Document Type : Research Article
Authors
Department of Mathematics and Applied Mathematics, University of the Western Cape, Private Bag X17, Bellville 7535, South Africa.
10.30473/coam.2025.73788.1292
Abstract
Malaria continues to represent a significant public health concern in Sudan, with cases rising over 40% from 2015 to 2020. This research investigates how climate change affects malaria transmission patterns using a mathematical model in an ordinary differential equation framework. The analysis involves calculating the basic reproduction number and evaluating the system's qualitative properties to gain insights into disease dynamics. Additionally, a sensitivity analysis is conducted to evaluate how climatic conditions, e.g., rainfall and temperature, influence key model parameters. Statistical approaches are utilized to estimate parameters and calibrate the model using empirical data from Sudan, ensuring consistency between the model and observed trends. Numerical simulations demonstrate the growing influence of climate variability on the spatial distribution of malaria vectors and the transmission progression over time. The study establishes a strong association between climatic changes and the exacerbation of malaria prevalence in Sudan. These findings emphasize the urgent need for climate-adaptive strategies, including improved vector control, strengthened surveillance systems, and climate-resilient public health interventions, to address the increased risks posed by changing environmental conditions. The research provides valuable insights to inform evidence-based policies aimed at reducing malaria transmission in Sudan and other regions that are experiencing similar challenges due to climate change.
Highlights
- A mathematical model employing Ordinary Differential Equations (ODEs) is developed to assess the impact of climate change on malaria transmission dynamics.
- The basic reproduction number (R0) is derived, accompanied by qualitative analysis to elucidate the underlying mechanisms governing disease propagation.
- Sensitivity analysis evaluates the influence of climatic variables—such as rainfall and temperature—on critical transmission parameters.
- The model calibration using empirical field data ensures consistency between theoretical predictions and observed malaria incidence trends in Sudan.
- Numerical simulations demonstrate a significant correlation between climate variability and alterations in vector distribution and malaria transmission patterns.
- Findings highlight the necessity for climate-adaptive public health strategies, including improved vector control measures, enhanced surveillance systems, and climate-resilient intervention frameworks.
- This study offers evidence-based insights to inform malaria control policies in Sudan and other regions vulnerable to climate-induced impacts on disease dynamics.
Keywords
Main Subjects
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