2021•International Journal of Current Research and ReviewOpen access

Forecasting of Covid-19 Cases in India by Time Series Analysis Using Autoregressive Integrated Moving Average Model

Ashish Khobragade, Kadam Dilip

Abstract

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.

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Key concepts: Autoregressive integrated moving average, Coronavirus disease 2019 (COVID-19), Time series, Autoregressive model, Pandemic, 2019-20 coronavirus outbreak, Econometrics, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)