Predicting the Pandemic COVID-19 Using ARIMA Model
Main Article Content
Abstract
Coronavirus disease 2019 (COVID-19) has been recognized as a global threat, and several studies are being conducted using various mathematical models to predict the probable evolution of this epidemic. The main objective of this study is to apply AutoRegressive Integrated Moving Average (ARIMA) model with the objective of monitoring and short-term forecasting the total confirmed new cases per day all over the world. The data are extracted from daily report of World Health Organization from 21st January 2020 to 16th March 2020. Akaike’s Information Criterion (AIC) and Ljung-Box test were used to evaluate the constructed models. To assess the validity of the proposed model, the Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) between the observed and fitted of COVID-19 total confirmed new cases was calculated. Finally, we applied “forecast” package in R software and the fitted ARIMA model to predict the infections of COVID-19. We found that the ARIMA (1, 2, 1) model was able to describe and predict the epidemiological trend of the disease of COVID-19. The MAPE and RMSE for the training set and validation set respectively, which we found was reasonable for use in the forecast. Furthermore, the model also provided forecast total confirmed new cases for the following days. ARIMA model applied to COVID-19 confirmed cases data are an important tool for COVID-19 surveillance all over the world. This study shows that accurate forecasting of the COVID-19 trend is possible using an ARIMA model. Unless strict infection management and control are taken, our findings indicate the potential of COVID-19 to cause greater outbreak all over the world.
References
[2] P. Guan, D.S. Huang, B.S. Zhou, Forecasting model for the incidence of hepatitis A based on artificial neural network, World J Gastroenterol 10 (24) (2004) 3579-3582.
[3] T.A. Reichert, L. Simonsen, A. Sharma, S.A Pardo, D.S. Fedson, M.A. Miller, Influenza and the winter increase in mortality in the United States, 1959-1999, Am J Epidemiol 160 (5) (2004) 492-502.
[4] J. Gaudart, O. Toure, N. Dessay, S. Ranque, L. Forest, J. Demongeot, O.K. Doumbo, Modelling malaria incidence with environmental dependency in a locality of Sudanese savannah area, Mali, Malaria Journal (2009), 61-10.
[5] P.M. Luz, B.V. Mendes, C.T. Codeco, Struchiner CJ, Galvani AP, Time series analysis of dengue incidence in Rio de Janeiro, Brazil, Am J Trop Med Hyg 79 (6) (2008) 933-939.
[6] J. Yi, C.T. Du, R.H. Wang, L. Liu, Applications of multiple seasonal autoregressive integrated moving average(ARIMA) model on predictive incidence of tuberculosis, Chinese Journal of Preventive Medicine 41 (2) (2007) 118-121.
[7] G.E. Box, G.M. Jenkins, Time Series Analysis: Forecasting and Control. Rev. ed, San Francisco: Holden-Day, (1976).
[8] R. Krispin, Hands-On Time Series Analysis with R, Packt Publisher, UK, (2019).
[9] R.J. Hyndman, G. Athanasopoulos, Forecasting: Principles and Practice, 2nd edition, OTexts Publisher, (2018).
[10] R.J. Hyndman, Y. Khandakar, Automatic Time Series Forecasting: The forecast Package for R, Journal of Statistical Software 27 (3) (2008)
[11] L. Liu, R.S. Luan, F. Yin, X.P. Zhu, Q. Lu, Predicting the incidence of hand, foot and mouth disease in Sichuan province, China using the ARIMA model, Epidemiol. Infect 144 (2016) 144–151.
[12] A. Earnest, M.I. Chen, Ng D, Y.S. Leo, Using autoregressive integrated moving average(ARIMA) models to predict and monitor the number of beds occupied during a SARS outbreak in a tertiary hospital in Singapore, BMC Health Services Research 5 (2005) 36-10.
[13] X.J Li, D.M Kang, J. Cao, J.Z. Wang, A time series model in incidence forecasting of hemorrhagic fever with renal syndrome, Journal of Shandong University (Health Sciences) 46 (5) (2008) 547-549.
[14] D. Benvenuto, M. Giovanetti, L. Vassallo, S. Angeletti, M. Ciccozzi, Application of the ARIMA model on the COVID-2019 epidemic dataset, Data in Brief, Published by Elsevier Inc, (2020).
[15] X. Chen, B. Yu, First two months of the 2019 Coronavirus Disease (COVID-19) epidemic in China: realtime surveillance and evaluation with a second derivative model, Global Health Research and Policy, Published by BMC, (2020).
[16] Website: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/advice-for-public.