PREDICTIVE MODELING OF COVID-19 OUTBREAKS USING LOGISTIC REGRESSION AND DECISION TREE
DOI:
https://doi.org/10.33003/fjs-2025-0912-4206Keywords:
COVID-19, Decision Tree, Forecasting, Logistic Regression, Outbreak, Predictive Modeling, Public HealthAbstract
The COVID-19 pandemic caused by SARS-CoV-2 remains one of the most significant global health crises of the 21st century. This study focuses on modeling and predicting COVID-19 outbreak trends in Ebonyi State, Nigeria, using logistic regression and decision tree algorithms. Secondary data were sourced from the Nigeria Centre for Disease Control (NCDC), WHO COVID-19 Dashboard, and Google Mobility Reports. Sociodemographic variables, clinical symptoms, and exposure histories were analyzed to determine their influence on infection risk. Findings revealed that age, religion, marital status, educational level, and occupation were significant predictors of infection (p < 0.001), whereas gender and ethnicity were not. Marital status emerged as a strong independent predictor—single and divorced individuals were more likely to test positive compared to married individuals. Interestingly, individuals with a history of travel or contact with confirmed cases were less likely to test positive, suggesting behavioral adaptations following exposure. The study concludes that predictive modeling can support early detection, efficient resource allocation, and targeted interventions in public health.
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Copyright (c) 2025 Stephen O. Etebefia, Victor Obukohwo, Chidinma A. Ibegbulam
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