Towards a comprehensive simulation model of malaria epidemiology and control.

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.

journal_name

Parasitology

journal_title

Parasitology

authors

Smith T,Maire N,Ross A,Penny M,Chitnis N,Schapira A,Studer A,Genton B,Lengeler C,Tediosi F,de Savigny D,Tanner M

doi

10.1017/S0031182008000371

subject

Has Abstract

pub_date

2008-11-01 00:00:00

pages

1507-16

issue

13

eissn

0031-1820

issn

1469-8161

pii

S0031182008000371

journal_volume

135

pub_type

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