ArticleMalaria journal2018
Stochastic lattice-based modelling of malaria dynamics.
Article in Malaria journal, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- RDA-PSO: A Computational Method to Quantify the Diffusive Dispersal of Insects.Bulletin of mathematical biology · 2026Article
- Investigating the Impact of Irrigation on Malaria Vector Larval Habitats and Transmission Using a Hydrology-Based Model.GeoHealth · 2023Article
- An Overview of Malaria Transmission Mechanisms, Control, and Modeling.Medical sciences (Basel, Switzerland) · 2022Review
- Host movement, transmission hot spots, and vector-borne disease dynamics on spatial networks.Infectious Disease Modelling · 2022Article
- Using models to provide rapid programme support for California's efforts to suppress Huanglongbing disease of citrus.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2019Review
- Predicting the direct and indirect impacts of climate change on malaria in coastal Kenya.PloS one · 2019Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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 It[Formula: see text] 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.
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