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To combat the coronavirus disease 2019 (COVID-19) pandemic, the world hasvaccination, plasma therapy, herd immunity, and epidemiological interventionsas few possible options. The COVID-19 vaccine development is underway and itmay take a significant amount of time to develop the vaccine and afterdevelopment, it will take time to vaccinate the entire population, and plasmatherapy has some limitations. Herd immunity can be a plausible option to fightCOVID-19 for small countries. But for a country with huge population likeIndia, herd immunity is not a plausible option, because to acquire herdimmunity approximately 67% of the population has to be recovered from COVID-19infection, which will put an extra burden on medical system of the country andwill result in a huge loss of human life. Thus epidemiological interventions(complete lockdown, partial lockdown, quarantine, isolation, social distancing,etc.) are some suitable strategies in India to slow down the COVID-19 spreaduntil the vaccine development. In this work, we have suggested the SIR modelwith intervention, which incorporates the epidemiological interventions in theclassical SIR model. To model the effect of the interventions, we haveintroduced \r{ho} as the intervention parameter. \r{ho} is a cumulativequantity which covers all type of intervention. We have also discussed thesupervised machine learning approach to estimate the transmission rate(\b{eta}) for the SIR model with intervention from the prevalence of COVID-19data in India and some states of India. To validate our model, we present acomparison between the actual and model-predicted number of COVID-19 cases.Using our model, we also present predicted numbers of active and recoveredCOVID-19 cases till Sept 30, 2020, for entire India and some states of Indiaand also estimate the 95% and 99% confidence interval for the predicted cases.

Analysis of COVID-19 cases in India through Machine Learning: A Study of Intervention