Forecasting of COVID-19 sixth peak in Iran based on singular spectrum analysis

Coronavirus (COVID-19) is a pandemic that has affected all countries of the world. Forecasting the spread of corona disease will lead to the necessary measures to be taken by the authorities to control this disease. These include increasing vaccinations, quarantining cities and banning entry and exit, increasing the capacity of hospital beds, setting up round-the-clock vaccination centers, requiring the use of masks in public places, and observing social distances. Therefore, predicting such cases will reduce the number of corona cases and therefore reduce the mortality rate.

In this paper, using the Singular Spectrum Analysis (SSA) algorithm, the sixth peak of coronavirus in Iran is predicted by considering the current situation. To improve the grouping process of the SSA algorithm, eigenvalues have been selected in the optimization process, so that the predicted time series of which has been significantly improved according to the error-index.

Comparing the proposed method with other forecasting methods include Autoregressive Integrated Moving Average (ARIMA), Fractional ARIMA (ARFIMA), TBATS, and Neural Network Autoregression (NNAR), it is observed that the forecasting error is acceptable and the SSA method can be used for forecasting.

Originality/Value: 

This article predicts a new case of COVID-19 using efficient method SSA and the presented results confirm the effectiveness of the proposed method.