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Mathematical Sciences Letters
An International Journal


Volumes > Vol. 11 > No. 2


Using ETS State Space Model for forecasting on third wave on COVID19 in India

PP: 59-64
Khder Mohammed Alakkari, Pradeep Mishra, Deepa Rawat, Mostafa Abotaleb, Abdullah Mohammad Ghazi Al khatib, Monika Devi, Immad A Shah,
Every day, there are new cases of COVID19 infections around the world, and a new strain of this virus known as Omicron is spreading rapidly. To contain and monitor gatherings, as well as provide assistance in areas where this infection is spreading rapidly, it is critical to identify the number of new cases of injuries and deaths. Forecasting new cases and deaths in India between the dates 9/1/2022 and 18/1/2022 was the goal of this study. Based on state-space likelihood calculations, the Error Trend Seasonal (ETS) model was used in the context of a dynamic nonlinear framework for model selection and forecast standard errors. Small sample size and non-normal distribution of data, non-stationarity, trend, and seasonality are all characteristics of these models that have never been used with data from India before. Predictions using the best-fit model from "9/1/2022" to "18/1/2022" indicate that new cases in India are expected to reach (209,275). With a growth rate of 100.43 percent, new deaths are expected to reach (369), 14.5%. The predicted values of daily new vaccination cases are expected to reach (7341793) with a growth rate of 0%. The Total cases is expected to reach (37044777) cases with a growth rate of 3.8%. The total number of deaths is expected to reach (486771) with a 0.56 percent growth rate. As the pandemic continues to spread, these estimates are essential for determining how many drugs and oxygen tanks will be needed as well as the number of ICU beds that will be needed. As a result, the government will benefit from this forecast, leading to the authorities taking the necessary precautions against this new wave of infection.

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