Mathematical and Nutritional Modeling for Predicting the Effectiveness of Malaria Preventive Interventions: An Integrated Epidemiological Framework for Population-Level Risk and Response Optimization

This work presents an innovative mathematical–nutritional modeling framework designed to predict the effectiveness of malaria preventive interventions under varying nutritional conditions. By integrating an extended SEIHR epidemiological model with nutrition-modulated parameters—including zinc deficiency, vitamin A deficiency, and protein–energy malnutrition—we quantify how nutritional status influences malaria susceptibility, progression, severity, and population-level intervention outcomes. Using nonlinear differential equations, vector–human transmission modeling, sensitivity analysis, and simulation-based optimization, we evaluate the performance of preventive strategies such as insecticide-treated nets (ITNs) and seasonal malaria chemoprevention (SMC) across diverse nutritional scenarios. Our results reveal threshold effects, heterogeneous risk profiles, and significant amplification of intervention effectiveness when nutritional status improves. This work provides the first integrative framework linking nutrition science, mathematical epidemiology, and malaria prevention. It supports the development of precision public health strategies and offers a predictive tool for policymakers to design nutrition-informed, cost-effective interventions for malaria-endemic regions.