Prediction of COVID-19 Disease Progression in India : Under the Effect of National Lockdown
This work addresses the urgent need for policymakers in India to understand and forecast COVID-19 spread under lockdown, though it is incremental as it applies existing models to new regional data.
The paper applied the SIR epidemiological model and a statistical machine learning model to estimate the basic reproduction number (R0) for COVID-19 in India and predict case progression, finding that India's R0 of 2.75 as of March 4, 2020, was comparable to early China and predicting less than 66,224 cases by May 1, 2020, if lockdown measures were effective.
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}