Stochastic lattice-based modelling of malaria dynamics
Abstract Background The 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 p...
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ftdoajarticles:oai:doaj.org/article:117c3623683c444a8191fd611b7ef409 2023-05-15T15:07:00+02:00 Stochastic lattice-based modelling of malaria dynamics Phong V. V. Le Praveen Kumar Marilyn O. Ruiz 2018-07-01T00:00:00Z https://doi.org/10.1186/s12936-018-2397-z https://doaj.org/article/117c3623683c444a8191fd611b7ef409 EN eng BMC http://link.springer.com/article/10.1186/s12936-018-2397-z https://doaj.org/toc/1475-2875 doi:10.1186/s12936-018-2397-z 1475-2875 https://doaj.org/article/117c3623683c444a8191fd611b7ef409 Malaria Journal, Vol 17, Iss 1, Pp 1-17 (2018) Malaria Climate change Metapopulation Stochastic Ecohydrology Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 article 2018 ftdoajarticles https://doi.org/10.1186/s12936-018-2397-z 2022-12-31T13:04:32Z Abstract Background The 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 results A 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$$\hat{o}$$ 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. Conclusions A 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. Article in Journal/Newspaper Arctic Climate change Directory of Open Access Journals: DOAJ Articles Arctic Malaria Journal 17 1 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Malaria Climate change Metapopulation Stochastic Ecohydrology Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 |
spellingShingle |
Malaria Climate change Metapopulation Stochastic Ecohydrology Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 Phong V. V. Le Praveen Kumar Marilyn O. Ruiz Stochastic lattice-based modelling of malaria dynamics |
topic_facet |
Malaria Climate change Metapopulation Stochastic Ecohydrology Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 |
description |
Abstract Background The 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 results A 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$$\hat{o}$$ 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. Conclusions A 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. |
format |
Article in Journal/Newspaper |
author |
Phong V. V. Le Praveen Kumar Marilyn O. Ruiz |
author_facet |
Phong V. V. Le Praveen Kumar Marilyn O. Ruiz |
author_sort |
Phong V. V. Le |
title |
Stochastic lattice-based modelling of malaria dynamics |
title_short |
Stochastic lattice-based modelling of malaria dynamics |
title_full |
Stochastic lattice-based modelling of malaria dynamics |
title_fullStr |
Stochastic lattice-based modelling of malaria dynamics |
title_full_unstemmed |
Stochastic lattice-based modelling of malaria dynamics |
title_sort |
stochastic lattice-based modelling of malaria dynamics |
publisher |
BMC |
publishDate |
2018 |
url |
https://doi.org/10.1186/s12936-018-2397-z https://doaj.org/article/117c3623683c444a8191fd611b7ef409 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Climate change |
genre_facet |
Arctic Climate change |
op_source |
Malaria Journal, Vol 17, Iss 1, Pp 1-17 (2018) |
op_relation |
http://link.springer.com/article/10.1186/s12936-018-2397-z https://doaj.org/toc/1475-2875 doi:10.1186/s12936-018-2397-z 1475-2875 https://doaj.org/article/117c3623683c444a8191fd611b7ef409 |
op_doi |
https://doi.org/10.1186/s12936-018-2397-z |
container_title |
Malaria Journal |
container_volume |
17 |
container_issue |
1 |
_version_ |
1766338586381123584 |