Modeling Cross Disease Vulnerability in Africa Using Environmental and Social Determinants to Predict Malaria Risk and Susceptibility to Dengue Chikungunya and Measles

Background Vector-borne and vaccine-preventable diseases such as malaria, dengue, chikungunya, and measles remain major public health challenges and a leading cause of morbidity and mortality in Africa, accounting for a significant proportion of the region’s disease burden. Factors including fragile health systems, rapid urbanization, and climate variability create an environment conducive to disease outbreaks and hinder socio-economic development. Despite the interconnected nature of these diseases, cross-disease vulnerability modeling using integrated environmental and social determinants remains limited in the African context. Methods This study proposes a predictive framework to identify African countries at heightened risk for multiple disease outbreaks, using malaria as a baseline for assessing susceptibility to dengue, chikungunya, and measles. Data from 40 African countries were compiled across 10 indicators reflecting climatic, demographic, and health system conditions. The methodology combined descriptive statistics, Random Forest classification, Lasso regression, Hotspot Analysis, Principal Component Analysis (PCA), and the development of a novel Cross-Disease Vulnerability Index (CDVI). Results Our findings revealed distinct clusters of high vulnerability across Central, Southern, and Western Africa. The Random Forest model achieved a classification accuracy of 87.5%, while the fine-tuned Lasso regression demonstrated a strong predictive performance with an R² of 0.8467. rainfall, urbanization, population density, and proportion of children under age 15 emerged as the most influential predictors of disease vulnerability. The CDVI showed a strong positive correlation with malaria case burdens (ρ = +0.72, p < 0.0028), validating its potential as a proxy for cross-disease susceptibility. Conclusion This study demonstrates the value of integrated modeling approaches in strengthening disease surveillance and early warning systems across Africa. By highlighting shared predictors of disease vulnerability, the CDVI can support public health decision-making by guiding the prioritization of epidemic preparedness efforts. Countries such as Botswana, Eswatini, Namibia, Gabon, and Equatorial Guinea with high vulnerability scores should be targeted for cross-cutting public health interventions. By addressing overlapping risk factors, this approach supports more equitable, data-driven public health planning aligned with the Sustainable Development Goals.