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Endocrine, Metabolic & Immune Disorders - Drug Targets


ISSN (Print): 1871-5303
ISSN (Online): 2212-3873

Review Article

Prognosticating the Spread of Covid-19 Pandemic Based on Optimal Arima Estimators

Author(s): Venuka Sandhir, Vinod Kumar* and Vikash Kumar*

Volume 21, Issue 4, 2021

Published on: 29 October, 2020

Page: [586 - 591] Pages: 6

DOI: 10.2174/1871530320666201029143122

Price: $65


COVID-19 cases have been reported as a global threat and several studies are being conducted using various modelling techniques to evaluate patterns of disease dispersion in the upcoming weeks. Here we propose a simple statistical model that could be used to predict the epidemiological extent of community spread of COVID-19 from the explicit data based on optimal ARIMA model estimators. Raw data was retrieved on confirmed cases of COVID-19 from Johns Hopkins University ( and the Auto-Regressive Integrated Moving Average (ARIMA) model was fitted based on cumulative daily figures of confirmed cases aggregated globally for ten major countries to predict their incidence trend. Statistical analysis was completed by using R 3.5.3 software. The optimal ARIMA model having the lowest Akaike information criterion (AIC) value for US (0,2,0); Spain (1,2,0); France (0,2,1); Germany (3,2,2); Iran (1,2,1); China (0,2,1); Russia (3,2,1); India (2,2,2); Australia (1,2,0) and South Africa (0,2,2) imparted the nowcasting of trends for the upcoming weeks. These parameters are (p, d, q) where p refers to the number of autoregressive terms, d refers to the number of times the series has to be differenced before it becomes stationary, and q refers to the number of moving average terms. Results obtained from the ARIMA model showed a significant decrease in cases in Australia; a stable case for China and rising cases have been observed in other countries. This study predicted the possible proliferate of COVID-19, although spreading significantly depends upon the various control and measurement policy taken by each country.

Keywords: COVID-19, Coronavirus, statistical analysis, ARIMA model, AIC, forecast.

Graphical Abstract
Cascella, M.; Rajnik, M.; Cuomo, A.; Dulebohn, S.C.; Napoli, R.D. Features, evaluation and treatment coronavirus (COVID-19). Stat Pearls (Internet); StatPearls Publishing: Treasure Island, FL, 2020.
Organization, W.H.O. Laboratory testing for coronavirus disease 2019 (COVID-19) in suspected human cases: interim guidance, 2 March 2020; World Health Organization, 2020.
Shereen, M.A.; Khan, S.; Kazmi, A.; Bashir, N.; Siddique, R. COVID-19 infection: Origin, transmission, and characteristics of human Coronaviruses. J. Adv. Res., 2020, (24), 91-98.
Benvenuto, D.; Giovanetti, M.; Vassallo, L.; Angeletti, S.; Ciccozzi, M. Application of the ARIMA model on the COVID-2019 epidemic dataset. Data Brief, 2020.29105340
Wu, T.; Hu, E.; Ge, X.; Yu, G. Open-source analytics tools for studying the COVID-19 coronavirus outbreak; MEDRXIV, 2020.
He, Z.; Tao, H. Epidemiology and ARIMA model of positive-rate of influenza viruses among children in Wuhan, China: A nine-year retrospective study. Int. J. Infect. Dis., 2018, 7, 61-70.
Wang, Y.W.; Shen, Z.Z.; Jiang, Y. Comparison of ARIMA and GM (1, 1) models for prediction of hepatitis B in China. PLoS One, 2018, 13(9)e0201987
Earnest, A.; Chen, M.I.; Ng, D.; Sin, L.Y. 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 Serv. Res., 2005, 11(5), 36-42.
Gaudart, J.; Touré, O.; Dessay, N.; Lassane, D.A.; Ranque, S.; Forest, L.; Demongeot, J.; Doumbo, O.K. Modelling malaria incidence with environmental dependency in a locality of Sudanese savannah area, Mali. Malar. J., 2009, 8(61)
[ PMID: 19361335]
Liu, Q.; Liu, X.; Jiang, B.; Yang, W. Forecasting incidence of hemorrhagic fever with renal syndrome in China using ARIMA model. BMC Infect. Dis., 2011, 11(1), 218.
Ren, H.; Li, J.; Yuan, Z.A.; Hu, J.Y.; Yu, Y.; Lu, Y.H. The development of a combined mathematical model to forecast the incidence of hepatitis E in Shanghai, China. BMC Infect. Dis., 2013, 13(1), 421.
Kane, M.J.; Price, N.; Scotch, M.; Rabinowitz, P. Comparison of ARIMA and random forest time series models for prediction of avian influenza H5N1 outbreaks. BMC Bioinformatics, 2014, 15(1), 276.
Box, G.E.; Jenkins, G.M.; Reinsel, G.C.; Ljung, G.M. Time series analysis: forecasting and control; John Wiley & Sons, 2015.
Anderson, D.R. Statistics for Business and Economics, 10th Ed; Thomson South-Western, 2008.
Gujarati, D.N.; Porter, D.C. Basic econometrics., 2003.
Chatfield, C. The analysis of time series: an introduction; Chapman & Hall/CRC: Washington, 2004.
Brockwell, P.J.; Davis, R.A. Introduction to time series and forecasting; Springer, 2016.
Dickey, D.A.; Fuller, W.A. Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica: J. Econ. Soc., 1981, 49(4), 1057-1072.
Cheung, Y.W.; Lai, K.S. Lag order and critical values of the augmented Dickey–Fuller test. J. Bus. Econ. Stat., 1995, 13(3), 277-280.
Fattah, J.; Ezzine, L.; Aman, Z.; Moussami, H.E.; Lachhab, A. Forecasting of demand using ARIMA model. Int. J. Eng. Bus. Manag., 2018, 10.
Anwar, M.Y.; Lewnard, J.A.; Parikh, S.; Pitzer, V.E. Time series analysis of malaria in Afghanistan: using ARIMA models to predict future trends in incidence. Malar. J., 2016, 15(1), 566.

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