Forecasting the Cumulative Confirmed Cases with the FGM and Fractional-Order Buffer Operator in Different Stages of COVID-19

This study proposes to use the fractional-order accumulation grey model (FGM) combined with the fractional-order buffer operator to predict the cumulative confirmed cases in different stages of COVID-19. In the early stages of COVID-19 outbreak, when the cumulative confirmed cases increased rapidly, we used the strengthening buffer operator in the prediction process. After the government’s prevention measures started to take effect, the growth rate of cumulative confirmed cases slows down. Therefore, the weakening buffer operator is applied in the prediction process. The fractional order of the buffer operator is derived from the historical data, which are more relevant. The empirical analysis of seven countries’ data shows that the FGM with the fractional-order buffer operator achieves the best results for most cases. The fractional-order buffer operator improves the prediction accuracy of the FGM in this study. Our study also suggests a practical way for predicting the trend of epidemic diseases.

Forecasting the Trend of COVID-19 Considering the Impacts of Public Health Interventions: An Application of FGM and Buffer Level.

PURPOSE: COVID-19 is still showing a tendency of spreading around the world. In order to improve the subsequent control of COVID-19, it is essential to conduct a study on measuring and predicting the scale of the outbreak in the future. METHODS: This paper uses rolling mechanism and grid search to find the best fractional order of Fractional Order Accumulation Grey Model (FGM). Buffer level is proposed based on the general form of weakening buffer operator to measure the effect of government control measurements on the epidemic. And the buffer level is associated with the Government Response Stringency index and the Mobility Index. RESULTS: Firstly, the model proposed in this paper dominates the ARIMA model which has been widely used in predicting the confirmed COVID-19 cases. Secondly, in the process of using the buffer level to modify the FGM, this paper finds that government measurements require the active cooperation of the public and often have a time lag when they are effective. Only when government increase its stringency and the public observe the order can the spread of COVID-19 be slowed down. If there is only the controlling measure and the public does not react actively, it will not slow down the epidemic. Thirdly, according to the Mobility Index and Government Response Stringency Index in December, this paper predicts the cumulative confirmed cases of the end of January in different scenarios according to different buffer levels. The study suggests that the world should continue to maintain high vigilance and take corresponding control measures for the outbreak of COVID-19. CONCLUSIONS: Government's control measures and public's abidance are both important in this battle with COVID-19. Governments control measures have time-lag effect and the time lag is about 9 days. When the government increases its stringency and the public cooperates with the government, we must consider the weaken buffer operator with proper buffer level in the prediction process. These prediction methods can be considered in the prediction of COVID-19 confirmed cases in the future or the trend of other epidemics.