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

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. Eckhoff, Thomas A. Smith, Melissa A. Penny and Peter W. Gething ()
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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
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

Nature Communications, 2015, vol. 6, issue 1, 1-10

Abstract: 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.

Date: 2015
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DOI: 10.1038/ncomms9170

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