ARIMA-based forecasting of the dynamics of confirmed Covid-19 cases for selected European countries

Authors

DOI:

https://doi.org/10.24136/eq.2020.009

Keywords:

Covid-19 epidemic, ARIMA model, forecasting, infection control, non-pharmaceutical intervention

Abstract

Research background: On 11 March 2020, the Covid-19 epidemic was identified by the World Health Organization (WHO) as a global pandemic. The rapid increase in the scale of the epidemic has led to the introduction of non-pharmaceutical countermeasures. Forecast of the Covid-19 prevalence is an essential element in the actions undertaken by authorities.

Purpose of the article: The article aims to assess the usefulness of the Auto-regressive Integrated Moving Average (ARIMA) model for predicting the dynamics of Covid-19 incidence at different stages of the epidemic, from the first phase of growth, to the maximum daily incidence, until the phase of the epidemic's extinction.

Methods: ARIMA(p,d,q) models are used to predict the dynamics of virus distribution in many diseases. Model estimates, forecasts, and the accuracy of forecasts are presented in this paper.

Findings & Value added: Using the ARIMA(1,2,0) model for forecasting the dynamics of Covid-19 cases in each stage of the epidemic is a way of evaluating the implemented non-pharmaceutical countermeasures on the dynamics of the epidemic.

Downloads

Download data is not yet available.

References

Ahmar, A. S., & del Val, E. B. (2020). SutteARIMA: short-term forecasting method, a case: Covid-19 and stock market in Spain. Science of The Total Environment, 138883. doi: 10.1016/j.scitotenv.2020. 138883. DOI: https://doi.org/10.1016/j.scitotenv.2020.138883
View in Google Scholar

Ainslie, K. E., Walters, C. E., Fu, H., Bhatia, S., Wang, H., Xi, X., Baguelin, M., Bhatt, S., Boonyasiri, A., Boyd, O., Cattarino, L., Ciavarella, C., Cucunuba, Z., Cuomo-Dannenburg, G., Dighe, A., Dorigatti, I., van Elsland, S. L., FitzJohn, R., Gaythorpe, K., Ghani, A. C., Green, W., Hamlet, A., Hinsley, W., Imai, N., Jorgensen, D., Knock, E., Laydon, D., Nedjati-Gilani, G., Okell, L. C., Siveroni, I., Thompson, H. A., Unwin, H. J. T., Verity, R., Vollmer, M., Walker, P. G. T., Wang, Y., Watson, O. J., Whittaker, C., Winskill, P., Donnelly, C. A. (2020). Evidence of initial success for China exiting COVID-19 social distancing policy after achieving containment. Welcome Open Research, 5(81). doi: 10.12688/wellcome openres.15843.1. DOI: https://doi.org/10.12688/wellcomeopenres.15843.2
View in Google Scholar

Alamo, T., Reina, D. G., Mammarella, M., & Abella, A. (2020). Open data resources for fighting covid-19. arXiv preprint arXiv:2004.06111.
View in Google Scholar

Azad, S., & Poonia, N. (2020). Short-term forecasts of COVID-19 spread across Indian states until 1 May 2020. Preprints 2020, 2020040491. doi: 10.20944/preprints202004.0491.v1. DOI: https://doi.org/10.20944/preprints202004.0491.v1
View in Google Scholar

Baiocchi, G., & Distaso, W. (2003). GRETL: econometric software for the GNU generation. Journal of Applied Econometrics, 18(1). DOI: https://doi.org/10.1002/jae.704
View in Google Scholar

Bandt, C. (2020). Transparent covid-19 prediction. arXiv preprint arXiv:2004.04732.
View in Google Scholar

Benvenuto, D., Giovanetti, M., Vassallo, L., Angeletti, S., & Ciccozzi, M. (2020). Application of the ARIMA model on the COVID-2019 epidemic dataset. Data in brief, 105340. doi: 10.1016/j.dib.2020.105340. DOI: https://doi.org/10.1016/j.dib.2020.105340
View in Google Scholar

Bertschinger, N. (2020). Visual explanation of country specific differences in Covid-19 dynamics. arXiv preprint arXiv:2004.0733c4.
View in Google Scholar

Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: forecasting and control. John Wiley & Sons.
View in Google Scholar

