Abstract
BackgroundThe transmission of malaria is highly variable and depends on a range of climatic and anthropogenic factors. In addition, the dispersal of Anopheles mosquitoes is a key determinant that affects the persistence and dynamics of malaria. Simple, lumped-population models of malaria prevalence have been insufficient for predicting the complex responses of malaria to environmental changes.Methods and resultsA stochastic lattice-based model that couples a mosquito dispersal and a susceptible-exposed-infected-recovered epidemics model was developed for predicting the dynamics of malaria in heterogeneous environments. The Ithat{o} approximation of stochastic integrals with respect to Brownian motion was used to derive a model of stochastic differential equations. The results show that stochastic equations that capture uncertainties in the life cycle of mosquitoes and interactions among vectors, parasites, and hosts provide a mechanism for the disruptions of malaria. Finally, model simulations for a case study in the rural area of Kilifi county, Kenya are presented.ConclusionsA stochastic lattice-based integrated malaria model has been developed. The applicability of the model for capturing the climate-driven hydrologic factors and demographic variability on malaria transmission has been demonstrated.
Highlights
The transmission of malaria is highly variable and depends on a range of climatic and anthropogenic factors
This shortcoming has been attributed to the differences in stochastic modelling approaches, as agent-based models [15, 17, 18] are inefficient for large-scale simulations with very large number of vector individuals involved, while lumped-population models [26, 27] bypass the spatial dynamics of the diseases
The results reveal that the variation of the mosquito population in both aquatic and adult stages are highly dependent on climatic factors
Summary
A stochastic lattice-based model that couples a mosquito dispersal and a susceptibleexposed-infected-recovered epidemics model was developed for predicting the dynamics of malaria in heterogeneous environments. The Itoapproximation of stochastic integrals with respect to Brownian motion was used to derive a model of stochastic differential equations. The results show that stochastic equations that capture uncertainties in the life cycle of mosquitoes and interactions among vectors, parasites, and hosts provide a mechanism for the disruptions of malaria. Model simulations for a case study in the rural area of Kilifi county, Kenya are presented
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