Application of Autoregressive Moving Average Model in the Prediction of COVID-19 of China
Li Xiuling
Center for Disease Control and Prevention, Yunyan, Guiyang City, China.
Li Xi
School of Public Health and Health, Guizhou Medical University, China.
Wang Wen
Center for Disease Control and Prevention, Yunyan, Guiyang City, China.
Xie Hongying
Center for Disease Control and Prevention, Yunyan, Guiyang City, China.
Su LiuQian
Center for Disease Control and Prevention, Yunyan, Guiyang City, China.
Zhang Jiangping *
Health Bureau Yunyan, Guiyang City, China.
*Author to whom correspondence should be addressed.
Abstract
Objective: To establish ARIMA model through time series analysis to understand the occurrence law of newly confirmed cases of novel coronavirus pneumonia and provide references for taking epidemic prevention and control measures.
Methods: The cumulative confirmed and cured cases of COVID-19 are collected through the official website of the National Health Commission, and the number of newly confirmed and cured cases per week are sorted out. We analyze the time series of newly diagnosed and cured COVID-19 cases every week from April 12, 2020 to December 5, 2021 by IBM SPSS 25.0 software. The model is established through model identification, parameter estimation and model fitting.
Results: The number of reported cases of COVID-19 has no obvious seasonal characteristics. The ARIMA(2,1,1) model well fitted the time series, R2 = 0.542/0.617. Through the residual white noise test, all parameters of the model have statistical significance, Ljung box q = 9.095/9.651, P > 0.05. We predict the cases and cures in the four weeks after December 5, 2021 by ARIMA(2,1,1). The measured values in the first week and the second week are within the predicted 95% CI range.
Discussion and Conclusion: The epidemiological characteristics of COVID-19 need a longer time series for validation and analysis. ARIMA model can predict the incidence of COVID-19 in a short term, and the model should be constantly revised according to the actual situation.
Keywords: COVID-19, time series analysis, ARIMA, infectious diseases, forecasting