  To combat the coronavirus disease 2019 (COVID-19) pandemic, the world has
vaccination, plasma therapy, herd immunity, and epidemiological interventions
as few possible options. The COVID-19 vaccine development is underway and it
may take a significant amount of time to develop the vaccine and after
development, it will take time to vaccinate the entire population, and plasma
therapy has some limitations. Herd immunity can be a plausible option to fight
COVID-19 for small countries. But for a country with huge population like
India, herd immunity is not a plausible option, because to acquire herd
immunity approximately 67% of the population has to be recovered from COVID-19
infection, which will put an extra burden on medical system of the country and
will 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 spread
until the vaccine development. In this work, we have suggested the SIR model
with intervention, which incorporates the epidemiological interventions in the
classical SIR model. To model the effect of the interventions, we have
introduced \r{ho} as the intervention parameter. \r{ho} is a cumulative
quantity which covers all type of intervention. We have also discussed the
supervised machine learning approach to estimate the transmission rate
(\b{eta}) for the SIR model with intervention from the prevalence of COVID-19
data in India and some states of India. To validate our model, we present a
comparison between the actual and model-predicted number of COVID-19 cases.
Using our model, we also present predicted numbers of active and recovered
COVID-19 cases till Sept 30, 2020, for entire India and some states of India
and also estimate the 95% and 99% confidence interval for the predicted cases.
