The online version of this article (doi:10.1186/1475-2875-11-357) contains supplementary material, which is available to authorized users.
The authors declare that they have no competing interests.
EMS designed the experiments, performed the literature review for model parameterization, analysed results and drafted the manuscript. JS participated in parameterization of the model and designed, supervised and conducted the MTC field studies. MC provided field implementation and sample analysis for the MTC entomological field studies. CO and EM supervised and coordinated the field collection of samples. GO was responsible for data management. DH programmed the simulation software. CD provided serological analysis for the MTC field studies. TAS and JC conceived of and designed the study. NC participated in designing the experiments, analysing the results, and drafting the manuscript. All authors read and approved the final manuscript.
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.
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.
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.
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 and for these scenarios, results were highly sensitive to transmission, vector exophagy, exophily and susceptibility to IRS, and the detection method used for surveillance. The level of accuracy of the results will thus depend upon the precision of estimates for each. New methods for analysing and evaluating uncertainty in simulation results will enhance the usefulness of simulations for malaria control decision-making. Improved measurement tools and increased primary data collection will enhance model parameterization and epidemiological monitoring. Further research is needed on the relationship between malaria indices to identify the best way to quantify transmission in low transmission settings. Measuring EIR through mosquito collection may not be the optimal way to estimate transmission intensity in areas with low, unstable transmission.
Additional file 1: Title: Model parameterization source overview. Description: Tables containing a detailed description of the various studies in Rachuonyo South district conducted by MTC and how the data was used to parameterize the base simulation scenario. (PDF 174 KB)12936_2012_2620_MOESM1_ESM.pdf
Additional file 2: Title: Parameter values for the model of the mosquito feeding cycle. Description: Tables containing a detailed description of the parameter values and their source(s) for the model of the mosquito feeding cycle. (PDF 405 KB)12936_2012_2620_MOESM2_ESM.pdf
Additional file 3: Title: Health system parameter values. Description: Tables containing a detailed description of the parameter values and their source(s) for the model of the health system. (PDF 400 KB)12936_2012_2620_MOESM3_ESM.pdf
Additional file 4: Title: Description of model demographic parameters. Description: Tables containing a detailed description of the parameter values and their source(s) for the model of the demography. (PDF 259 KB)12936_2012_2620_MOESM4_ESM.pdf
Additional file 5: Title: Vector control intervention effective length of protection parameter values. Description: Tables containing a detailed description of the parameter values and their source(s) for effective length of protection for the model of vector control interventions. (PDF 393 KB)12936_2012_2620_MOESM5_ESM.pdf
Additional file 6: Title: Vector control intervention effectiveness parameter values. Description: Tables containing a detailed description of the parameter values and their source(s) for effectiveness for the model of vector control interventions. (PDF 433 KB)12936_2012_2620_MOESM6_ESM.pdf
Additional file 7: Title: Vector control intervention implementation parameter values. Description: Tables containing a detailed description of the parameter values and their source(s) for implementation schedule and coverage levels for the model of vector control interventions. (PDF 283 KB)12936_2012_2620_MOESM7_ESM.pdf
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- Simulation of malaria epidemiology and control in the highlands of western Kenya
Erin M Stuckey
Jennifer C Stevenson
Mary K Cooke
Thomas A Smith
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