[The mathematical modelling of tropical malaria]

Area of Science:

• Epidemiology
• Mathematical Biology
• Computational Science

Context:

• Malaria remains a significant global health challenge, necessitating accurate predictive tools for effective control.
• Existing models often lack the granularity to capture the full spectrum of epidemic dynamics and intervention impacts.

Purpose:

• To develop and validate a novel mathematical model for forecasting Plasmodium falciparum malaria epidemics.
• To create a flexible modeling framework that accounts for natural parameter variability and diverse control strategies.

Summary:

• A new nonlinear integro-differential model in partial derivatives has been developed for Plasmodium falciparum malaria.
• The model integrates individual and population characteristics, utilizing an original epidemic modeling methodology.
• Verification was performed using data from the Garki Project, demonstrating its operational forecasting capabilities.

Impact:

• Provides a robust tool for the operational forecast of malaria epidemics under various control scenarios.
• Enhances understanding of malaria transmission dynamics by incorporating parameter variability.
• Facilitates evidence-based decision-making for public health interventions against malaria.