Modeling Cross-Disease Vulnerability in Africa: Predicting Malaria-Prone Countries and Accessing Susceptibility to Dengue, Chikungunya, and Measles Through Environmental and Social Determinants

Background Malaria and its related vector-borne diseases, including dengue, chikungunya, and measles, among the parasitic diseases, are significant contributors to the estimated 95% deaths occurring in Africa where various factors such as ecosystem, fragile health systems, and climate conditions are favorable to species of mosquitoes transmitting the malaria parasite and have been a serious obstacle to socio-economic development across Africa. Understanding the shared predictors of these diseases can help prioritize public health interventions and resource allocation. However, cross-disease vulnerability modeling in the African context remains understudied, especially using integrated environmental and social indicators. Methods This research proposes developing a predictive framework that aims to address this challenge by identifying African countries that are highly vulnerable to multiple disease outbreaks, using malaria as a baseline for assessing susceptibility to dengue, chikungunya, and measles. To achieve this objective, we compiled data from 40 African countries across 10 variables capturing climatic, demographic, and healthcare system features. Analytical techniques included descriptive statistics, Random Forest, Lasso regression, Hotspot Analysis, Principal Component Analysis (PCA), and the construction of a novel Cross-Disease Vulnerability Index (CDVI). Results The analysis revealed highly vulnerable clusters in Central, Southern, and Western Africa. Our random forest achieved a classification accuracy of 87.5%, and the lasso regression achieved a coefficient of determination of 84.67%, highlighting rainfall, urbanization, population under age 15, and population density as the most influential predictors of vulnerability. The CDVI strongly correlated with actual malaria burdens (ρ = +0.72, p < 0.0028), indicating a significant association of malaria and cross-disease vulnerability. Conclusion This study highlights the importance of integrating cross-modeling in identifying multiple diseases proves to be a crucial factor in strengthening early warning systems. Health Organizations, policymakers, and other researchers should prioritize countries, including Botswana, Eswatini, Namibia, Gabon, and Equatorial Guinea, which have high CDVI scores for epidemic readiness initiatives.