IJCRRI Journ Cur Res ReInternational Journal of Current Research and ReviewI Journ Cur Res Re2231-21960975-5241Radiance Research Academy4050 http://dx.doi.org/10.31782/IJCRR.2021.SP263HealthcareForecasting of Covid-19 Cases in India by Time Series Analysis Using Autoregressive Integrated Moving Average Model AshishKhobragadeDilipKadam11062021WaOV178181This article is copyright of Popeye Publishing, 20092009This is an open-access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0) Licence. You may share and adapt the material, but must give appropriate credit to the source, provide a link to the licence, and indicate if changes were made.

Introduction: COVID-19 is caused by SARS-CoV-2, a coronavirus. Forecasting has an important role in the surveillance of new emerging diseases like COVID-19. Objective: The objective of the study was to forecast COVID-19 cases by using the ARIMA model. Methods: We have used the ARIMA model to forecast cases of COVID-19 occurring per day in India. A total of 50 observations were used to fit the model. Model is best fitted by using order (0,2,1) which has the lowest AIC value. Forecasted values were compared with actual values. Results: We have found that actual reported cases per day were within 95% CI of forecasted values. Conclusions: ARIMA model can be used to forecast over a short period. This model can be used to develop strategies for the containment of pandemics.

ARIMA COVID-19 Forecast India Model Time series analysis