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Towards a comprehensive simulation model of malaria epidemiology and control

Published online by Cambridge University Press:  11 August 2008

T. SMITH*
Affiliation:
Swiss Tropical Institute, Socinstrasse 57, PO. Box, CH-4002 Basel, Switzerland
N. MAIRE
Affiliation:
Swiss Tropical Institute, Socinstrasse 57, PO. Box, CH-4002 Basel, Switzerland
A. ROSS
Affiliation:
Swiss Tropical Institute, Socinstrasse 57, PO. Box, CH-4002 Basel, Switzerland
M. PENNY
Affiliation:
Swiss Tropical Institute, Socinstrasse 57, PO. Box, CH-4002 Basel, Switzerland
N. CHITNIS
Affiliation:
Swiss Tropical Institute, Socinstrasse 57, PO. Box, CH-4002 Basel, Switzerland
A. SCHAPIRA
Affiliation:
Swiss Tropical Institute, Socinstrasse 57, PO. Box, CH-4002 Basel, Switzerland
A. STUDER
Affiliation:
Swiss Tropical Institute, Socinstrasse 57, PO. Box, CH-4002 Basel, Switzerland
B. GENTON
Affiliation:
Swiss Tropical Institute, Socinstrasse 57, PO. Box, CH-4002 Basel, Switzerland
C. LENGELER
Affiliation:
Swiss Tropical Institute, Socinstrasse 57, PO. Box, CH-4002 Basel, Switzerland
F. TEDIOSI
Affiliation:
Swiss Tropical Institute, Socinstrasse 57, PO. Box, CH-4002 Basel, Switzerland
D. DE SAVIGNY
Affiliation:
Swiss Tropical Institute, Socinstrasse 57, PO. Box, CH-4002 Basel, Switzerland
M. TANNER
Affiliation:
Swiss Tropical Institute, Socinstrasse 57, PO. Box, CH-4002 Basel, Switzerland
*
*Corresponding author. E-mail: Thomas-A.Smith@unibas.ch

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. 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.

Type
Research Article
Copyright
Copyright © 2008 Cambridge University Press

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