SARS-CoV-2 epidemic in India: epidemiological features and in silico analysis of the effect of interventions

F1000Res. 2020 Apr 30:9:315. doi: 10.12688/f1000research.23496.2. eCollection 2020.

Abstract

Background: After SARS-CoV-2 set foot in India, the Government took a number of steps to limit the spread of the virus in the country. This included restricted testing, isolation, contact tracing and quarantine, and enforcement of a nation-wide lockdown starting 25 March 2020. The objectives of this study were to i) describe the age, gender distribution, and mortality among COVID-19 patients identified till 14 April 2020 and predict the range of contact rate; and ii) predict the number of COVID-19 infections after 40 days of lockdown. Methods: We used a cross-sectional descriptive design for the first objective and a susceptible-infected-removed model for in silico predictions. We collected data from government-controlled and crowdsourced websites. Results: Studying age and gender parameters of 1161 Indian COVID-19 patients, the median age was 38 years (IQR, 27-52) with 20-39 year-old males being the most affected group. The number of affected patients were 854 (73.6%) men and 307 (26.4%) women. If the current contact rate continues (0.25-27), India may have 110460 to 220575 infected persons at the end of 40 days lockdown. Conclusion: The disease is majorly affecting a younger age group in India. Interventions have been helpful in preventing the worst-case scenario in India but will be unable to prevent the spike in the number of cases.

Keywords: COVID-19; Coronavirus; India; SIR model.

MeSH terms

  • Adult
  • Age Distribution
  • Betacoronavirus
  • COVID-19
  • Communicable Disease Control
  • Coronavirus Infections* / epidemiology
  • Coronavirus Infections* / mortality
  • Cross-Sectional Studies
  • Female
  • Humans
  • India / epidemiology
  • Male
  • Middle Aged
  • Pandemics
  • Pneumonia, Viral* / epidemiology
  • Pneumonia, Viral* / mortality
  • SARS-CoV-2
  • Sex Distribution
  • Young Adult

Associated data

  • figshare/10.6084/m9.figshare.c.4940472.v2

Grants and funding

The author(s) declared that no grants were involved in supporting this work.