A Study to Analyse Covid-19 Outbreak Using Multiple Linear Regression: A Supervised Machine Learning Approach - Semwal, Jayanti; Bahuguna, Abhinav; Uniyal, Akanksha; Vyas, Shaili (National Journal of Community Medicine, 2023)
Introduction: Globally, COVID-19 have impacted people's quality of life Machine learning have recently become popular for making predictions because of their precision and adaptability in identifying diseases. This study aims to identify significant predictors for daily active cases and to visualise trends in daily active, positive cases, and immunisations. Material and methods: This paper utilized secondary data from Covid-19 health bulletin of Uttarakhand and multiple linear regression as a part of supervised machine learning is performed to analyse dataset. Results: Multiple Linear Regression model is more accurate in terms of greater score of R2 (=0.90) as compared to Linear Regression model with R2=0.88. The daily number of positive, cured, deceased cases are significant predictors for daily active cases (p <0.001). Using time series linear regression approach, cumulative number of active cases is forecasted to be 6695 (95% CI: 6259 - 7131) on 93rd day since 18 Sep 2022, if similar trend continues in upcoming 3 weeks in Uttarakhand. Conclusion: Regression models are useful for forecasting COVID-19 instances, which will help governments and health organisations address this pandemic in future and establish appropriate policies and recommendations for regular prevention.