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

Authored by Donal Bisanzio, Ewan Cameron, Katherine E Battle, Samir Bhatt, Daniel J Weiss, 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, Peter W Gething

Date Published: 2015

DOI: 10.1038/ncomms9170

Sponsors: Bill and Melinda Gates Foundation United States National Institutes of Health (NIH)

Platforms: No platforms listed

Model Documentation: Other Narrative Mathematical description

Model Code URLs: Model code not found

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.

Tags

disease Children West-africa Mathematical-model Sub-saharan africa Burkina-faso Seasonal malaria Transmission intensity Epidemiologic model Morbidity