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

Modeling Cross-Disease Vulnerability in Africa: Predicting Malaria-Prone Countries and Accessing Susceptibility to Dengue, Chikungunya, and Measles Through Environmental and Social Determinants | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Modeling Cross-Disease Vulnerability in Africa: Predicting Malaria-Prone Countries and Accessing Susceptibility to Dengue, Chikungunya, and Measles Through Environmental and Social Determinants Bernard Sefah, Erick Abarkah, Davison Bosangi, Derrick Asante, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6956759/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract 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. Malaria Vulnerability Cross-disease risk Machine Learning Environmental and Social factors Vector-borne diseases Africa Public health CDVI Full Text Additional Declarations No competing interests reported. Supplementary Files MalariaDataset.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6956759","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":475459046,"identity":"e9ed4502-671a-4504-9eb0-e5e873b1487f","order_by":0,"name":"Bernard 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