Calvetti, D., Hoover, A., Rose, J., & Somersalo, E. (2020). Bayesian dynamical estimation of the parameters of an SE (A) IR COVID-19 spread model. arXiv preprint arXiv:2005.04365.
View in Google Scholar

Casella, F. (2020). Can the COVID-19 epidemic be managed on the basis of daily data? arXiv preprint arXiv:2003.06967. DOI: https://doi.org/10.1109/LCSYS.2020.3009912
View in Google Scholar

Centeno, R. S., & Marquez, J. P. (2020). How much did the tourism industry lost? Estimating earning loss of tourism in the Philippines. arXiv preprint arXiv:2004.09952.
View in Google Scholar

Ceylan, Z. (2020). Estimation of COVID-19 prevalence in Italy, Spain, and France. Science of The Total Environment, 138817. doi: 10.1016/j.scitotenv.2020. 138817. DOI: https://doi.org/10.1016/j.scitotenv.2020.138817
View in Google Scholar

Chakraborty, T., & Ghosh, I. (2020). Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: a data-driven analysis. Chaos, Solitons & Fractals, 135. doi: 10.1016/j.chaos.2020.109850. DOI: https://doi.org/10.1016/j.chaos.2020.109850
View in Google Scholar

Chimmula, V. K. R., & Zhang, L. (2020). Time series forecasting of COVID-19 transmission in Canada using LSTM networks. Chaos, Solitons & Fractals, 135. doi: 10.1016/j.chaos.2020.109864. DOI: https://doi.org/10.1016/j.chaos.2020.109864
View in Google Scholar

Chintalapudi, N., Battineni, G., & Amenta, F. (2020). COVID-19 disease outbreak forecasting of registered and recovered cases after sixty-day lockdown in Italy: a data driven model approach. Journal of Microbiology, Immunology and Infection, 53(3). doi:10.1016/j.jmii.2020.04.004. DOI: https://doi.org/10.1016/j.jmii.2020.04.004
View in Google Scholar

Cottrell, A., & Lucchetti, R., Gretl user’s guide, gnu regression, econometric time-series library, gretl.sourceforge.net. Retrieved from http:/ricardo.ecn.wfu. edu/pub/gretl/manual/PDF/gretl-guide-a4.pdf.
View in Google Scholar

de Wolff, T., Pflüger, D., Rehme, M., Heuer, J., & Bittner, M. I. (2020). Evaluation of pool-based testing approaches to enable population-wide screening for COVID-19. arXiv preprint arXiv:2004.11851. DOI: https://doi.org/10.1371/journal.pone.0243692
View in Google Scholar

Dehesh, T., Mardani-Fard, H. A., & Dehesh, P. (2020). Forecasting of covid-19 confirmed cases in different countries with ARIMA models. medRxiv. preprint doi: 10.1101/2020.03.13.20035345. DOI: https://doi.org/10.1101/2020.03.13.20035345
View in Google Scholar

Dickey, D. A., & Fuller, W. A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica: Journal of the Econometric Society, 49(4). DOI: https://doi.org/10.2307/1912517
View in Google Scholar

Ding, G., Li, X., Shen, Y., & Fan, J. (2020). Brief Analysis of the ARIMA model on the COVID-19 in Italy. medRxiv preprint doi: 10.1101/2020.04.08. 20058636. DOI: https://doi.org/10.1101/2020.04.08.20058636
View in Google Scholar

Fanelli, D., & Piazza, F. (2020). Analysis and forecast of COVID-19 spreading in China, Italy and France. Chaos, Solitons & Fractals, 134. doi: 10.1016/j.chaos. 2020.109761. DOI: https://doi.org/10.1016/j.chaos.2020.109761
View in Google Scholar

Fattah, J., Ezzine, L., Aman, Z., El Moussami, H., & Lachhab, A. (2018). Forecasting of demand using ARIMA model. International Journal of Engineering Business Management, 10. doi: 10.1177/1847979018808673. DOI: https://doi.org/10.1177/1847979018808673
View in Google Scholar

Flaxman, S., Mishra, S., Gandy, A., Unwin, H. J. T., Coupland, H., Mellan, T. A., Zhu, H., Berah, T., Eaton, J. W., Guzman, P. N. P., Schmit, N., Callizo, L., Imperial College COVID-19 Response Team, Whittaker, C., Winskill, P., Xi, X., Ghani, A., Donnelly, C. A., Riley, S., Okell, L. C., Vollmer, M. A. C., Ferguson, N. M., & Bhatt, S. (2020). Estimating the number of infections and the impact of non-pharmaceutical interventions on COVID-19 in European countries: technical description update. arXiv preprint arXiv:2004.11342.
View in Google Scholar

