The Impact of Temperature and Isolation on COVID-19 in India: A Mathematical Modelling approach

TL;DR

This paper uses mathematical modeling to analyze how temperature and isolation delays influence COVID-19 spread in India, predicting increased infections during colder seasons and emphasizing the importance of timely isolation.

Contribution

It introduces a delay differential equation model incorporating temperature effects and analyzes stability, bifurcation, and sensitivity to understand COVID-19 dynamics in India.

Findings

Lower temperatures lead to higher infection rates.

Increased isolation delay results in more infections.

Model predicts winter season will see a rise in COVID-19 cases.

Abstract

The dynamics of COVID-19 in India are captured using a set of delay differential equations by dividing a constant population into six compartments. The equilibrium points are calculated and stability analysis is performed. Sensitivity analysis is performed on the parameters of the model. Bifurcation analysis is performed and the critical delay is calculated. By formulating the spread parameter as a function of temperature, the impact of temperature on the population is studied. We concluded that with the decrease in temperature, the average infections in the population increases. In view of the coming winter season in India, there will be an increase in new infections. This model falls in line with the characteristics that increase in isolation delay increases average infections in the population.