A model to simulate the impact of timing, coverage and transmission intensity on the effectiveness of indoor residual spraying (IRS) for malaria control

Abstract

(i) To develop a temperature- and rainfall-driven model of malaria transmission capable of prediction. (ii) To use the model to examine the relationship between the intervention timing and transmission intensity on the effectiveness of indoor residual spraying (IRS). A dynamic model of malaria transmission was developed from existing models of malaria transmission dynamics. The model was used to retrospectively predict actual malaria cases from Hwange district in Zimbabwe using actual meteorological and IRS timing and coverage data. Simulations of alternative intervention scenarios (timing and coverage) examined the effectiveness of earlier and later interventions, at higher and lower coverage levels in epidemic and non-epidemic years. The model was able to predict actual malaria cases in Hwange over a four-and-a-half-year period with a lead time of 4 months (e.g. January rainfall and temperature predicts April malaria) and a correlation coefficient of 0.825 (r(2) = 0.6814). The IRS simulations show that the marginal benefits of increasing IRS coverage are higher in high-transmission (HT) years relative to lower transmission years. This implies that over a period of years, maximum impact could be achieved with a given quantity of insecticide by increasing coverage in HT years. However, the model also shows that earlier spraying is more effective in all years, especially so in epidemic years, and that IRS has limited impact if it is carried out too late in relation to peak transmission. Temperature- and rainfall-driven models of malaria transmission have the potential to predict malaria epidemics. Early intervention based on prior knowledge of the magnitude of the malaria season can be more effective and efficient than carrying out routine activities every year. Malaria control planners need improved access to the technology that would allow them to better predict malaria epidemics and develop Malaria Early Warning Systems (MEWS). MEWS can then be linked to intervention planning to reduce the devastating impact of malaria epidemics on populations.