckhoff

    (Institute for Disease Modeling)

  • Thomas A. Smith

    (Swiss Tropical and Public Health Institute, University of Basel)

  • Melissa A. Penny

    (Swiss Tropical and Public Health Institute, University of Basel)

  • Peter W. Gething

    (Spatial Ecology and Epidemiology Group, University of Oxford, Tinbergen Building, Oxford OX1 3PS, UK)

Abstract

In many countries health system data remain too weak to accurately enumerate Plasmodium falciparum malaria cases. In response, cartographic approaches have been developed that link maps of infection prevalence with mathematical relationships to predict the incidence rate of clinical malaria. Microsimulation (or ‘agent-based’) models represent a powerful new paradigm for defining such relationships; however, differences in model structure and calibration data mean that no consensus yet exists on the optimal form for use in disease-burden estimation. Here we develop a Bayesian statistical procedure combining functional regression-based model emulation with Markov Chain Monte Carlo sampling to calibrate three selected microsimulation models against a purpose-built data set of age-structured prevalence and incidence counts. This allows the generation of ensemble forecasts of the prevalence–incidence relationship stratified by age, transmission seasonality, treatment level and exposure history, from which we predict accelerating returns on investments in large-scale intervention campaigns as transmission and prevalence are progressively reduced.

Suggested Citation

  • Ewan Cameron & Katherine E. Battle & Samir Bhatt & Daniel J. Weiss & Donal Bisanzio & Bonnie Mappin & Ursula Dalrymple & Simon I. Hay & David L. Smith & Jamie T. Griffin & Edward A. Wenger & Philip A., 2015. "Defining the relationship between infection prevalence and clinical incidence of Plasmodium falciparum malaria," Nature Communications, Nature, vol. 6(1), pages 1-10, November.
    • Handle: RePEc:nat:natcom:v:6:y:2015:i:1:d:10.1038_ncomms9170
    DOI: 10.1038/ncomms9170
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    Cited by:

    1. Theresa Reiker & Monica Golumbeanu & Andrew Shattock & Lydia Burgert & Thomas A. Smith & Sarah Filippi & Ewan Cameron & Melissa A. Penny, 2021. "Emulator-based Bayesian optimization for efficient multi-objective calibration of an individual-based model of malaria," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    2. Tim C. D. Lucas & Anita K. Nandi & Elisabeth G. Chestnutt & Katherine A. Twohig & Suzanne H. Keddie & Emma L. Collins & Rosalind E. Howes & Michele Nguyen & Susan F. Rumisha & Andre Python & Rohan Ara, 2021. "Mapping malaria by sharing spatial information between incidence and prevalence data sets," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(3), pages 733-749, June.
    3. Christopher N Davis & T Deirdre Hollingsworth & Quentin Caudron & Michael A Irvine, 2020. "The use of mixture density networks in the emulation of complex epidemiological individual-based models," PLOS Computational Biology, Public Library of Science, vol. 16(3), pages 1-16, March.

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