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<p>Environmental factors such as temperature, rainfall, and vegetation index play a crucial role in the transmission dynamics of malaria. Accurately quantifying the complex relationships between these variables and the malaria burden poses a significant challenge. In this study, we developed a host-mosquito mathematical model to investigate the impact of temperature, rainfall, and normalized difference vegetation index (NDVI) on malaria transmission dynamics calibrated with the Burundi case’s study. Mathematical analysis explored the equilibria, stability, and computation of the model’s threshold values. Numerical simulations suggest that temperature, rainfall, and vegetation index affect the transmission dynamics of malaria. Temperature and NDVI appear to exhibit a more pronounced influence among these factors. The conditions conducive to malaria transmission include a mean monthly temperature range of [20-25&#xa0;°C], an averaged monthly NDVI range of approximately [0.4-0.6]. The reproduction number was used as a quantitative measure to predict the impact of temperature, rainfall, and NDVI on malaria transmission dynamics across Burundi. The results suggest a progressive increase in the reproduction number over time, suggesting a threat of the rising number of cases in Burundi if drastic control measures are not implemented.</p>

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Mathematical Modelling of Malaria Integrating Temperature, Rainfall, and Vegetation Index

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月4日

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  • Kelly Joëlle Gatore Sinigirira,
  • Wandera Ogana,
  • Faraimunashe Chirove

摘要

Environmental factors such as temperature, rainfall, and vegetation index play a crucial role in the transmission dynamics of malaria. Accurately quantifying the complex relationships between these variables and the malaria burden poses a significant challenge. In this study, we developed a host-mosquito mathematical model to investigate the impact of temperature, rainfall, and normalized difference vegetation index (NDVI) on malaria transmission dynamics calibrated with the Burundi case’s study. Mathematical analysis explored the equilibria, stability, and computation of the model’s threshold values. Numerical simulations suggest that temperature, rainfall, and vegetation index affect the transmission dynamics of malaria. Temperature and NDVI appear to exhibit a more pronounced influence among these factors. The conditions conducive to malaria transmission include a mean monthly temperature range of [20-25 °C], an averaged monthly NDVI range of approximately [0.4-0.6]. The reproduction number was used as a quantitative measure to predict the impact of temperature, rainfall, and NDVI on malaria transmission dynamics across Burundi. The results suggest a progressive increase in the reproduction number over time, suggesting a threat of the rising number of cases in Burundi if drastic control measures are not implemented.