Forecasting COVID-19 epidemic in India and high incidence states using SIR and logistic growth models

• Main Authors: B. Malavika, S. Marimuthu, Melvin Joy, Ambily Nadaraj, Edwin Sam Asirvatham, L. Jeyaseelan
• Format: Article
• Language: English
• Published: Elsevier 2021-01-01
• Series: Clinical Epidemiology and Global Health
• Subjects: COVID-19; Logistic growth model; SIR model; Time interrupted regression model; Projection
• Online Access: http://www.sciencedirect.com/science/article/pii/S2213398420301639

Background: Ever since the Coronavirus disease (COVID-19) outbreak emerged in China, there has been several attempts to predict the epidemic across the world with varying degrees of accuracy and reliability. This paper aims to carry out a short-term projection of new cases; forecast the maximum number of active cases for India and selected high-incidence states; and evaluate the impact of three weeks lock down period using different models. Methods: We used Logistic growth curve model for short term prediction; SIR models to forecast the maximum number of active cases and peak time; and Time Interrupted Regression model to evaluate the impact of lockdown and other interventions. Results: The predicted cumulative number of cases for India was 58,912 (95% CI: 57,960, 59,853) by May 08, 2020 and the observed number of cases was 59,695. The model predicts a cumulative number of 1,02,974 (95% CI: 1,01,987, 1,03,904) cases by May 22, 2020. As per SIR model, the maximum number of active cases is projected to be 57,449 on May 18, 2020. The time interrupted regression model indicates a decrease of about 149 daily new cases after the lock down period, which is statistically not significant. Conclusion: The Logistic growth curve model predicts accurately the short-term scenario for India and high incidence states. The prediction through SIR model may be used for planning and prepare the health systems. The study also suggests that there is no evidence to conclude that there is a positive impact of lockdown in terms of reduction in new cases.