Application of Autoregressive Moving Average Model in the Prediction of COVID-19 of China
Asian Journal of Probability and Statistics,
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.
- time series analysis
- infectious diseases
How to Cite
Yang Q, Wang J, Ma H, Wang X. Research on COVID-19 based on ARIMA model-Taking Hubei, China as an example to see the epidemic in Italy. Journal of Infection and Public Health. 2020;13(10):1415-1418.
Chen N, Zhou M, Dong X. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet; 2020.
Sun J. Forecasting COVID-19 pandemic in Alberta, Canada using modified ARIMA models. Computer Methods and Programs in Bio-medicine Update; 2021.
Sohrabi C, Alsafi Z, O'Neill N, Khan M, Kerwan A, Al-Jabir A, Iosifidis C, Agha R. World Health Organization declares global emergency: A review of the 2019 novel coronavirus (COVID-19). International Journal of Surgery. 2020;76:71-76.
World Health Organization. Novel Coronavirus (2019-nCoV) Situation Report-12; 2020.
Classification of Omicron (B.1.1.529): SARS-CoV-2 Variant of Concern.
Available:https://www.who.int/news/item/26-11-2021-classification-of-omicron-(b.1.1.529)-sars-cov-2-variant-of-concern (accessed Jul. 12, 2022)
Liao Z, Song Y, Ren S, Song X, Fan X, Liao Z. VOC-DL: Deep learning prediction model for COVID-19 based on VOC virus variants. Computer Methods and Programs in Biomedicine. 2022;224.
Ture M, Kurt I. Comparison of four different time series methods to forecast hepatitis A virus infection. Expert Systems Applications. 2006;31:41–6.
Shaman J, Karspeck A. Forecasting seasonal outbreaks of influenza. Proceedings of the National Academy of Sciences of the United States of America.2012;109(50):20425–20430.
Ilie OD, Ciobica A, Doroftei B. Testing the accuracy of the ARIMA models in forecasting the spreading of COVID-19 and the associated mortality rate. Medicina (Kaunas). 2020;56(11):566.
Yue XG, Shao XF, Li RYM, Crabbe MJC, Mi L, Hu S. Risk prediction and assessment: Duration, infections, and death toll of the COVID-19 and its impact on China’s economy. Journal of Risk and Financial Management. 2020;13(4):66.
Alabdulrazzaq H, Alenezi MN, Rawajfih Y, Alghannam BA, Al-Hassan AA, Al-Anzi FS. On the accuracy of ARIMA based prediction of COVID-19 spread. Results in Physics. 2021;27:104509.
Awan TM, Aslam F. Prediction of daily COVID-19 cases in European countries using automatic ARIMA model. Journal of Public Health Research. 2020;9(3):1765.
Wei W, Jiang J, Liang H, Gao L, Liang B, Huang J, Zang N, Liao Y, Yu J, Lai J, Qin F, Su J, Ye L, Chen H. Application of a combined model with Autoregressive Integrated Moving Average (ARIMA) and Generalized Regression Neural Network (GRNN) in forecasting hepatitis incidence in Heng County, China. PLoS One. 2016;11(6): e0156768.
Guan P, Huang DS, Zhou BS. Forecasting model for the incidence of hepatitis A based on artificial neural network. World Journal of Gastroenterology. 2004;10(24):3579–3582.
Tan CV, Singh S, Lai CH, et al. Forecasting COVID-19 case trends using SARIMA models during the third wave of COVID-19 in Malaysia. Int J Environ Res Public Health. 2022;19(3):1504.
Liu Q, Liu X, Jiang B, Yang W. Forecasting incidence of hemorrhagic fever with renal syndrome in China using ARIMA model. BMC Infectious Diseases. 2011;11:218.
Nsoesie EO, Beckman RJ, Shashaani S, Nagaraj KS, Marathe MV. A simulation optimization approach to epidemic forecasting. PLoS ONE. 2013;8:e67164.
Kuhe DA, Atsua Ikughur J. A time series model on the occurrence of COVID-19 pandemic in Nigeria. Asian Journal of Research in Infectious Diseases. 2021;8(4):66-80.
Abolmaali S, Shirzaei S. A comparative study of SIR Model, Linear Regression, Logistic Function and ARIMA Model for forecasting COVID-19 cases. AIMS Public Health. 2021;8(4):598-613.
Abstract View: 50 times
PDF Download: 27 times