Bayesian Optimized Event Based Epidemic Modeling in India

TL;DR

This paper develops a Bayesian-based epidemic model for India, incorporating mass gatherings and flight data, to predict and analyze COVID-19 spread, validated against government statistics.

Contribution

It introduces a novel Bayesian epidemic modeling approach that integrates mass gathering and flight data specific to India.

Findings

Validated the model with government COVID-19 data

Demonstrated the impact of mass gatherings on case numbers

Showed how flight data influences epidemic progression

Abstract

Pandemic outbreak creates a life threatening situation around the world and for a while it feels as if it slows down the world. A lot of effort is being taken to mitigate the spread of the pandemics. The main objective of this paper is to build a mathematical model of the pandemic spread in India based on the mass gathering using distribution functions and Bayesian approach. Subsequently modeled the pandemic spread based on the flights arrived in India using generalized linear regression. We validated the effect of these events in the number of confirmed cases in India and formulated the number of confirmed cases in the positive sense. Subsequently, studied the progression of the infective using progression series and flattened the curve using an appropriate convergence criterion. We validated the theoretical aspects with the statistics released by the Government.