Modelling and analysis of COVID-19 epidemic in India

COVID-19 epidemic is declared as the public health emergency of international concern by the World Health Organisation in the last week of March 2020. This disease originated from China in December 2019 has already caused havoc around the world, including India. The first case in India was reported on 30th January 2020, with the cases crossing 4 million on the day paper was written. This pandemic has caused more than 80,000 fatalities with 3 million recoveries. Strict lockdown of the nation for two months, immediate isolation of infected cases and app-based tracing of infected are some of the proactive steps taken by the authorities. For a better understanding of the evolution of COVID-19 in the world, study on evolution and growth of cases in India could not be avoided. To understand the same, one of the compartment model: Susceptible-Infectious-Quarantined-Recovered (SIQR) is used. Recovery rate and doubling rate of the total reported positive cases in the country had crossed 75% and 25 days, respectively. It is also estimated that there is a strong positive correlation between testing rate and detection of new cases up to 6 million tests per day. Using the SIQR modelling effective reproduction number, epidemic doubling rate and infected to quarantined ratio is determined to check the temporal evolution of the pandemic in the country. Effective reproduction number that was at its peak during first half of the April is gradually converging to 1. It is also estimated using this model that with each detected cases in India, there could be 10–50 undetected cases. Like every mathematical model, this model also has some assumptions. To make this model more robust, a technique with weighted parameter that can avoid a person with a strong immune system to be equally vulnerable to the infection, can be worked out. Machine learning algorithms can also be used to train our model with the data of other countries to make the analysis and prediction more precise and accurate.