A comparative study of modified SIR and logistic predictors using local level database of COVID-19 in India

Abstract

Purpose Several national- and state-level studies have been predicting the course of the COVID-19 pandemic using supervised machine learning algorithms. However, the comparison of such models has not been discussed before. This is the first-ever research wherein the two leading models, susceptible-infected-recovered (SIR) and logistic are compared. The purpose of this study is to observe their utility, at both the National and Municipal Corporation level in India. Design/methodology/approach The modified SIR and the logistic were deployed for analysis of the COVID-19 patients’ database of India and three Municipal Corporations, namely, Akola, Kalyan-Dombivli and Mira-Bhayander. The data for the study was collected from the official notifications for COVID-19 released by respective government websites. Findings This study provides evidence to show the superiority of the modified SIR over the logistic model. The models give accurate predictions for a period up to 14 days. The prediction accuracy of the models is limited due to change in government policies. This can be observed by the drastic increase in the COVID-19 cases after Unlock 1.0 in India. The models have proven that they can effectively predict for both National and Municipal Corporation level database. Practical implications The modified SIR model can be used to signify the practicality and effectiveness of the decisions taken by the authorities to contain the spread of coronavirus. Originality/value This study modifies the SIR model and also identifies and fulfills the need to find a more accurate prediction algorithm to help curb the pandemic.