Machine learning analysis of COVID-19 transmission dynamics demographic risk and contact tracing outcomes in Nigeria

Abstract

The coronavirus 2019 (COVID-19) pandemic has posed significant challenges to developing countries like Nigeria due to limited resources. Accurate prediction of disease spread is crucial for effective containment measures. This study investigates the application of statistical and machine learning (ML) techniques in modelling and predicting COVID-19 cases in Nigeria, using data from January 2020 through December 2021. By analyzing demographic data (age, gender, location), symptom patterns, and contact tracing information, we seek to identify correlations and temporal trends associated with disease transmission. The dataset obtained from the National Centre for Disease Control (NCDC) was cleaned before statistical analyses were conducted using Pearson’s Correlation, Analysis of Variance, and Cramer’s V Correlation. Prediction performed using a random forest (RF) classification model, implemented in Python’s scikit-learn library. Key findings include (1) 94.97% of confirmed contacts tested positive, underscoring high transmission rates; (2) occupations like healthcare workers and students were high-risk groups; and (3) the RF model achieved 90.8% accuracy in classifying source cases, though it struggled with minority classes. These findings can inform evidence-based policymaking and contribute to mitigating the impact of future outbreaks. A key limitation of this study is its reliance on the accuracy of the NCDC data.