Simulation of malaria epidemiology and control in the highlands of western Kenya

• Published in: Malaria Journal
• Main Authors: Stuckey Erin M, Stevenson Jennifer C, Cooke Mary K, Owaga Chrispin, Marube Elizabeth, Oando George, Hardy Diggory, Drakeley Chris, Smith Thomas A, Cox Jonathan, Chitnis Nakul
• Format: Article in Journal/Newspaper
• Language: English
• Published: BMC 2012
• Subjects: Simulation; Kenya; EIR; Mathematical Modelling; Sensitivity analysis; Malaria; OpenMalaria; Arctic medicine. Tropical medicine; RC955-962; Infectious and parasitic diseases; RC109-216; Arctic
• Online Access: https://doi.org/10.1186/1475-2875-11-357; https://doaj.org/article/100b37809e3b40a3a5581baf1da26232

Abstract Background Models of Plasmodium falciparum malaria epidemiology that provide realistic quantitative predictions of likely epidemiological outcomes of existing vector control strategies have the potential to assist in planning for the control and elimination of malaria. This work investigates the applicability of mathematical modelling of malaria transmission dynamics in Rachuonyo South, a district with low, unstable transmission in the highlands of western Kenya. Methods Individual-based stochastic simulation models of malaria in humans and a deterministic model of malaria in mosquitoes as part of the OpenMalaria platform were parameterized to create a scenario for the study area based on data from ongoing field studies and available literature. The scenario was simulated for a period of two years with a population of 10,000 individuals and validated against malaria survey data from Rachuonyo South. Simulations were repeated with multiple random seeds and an ensemble of 14 model variants to address stochasticity and model uncertainty. A one-dimensional sensitivity analysis was conducted to address parameter uncertainty. Results The scenario was able to reproduce the seasonal pattern of the entomological inoculation rate (EIR) and patent infections observed in an all-age cohort of individuals sampled monthly for one year. Using an EIR estimated from serology to parameterize the scenario resulted in a closer fit to parasite prevalence than an EIR estimated using entomological methods. The scenario parameterization was most sensitive to changes in the timing and effectiveness of indoor residual spraying (IRS) and the method used to detect P. falciparum in humans. It was less sensitive than expected to changes in vector biting behaviour and climatic patterns. Conclusions The OpenMalaria model of P. falciparum transmission can be used to simulate the impact of different combinations of current and potential control interventions to help plan malaria control in this low transmission setting. In this setting ...