COVID 19, a realistic model for saturation, growth and decay of the India specific disease

TL;DR

This paper presents a simple, realistic model for COVID-19 progression in India, incorporating lockdown effects, recovery times, and exponential growth, to forecast infection, recovery, and death trends with optimistic predictions.

Contribution

It introduces a novel model that combines lockdown timing, recovery dynamics, and exponential growth to accurately forecast COVID-19 trends in India.

Findings

Infected counts show near-linear growth with R0 close to 1/6.

Model predicts saturation and decay phases effectively.

Forecasts indicate optimistic future scenario.

Abstract

This work presents a simple and realistic approach to handle the available data of COVID-19 patients in India and to forecast the scenario. The model proposed is based on the available facts like the onset of lockdown (as announced by the Government on 25th day, {\tau}0 and the recovery pattern dictated by a mean life recovery time of {\tau}1 ( normally said to be around 14 days). The data of infected COVID-19 patients from March 2, to April 16, 2020 has been used to fit the evolution of infected, recovery and death counts. A slow rising exponential growth, with R0 close to 1/6, is found to represent the infected counts indicating almost a linear rise. The rest of growth, saturation and decay of data is comprehensibly modelled by incorporating lockdown time controlled R0, having a normal error function like behaviour decaying to zero in some time frame of {\tau}2 . The recovery mean life time {\tau}1 dictates the peak and decay. The results predicted for coming days are interesting and optimistic. The introduced time constants based on experimental data for both the recovery rate as well as for determining the time span of activity of R0 after the lockdown are subject of debate and provide possibility to introduce trigger factors to alter these to be more suited to the model. The model can be extended to other communities with their own R0 and recovery time parameters.