Brief Analysis of the ARIMA model on the COVID-19 in Italy

Guorong Ding; Xinru Li; Yang Shen; Jiao Fan

Various mathematical models are being used to conduct a variety of studies to analyze and predict the evolution of this epidemic. Reference [2] based on the SEIR kinetic model, taking into account the propagation mechanism, infection rate, and isolation measures of COVID-19, established a SEIR +CAQ propagation kinetic model, which can be used to predict the trend of COVID-19 in China, and to provide epidemic prevention and help with decision making. Reference [3] used the least square method of SEIR partitioning and Poisson noise to estimate the basic reproduction number of COVID-19 in Japan as R0 = 2.6 (95% CI, 2.4-2.8). The experimental results show that the epidemic of COVID-19 in Japan will not end quickly, and it is ridiculous to think that COVID-19 will disappear in summer spontaneously. The traditional epidemic model (SEIR) involves various factors and analyses, which may subject to potential bias. Therefore, it is necessary to propose a COVID-19 prediction model based on time series. Reference [4] proposed the ARIMA model that is useful to predict the spread of COVID-19, and then continuously improved the model by updating the data set. The experimental results show that it has good consistency with the actual epidemic spread.