Flaxman, S., Mishra, S., Gandy, A., Unwin, H., Coupland, H., Mellan, T., Zhu, H., Berah, T., Eaton, J., Perez Guzman, P., Schmit, N., Cilloni, L., Ainslie, K., Baguelin, M., Blake, I., Boonyasiri, A., Boyd, O., Cattarino, L., Ciavarella, C., Cooper, L., Cucunuba Perez, Z., Cuomo-Dannenburg, G., Dighe, A., Djaafara, A., Dorigatti, I., Van Elsland, S., Fitzjohn, R., Fu, H., Gaythorpe, K., Geidelberg, L., Grassly, N., Green, W., Hallett, T., Hamlet, A., Hinsley, W., Jeffrey, B., Jorgensen, D., Knock, E., Laydon, D., Nedjati Gilani, G., Nouvellet, P., Parag, K., Siveroni, I., Thompson, H., Verity, R., Volz, E., Walters, C., Wang, H., Wang, Y., Watson, O., Winskill, P., Xi, X., Whittaker, C., Walker, P., Ghani, A., Donnelly, C., Riley, S., Okell, L., Vollmer, M., Ferguson, N., & Bhatt, S. (2020a). Report 13: estimating the number of infections and the impact of non-pharmaceutical interventions on COVID-19 in 11 European countries. Imperial College London. doi: 10.25561/77731. DOI: https://doi.org/10.1038/s41586-020-2405-7
View in Google Scholar

Fong, S. J., Li, G., Dey, N., Crespo, R. G., & Herrera-Viedma, E. (2020). Composite Monte Carlo decision making under high uncertainty of novel coronavirus epidemic using hybridized deep learning and fuzzy rule induction. Applied Soft Computing, 106282. doi:.10.1016/j.asoc.2020.106282. DOI: https://doi.org/10.1016/j.asoc.2020.106282
View in Google Scholar

Grassly, N. C., Pons-Salort, M., Parker, E. P. K., White, P. J., Ainslie, K., Baguelin, M., Bhatia, S., Bhatt, S., Blake, I., Boonyasiri, A., Boyd, O., Brazeau, N., Cattarino, L., Charles, G., Ciavarella, C., Cooper, L.V., Coupland, H., Cucunuba, Z., Cuomo-Dannenburg, G., Dighe, A., Djaafara, V., Donnelly, C., Dorigatti, I., Eaton, J., van Elsland, S. L., Ferreira, F., Nascimento, D., FitzJohn, R., Flaxman, S., Fraser, K., Fu, H., Gaythorpe, K., Geidelberg, L., Ghani, A., Green, W., Hallett, T., Hamlet, A., Hauck, K., Haw, D., Hayes, S., Hinsley, W., Imai, N., Jeffrey, B., Jorgensen, D., Knock, E., Laydon, D., Lees, J., Mangal, T., Mellan, T., Mishra, S., Mousa, A., Nedjati-Gilani, G., Nouvellet, P., Okell, L., Olivera, D., Ower, A., Parag, K. V., Pickles, M., Ragonnet-Cronin, M., Riley, S., Siveroni, I., Stopard, I., Thompson, H. A., Unwin, H. J. Y., Verity, R., Vollmer, M., Volz, E., Walker, P., Walters, C., Wang, H., Wang, Y., Watson, O. J., Whittaker, C., Whittles, L., Winskill, P., Xi, X., & Ferguson, N. (2020). Report 16: role of testing in COVID-19 control. Imperial College London. doi: 10.25561/78439.
View in Google Scholar

Guzzetta, G., Riccardo, F., Marziano, V., Poletti, P., Trentini, F., Bella, A., Andrianou, X., Del Manso, M., Fabiani, M., Bellino, S., Boros, S., Urdiales, A.M., Vescio, M. F., Piccioli, A., COVID-19 working group, Brusaferro, S., Rezza, G., Pezzotti, P., Ajelli, M., & Merler, S. (2020). The impact of a nation-wide lockdown on COVID-19 transmissibility in Italy. arXiv preprint arXiv:2004.12338.
View in Google Scholar

