Prediction of COVID-19 Disease Progression in India : Under the Effect of National Lockdown

TL;DR

This paper uses epidemiological modeling and machine learning to predict COVID-19 progression in India, highlighting regional differences in transmission rates and estimating case numbers under lockdown scenarios.

Contribution

It combines SIR epidemiological modeling with machine learning predictions to analyze COVID-19 spread in India at national and state levels, providing early insights and case forecasts.

Findings

Punjab has a very high R0 (~16), indicating urgent attention needed.

Most states have R0 above 3, suggesting rapid disease spread.

India's early R0 is comparable to China's, implying similar case growth if no effective measures are taken.

Abstract

In this policy paper, we implement the epidemiological SIR to estimate the basic reproduction number $\mathcal{R}_0$ at national and state level. We also developed the statistical machine learning model to predict the cases ahead of time. Our analysis indicates that the situation of Punjab ($\mathcal{R}_0\approx 16$) is not good. It requires immediate aggressive attention. We see the $\mathcal{R}_0$ for Madhya Pradesh (3.37) , Maharastra (3.25) and Tamil Nadu (3.09) are more than 3. The $\mathcal{R}_0$ of Andhra Pradesh (2.96), Delhi (2.82) and West Bengal (2.77) is more than the India's $\mathcal{R}_0=2.75$, as of 04 March, 2020. India's $\mathcal{R}_0=2.75$ (as of 04 March, 2020) is very much comparable to Hubei/China at the early disease progression stage. Our analysis indicates that the early disease progression of India is that of similar to China. Therefore, with lockdown in place, India should expect as many as cases if not more like China. If lockdown works, we should expect less than 66,224 cases by May 01,2020. All data and \texttt{R} code for this paper is available from \url{https://github.com/sourish-cmi/Covid19}