Prediction for the spread of COVID-19 in India and effectiveness of preventive measures

Anuradha Tomar (Corresponding author), Neeraj Gupta (Contributor)

Research output: Contribution to journalArticleAcademicpeer-review

293 Citations (Scopus)


The spread of COVID-19 in the whole world has put the humanity at risk. The resources of some of the largest economies are stressed out due to the large infectivity and transmissibility of this disease. Due to the growing magnitude of number of cases and its subsequent stress on the administration and health professionals, some prediction methods would be required to predict the number of cases in future. In this paper, we have used data-driven estimation methods like long short-term memory (LSTM) and curve fitting for prediction of the number of COVID-19 cases in India 30 days ahead and effect of preventive measures like social isolation and lockdown on the spread of COVID-19. The prediction of various parameters (number of positive cases, number of recovered cases, etc.) obtained by the proposed method is accurate within a certain range and will be a beneficial tool for administrators and health officials.
Original languageEnglish
Article number138762
Number of pages6
JournalScience of the Total Environment
Publication statusPublished - 1 Aug 2020


  • Betacoronavirus
  • Coronavirus Infections/epidemiology
  • Forecasting
  • Humans
  • India/epidemiology
  • Models, Theoretical
  • Neural Networks, Computer
  • Pandemics/prevention & control
  • Pneumonia, Viral/epidemiology
  • LSTM
  • Prediction
  • COVID-19
  • Recurrent neural network
  • Curve fitting


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