Stochastic approach to study control strategies of Covid-19 pandemic in India

Epidemiol Infect. 2020 Aug 28:148:e200. doi: 10.1017/S0950268820001946.

Abstract

India is one of the severely affected countries by the Covid-19 pandemic at present. Within the stochastic framework of the SEQIR model, we studied publicly available data of the Covid-19 patients in India and analysed possible impacts of quarantine and social distancing as controlling strategies for the pandemic. Our stochastic simulation results clearly show that proper quarantine and social distancing should be maintained right from the start of the pandemic and continued until its end for effective control. This calls for a more disciplined social lifestyle in the future. However, only social distancing and quarantine of the exposed population are found not sufficient enough to end the pandemic in India. Therefore, implementation of other stringent policies like complete lockdown as well as increased testing of susceptible populations is necessary. The demographic stochasticity, which is quite visible in the system dynamics, has a critical role in regulating and controlling the pandemic.

Keywords: Covid-19 in India; quarantine; social distancing; stochastic modelling; stochastic simulation algorithm.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Betacoronavirus*
  • COVID-19
  • Communicable Disease Control / methods*
  • Communicable Disease Control / statistics & numerical data*
  • Coronavirus Infections / epidemiology*
  • Coronavirus Infections / prevention & control*
  • Humans
  • India / epidemiology
  • Pandemics / prevention & control*
  • Pneumonia, Viral / epidemiology*
  • Pneumonia, Viral / prevention & control*
  • SARS-CoV-2
  • Stochastic Processes