Hotz, T., Glock, M., Heyder, S., Semper, S., Böhle, A., & Krämer, A. (2020). Monitoring the spread of COVID-19 by estimating reproduction numbers over time. arXiv preprint arXiv:2004.08557.
View in Google Scholar

Iacus, S. M., Natale, F., Santamaria, C., Spyratos, S., & Vespe, M. (2020). Estimating and projecting air passenger traffic during the COVID-19 coronavirus outbreak and its socio-economic impact. Safety Science, 104791. doi: 10.1016/j. ssci.2020.104791. DOI: https://doi.org/10.1016/j.ssci.2020.104791
View in Google Scholar

Johns Hopkins University Center for Systems Science and Engineering, Coronavirus (COVID-19) Cases. Retrieved form https://github.com/CSSEGISandData/ COVID-19 (30.05.2020).
View in Google Scholar

Karina, A. C., Fernando, A. M., Morteza, N. N., & Michael, H. (2020). Forecasting the effect of COVID-19 on the S&P500. arXiv preprint arXiv:2005.03969.
View in Google Scholar

Kevrekidis, P. G., Cuevas-Maraver, J., Drossinos, Y., Rapti, Z., & Kevrekidis, G. A. (2020). Spatial modeling of COVID-19: Greece and Andalusia as case examples. arXiv preprint arXiv:2005.04527. DOI: https://doi.org/10.1103/PhysRevE.104.024412
View in Google Scholar

Kobayashi, G., Sugasawa, S., Tamae, H., & Ozu, T. (2020). Predicting infection of COVID-19 in Japan: state space modeling approach. arXiv preprint arXiv:2004.13483, 2020. DOI: https://doi.org/10.5582/bst.2020.03133
View in Google Scholar

Kucharski, A. J., Russell, T. W., Diamond, C., Liu, Y., Edmunds, J., Funk, S., & Eggo, R. M., (2020). Early dynamics of transmission and control of COVID-19: a mathematical modelling study. Lancet Infectious Diseases, 20(5). doi: 10.1016/S1473-3099(20)30144-4. DOI: https://doi.org/10.1016/S1473-3099(20)30144-4
View in Google Scholar

Kumar, P., Kalita, H., Patairiya, S., Sharma, Y. D., Nanda, C., Rani, M., Rahmani, J., & Bhagavathula, A. S. (2020). Forecasting the dynamics of COVID-19 pandemic in top 15 countries in April 2020: ARIMA model with machine learning approach. medRxiv: 2020.03.30.20046227; doi: 10.1101/2020. 03.30.20046227. DOI: https://doi.org/10.1101/2020.03.30.20046227
View in Google Scholar

Kumar, S., Sharma, S., & Kumari, N. (2020). Future of COVID-19 in Italy: a mathematical perspective. arXiv preprint arXiv:2004.08588.
View in Google Scholar

Kuniya, T. (2020). Prediction of the epidemic peak of coronavirus disease in Japan, 2020. Journal of Clinical Medicine, 9(3). doi: 10.3390/ jcm9030789. DOI: https://doi.org/10.3390/jcm9030789
View in Google Scholar

Lesniewski, A. (2020). Epidemic control via stochastic optimal control. arXiv preprint arXiv:2004.06680.
View in Google Scholar

Li, Y, Wang, B, Peng, R, Zhou, C, Zhan, Y, Liu., Z, Jiang., X., & B., Zhao (2020. Mathematical modeling and epidemic prediction of COVID-19 and its significance to epidemic prevention and control measures. Annals Infectious Disease Epidemiology, 5(1).
View in Google Scholar

Magri, L., & Doan, N. A. K. (2020). First-principles machine learning modelling of COVID-19. arXiv preprint arXiv:2004.09478.
View in Google Scholar

Marsland III, R., & Mehta, P. (2020). Data-driven modeling reveals a universal dynamic underlying the COVID-19 pandemic under social distancing. medRxiv 2020.04.21.20073890; doi: 10.1101/ /2020.04.21.20073890.
View in Google Scholar

Mena, R. H., Velasco-Hernandez, J. X., Mantilla-Beniers, N. B., Carranco-Sapiéns, G. A., Benet, L., Boyer, D., & Castillo, I. P. (2020). Using the posterior predictive distribution to analyse epidemic models: COVID-19 in Mexico City. arXiv preprint arXiv:2005.02294. DOI: https://doi.org/10.1088/1478-3975/abb115
View in Google Scholar

