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Assessing the spatiotemporal malaria transmission intensity with heterogeneous risk factors: A modeling study in Cambodia

Malaria control can significantly benefit from a holistic and precise way of quantitatively measuring the transmission intensity, which needs to incorporate spatiotemporally varying risk factors. In this study, we conduct a systematic investigation to characterize malaria transmission intensity by t...

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Detalles Bibliográficos
Autores principales: Liu, Mutong, Liu, Yang, Po, Ly, Xia, Shang, Huy, Rekol, Zhou, Xiao-Nong, Liu, Jiming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: KeAi Publishing 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9944205/
https://www.ncbi.nlm.nih.gov/pubmed/36844760
http://dx.doi.org/10.1016/j.idm.2023.01.006
Descripción
Sumario:Malaria control can significantly benefit from a holistic and precise way of quantitatively measuring the transmission intensity, which needs to incorporate spatiotemporally varying risk factors. In this study, we conduct a systematic investigation to characterize malaria transmission intensity by taking a spatiotemporal network perspective, where nodes capture the local transmission intensities resulting from dominant vector species, the population density, and land cover, and edges describe the cross-region human mobility patterns. The inferred network enables us to accurately assess the transmission intensity over time and space from available empirical observations. Our study focuses on malaria-severe districts in Cambodia. The malaria transmission intensities determined using our transmission network reveal both qualitatively and quantitatively their seasonal and geographical characteristics: the risks increase in the rainy season and decrease in the dry season; remote and sparsely populated areas generally show higher transmission intensities than other areas. Our findings suggest that: the human mobility (e.g., in planting/harvest seasons), environment (e.g., temperature), and contact risk (coexistences of human and vector occurrence) contribute to malaria transmission in spatiotemporally varying degrees; quantitative relationships between these influential factors and the resulting malaria transmission risk can inform evidence-based tailor-made responses at the right locations and times.