Abstract
Planning of the control of Plasmodium falciparum malaria leads to a need for models of malaria epidemiology that provide realistic quantitative prediction of likely epidemiological outcomes of a wide range of control strategies. Predictions of the effects of control often ignore medium- and long-term dynamics. The complexities of the Plasmodium life-cycle, and of within-host dynamics, limit the applicability of conventional deterministic malaria models. We use individual-based stochastic simulations of malaria epidemiology to predict the impacts of interventions on infection, morbidity, mortality, health services use and costs. Individual infections are simulated by stochastic series of parasite densities, and naturally acquired immunity acts by reducing densities. Morbidity and mortality risks, and infectiousness to vectors, depend on parasite densities. The simulated infections are nested within simulations of individuals in human populations, and linked to models of interventions and health systems. We use numerous field datasets to optimise parameter estimates. By using a volunteer computing system we obtain the enormous computational power required for model fitting, sensitivity analysis, and exploration of many different intervention strategies. The project thus provides a general platform for comparing, fitting, and evaluating different model structures, and for quantitative prediction of effects of different interventions and integrated control programmes.
Highlights
SUMMARYPlanning of the control of Plasmodium falciparum malaria leads to a need for models of malaria epidemiology that provide realistic quantitative prediction of likely epidemiological outcomes of a wide range of control strategies
New interventions considered suitable for long-term control were validated through large-scale field trials. This motivated the development of predictive models that use empirical estimates of the effectiveness of interventions, studies on disease burden, and unit costs to quantify morbidity and mortality and likely
The first phase of the project focused on developing a single model for malaria epidemiology that reproduces the relevant aspects of malaria biology. We used this to predict the likely impact of pre-erythrocytic vaccines, including projections of the consequences of introducing such vaccines into the Expanded Program on Immunization, for both malaria epidemiology (Maire et al 2006 c) and vaccine cost-effectiveness (Tediosi et al 2006 a)
Summary
Planning of the control of Plasmodium falciparum malaria leads to a need for models of malaria epidemiology that provide realistic quantitative prediction of likely epidemiological outcomes of a wide range of control strategies. Predictions of the effects of control often ignore medium- and long-term dynamics. We use individual-based stochastic simulations of malaria epidemiology to predict the impacts of interventions on infection, morbidity, mortality, health services use and costs. Individual infections are simulated by stochastic series of parasite densities, and naturally acquired immunity acts by reducing densities. The simulated infections are nested within simulations of individuals in human populations, and linked to models of interventions and health systems. The project provides a general platform for comparing, fitting, and evaluating different model structures, and for quantitative prediction of effects of different interventions and integrated control programmes
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