Mora, J. C., Pérez, S., Rodriguez, I., Nunez, A., & Dvorzhak, A. (2020). A semiempirical dynamical model to forecast the propagation of epidemics: the case of the Sars-Cov-2 in Spain. arXiv preprint arXiv:2004.08990.
View in Google Scholar

Narajewski, M., & Ziel, F. (2020). Changes in electricity demand pattern in Europe due to COVID-19 shutdowns. arXiv preprint arXiv:2004.14864.
View in Google Scholar

Novel Coronavirus (COVID-19) cases, provided by John Hopkins University CSSE. Retrieved form https://github.com/CSSEGISandData/COVID-19.
View in Google Scholar

Pai, C., Bhaskar, A., & Rawoot, V. (2020). Investigating the dynamics of COVID-19 pandemic in India under lockdown. arXiv preprint arXiv:2004.13337, 2020. DOI: https://doi.org/10.1016/j.chaos.2020.109988
View in Google Scholar

Patwardhan, C. (2020). SARS-COV-2 pandemic: understanding the impact of lockdown in the most affected states of India. arXiv preprint arXiv:2004.13632.
View in Google Scholar

Perone, G. (2020). An ARIMA model to forecast the spread and the final size of COVID-2019 epidemic in Italy. HEDG - Health Econometrics and Data Group Working Paper Series, University of York. doi: 10.2139/ssrn.3564865. DOI: https://doi.org/10.1101/2020.04.27.20081539
View in Google Scholar

Pugliese, A., & Sottile, S. (2020). Inferring the COVID-19 infection curve in Italy. arXiv preprint arXiv:2004.09404.
View in Google Scholar

Radiom, M., & Berret, J. F. (2020). Common trends in the epidemic of Covid-19 disease. arXiv preprint arXiv:2004.12124. DOI: https://doi.org/10.1140/epjp/s13360-020-00526-1
View in Google Scholar

Ribeiro, M. H. D. M., da Silva, R. G., Mariani, V. C., & dos Santos Coelho, L. (2020). Short-term forecasting COVID-19 cumulative confirmed cases: perspectives for Brazil. Chaos, Solitons & Fractals, 109853. doi: 10.1016/ j.chaos.2020.109853. DOI: https://doi.org/10.1016/j.chaos.2020.109853
View in Google Scholar

Rogers, L. C. G. (2020). Ending the COVID-19 epidemic in the United Kingdom. arXiv preprint arXiv:2004.12462.
View in Google Scholar

Singh, S., Parmar, K. S., Kumar, J., & Makkhan, S. J. S. (2020). Development of new hybrid model of discrete wavelet decomposition and autoregressive integrated moving average (ARIMA) models in application to one month forecast the casualties cases of COVID-19. Chaos, Solitons & Fractals, 109866. doi: 10.1016/j.chaos.2020.109866. DOI: https://doi.org/10.1016/j.chaos.2020.109866
View in Google Scholar

Sonnino, G. (2020). Dynamics of the COVID-19--comparison between the theoretical predictions and real data. arXiv preprint arXiv:2003.13540.
View in Google Scholar

Tandon, H., Ranjan, P., Chakraborty, T., & Suhag, V. (2020). Coronavirus (COVID-19): ARIMA based time-series analysis to forecast near future. arXiv preprint arXiv:2004.07859.
View in Google Scholar

Tarassow, A. (2020). ARIMA-based forecasting of confirmed COVID/ Corona cases for various country-province combinations. Retrieved from https://github.com/atecon/covid_19_forecast (30.05.2020).
View in Google Scholar

Tuli, S., Tuli, S., Tuli, R., & Gill, S. S. (2020). Predicting the growth and trend of COVID-19 pandemic using machine learning and cloud computing. Internet of Things, 100222. doi: 10.1016/j.iot.2020.100222. DOI: https://doi.org/10.1016/j.iot.2020.100222
View in Google Scholar

Vattay, G. (2020). Forecasting the outcome and estimating the epidemic model parameters from the fatality time series in COVID-19 outbreaks. arXiv preprint arXiv:2004.08973. DOI: https://doi.org/10.1088/1478-3975/abac69
View in Google Scholar

Wang, L., Wang, G., Gao, L., Li, X., Yu, S., Kim, M., