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

Introduction: Coronaviruses are single-stranded RNA viruses that affect human and non-human mammals and birds Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus The World Health Organization (WHO) declared this as a pandemic on the 11th of March 2020 Since then, governments have been on war footing imposing lockdowns with various restrictions, ramping up medical infrastructure and creating awareness among the people asking them to wear masks, to follow physical distancing, to wash hands regularly and various other safety measures Objective: To forecast the number of cases in the states of Tamil Nadu and Maharashtra for 20 days and visualize the numbers for each state Methods: Different approaches using the number of deaths, the number of recovered have experimented, but the results using the number of tests done turned out to be more accurate The authors have presented a methodology that could predict the number of infections based on the number of tests done using the Random Forest Regressor method The forecast helps in estimating the spread of the disease and act as a tool for the government to take appropriate actions Results and Conclusion: It was concluded that the accuracy of the predictions was heavily dependent on the consistency of the number of tests taken More consistency, greater accuracy © IJCRR

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M. Sivagami

Papers

Predicting COVID-19 cases in Indian states using random forest regression