Predicting the Spread of COVID-19 Using $$SIR$$ Model Augmented to Incorporate Quarantine and Testing

Abstract

India imposed a nationwide lockdown from 25th March 2020 onwards to combat the spread of COVID-19 pandemic. To model the spread of a disease and to predict its future course, epidemiologists make use of compartmental models such as the model. In order to address some of the assumptions of the standard model, a new modified version of model is proposed in this paper that takes into account the percentage of infected individuals who are tested and quarantined. This approach helps overcome the assumption of homogenous mixing of population which is inherent to the conventional model. Using the available data of the number of COVID-19 positive cases reported in the state of Kerala, and in India till 26th April, 2020 and 12th May 2020, respectively, the parameter estimation problem is converted into an optimization problem with the help of a least squared cost function. The optimization problem is then solved using differential evolution optimizer. The impact of lockdown is quantified by comparing the rising trend in infections before and during the lockdown. Using the estimated set of parameters, the model predicts that in the state of Kerala, by using certain interventions the pandemic can be successfully controlled latest by the first week of July, whereas the value for India is still greater than 1, and hence lifting of lockdown from all regions of the country is not advisable.

Predicting the Peak and COVID-19 trend in six high incidence countries: A study based on Modified SEIRD model

“…Anand et al (Anand, Sabarinath, Geetha, & Somanath, 2020) augmented the standard SIR model by incorporating Quarantine and Testing for the prediction of COVID-19 spread in India. In their paper, pre-lockdown and post lockdown conditions were accessed.…”

Data Analytics and Sir Modeling of Covid-19 in Bulgaria

“…Since the beginning of the COVID-19 epidemic, there has been various mathematical and statistical modelling that have predicted the global and national epidemic with varying degrees of accuracy and reliability (see [1], [2], [4], [10], [21], [25], [27], [28], [29], [32], [35], [40]). The accuracy of prediction and its uncertainty depend on the assumptions, availability and quality of data (see [31]).…”

Sen's Innovative Method for Trend Analysis of Epidemic: A Case Study of Covid-19 Pandemic in India

“…There have been numerous studies on the Covid-19 spread this year, in which researchers have discussed epidemic models (Sahoo and Sapra 2020 ; Samui et al 2020 ; Mahajan and Kaushal 2020 ; Mandal et al 2020 ; Rafiq et al 2020 ), SIPHERD (Susceptible-Infected or Symptomatic-Purely Asymptomatic-Hospitalized or Quarantined-Exposed-Recovered-Deceased) model (Mahajan et al 2020 ), SIR (Susceptible-Infected-Recovered) model (Anand et al 2020 ), SUC (Susceptible-Unidentified Infected-Confirmed) model (Lee et al 2020 ), SEIR (Susceptible-Exposed-Infectious-Recovered) model (Pai et al 2020 ), Gaussian mixture model (Singhal et al 2020 ), growth models (power model: Asad et al 2020 , Verma et al 2020 ; regression based model: Bhardwaj 2020 ), delay model (Contreras et al 2020 ), numerical simulation (Diwan et al 2020 ), and trend analysis of the infection (Gupta and Pal 2020 ; Singh and Sharma 2020 ). The different studies considered some factors directly whereas the other factors were based on certain assumptions.…”