Current Medical Issues

ORIGINAL ARTICLE
Year
: 2020  |  Volume : 18  |  Issue : 4  |  Page : 275--280

Models for forecasting the number of COVID cases in Indian states


T Unnikrishnan 
 Department of Statistics, Sri C. Achutha Menon Government College, Thrissur, Kerala, India

Correspondence Address:
Prof. T Unnikrishnan
Department of Statistics, Sri C. Achutha Menon Government College, Thrissur, Kerala
India

Introduction: Coronavirus, a worldwide pandemic today, is continuing its spread day by day. The only way that can be adopted at this stage is to control the number of cases to a minimum. Time series-forecasting models can enable the planners and administrators to foresee the picture and take timely action to control. Methodology: To forecast the daily number of COVID cases, prediction models were developed using Autoregressive-Integrated Moving Average (ARIMA) modelling techniques in the various states of India where the cases are highly reported. The main objectives included the assessment of trend and growth rates of number of COVID-19 cases confirmed and identification of the best ARIMA model for their prediction. Results: Excellent parsimonious forecasting equation for each state in India could be generated using the method. These models will be helpful for planning purposes in controlling the cases. The best model for the prediction of number of COVID cases in all over India was observed as ARIMA (0,2,1). ARIMA (0,1,0) was identified as best model for Mizoram and Puducherry. Conclusion: To predict all India cases, ARIMA (0,2,7) was identified as the best model.


How to cite this article:
Unnikrishnan T. Models for forecasting the number of COVID cases in Indian states.Curr Med Issues 2020;18:275-280


How to cite this URL:
Unnikrishnan T. Models for forecasting the number of COVID cases in Indian states. Curr Med Issues [serial online] 2020 [cited 2020 Dec 5 ];18:275-280
Available from: https://www.cmijournal.org/article.asp?issn=0973-4651;year=2020;volume=18;issue=4;spage=275;epage=280;aulast=Unnikrishnan;type=0