Forecasting COVID 19 growth in India using Susceptible-Infected-Recovered (S.I.R) model

TL;DR

This paper models COVID-19 spread in India using an SIR model that incorporates social-contact structure, providing forecasts and insights into the effectiveness of social distancing measures.

Contribution

It introduces a Python algorithm that learns the social-contact parameter {eta} from data, enhancing epidemic forecasting accuracy.

Findings

Social distancing in India is currently insufficient to control COVID-19 growth.

The {eta} parameter effectively captures social-contact influence on spread.

Forecasts indicate the need for stricter social distancing measures.

Abstract

This work covers the analysis of the COVID 19 spread in different countries and dealing the main feature of COVID 19 growth, which is the spread due to the social-contact structure, which is governed by the parameter \b{eta}. The dependency of this parameter \b{eta} on the transmission level in society gives a sense of the effectiveness of the measures taken for social distancing. A separate algorithm is hardcoded in python using Scipy which learns the social-contact structure and gives a suitable value for \b{eta}, which has a major impact on the outcome of the result. Forecasting for the epidemic spread in India was done, and it was found that the strictness at which social distancing in India is done, is insufficient for the growth of COVID 19.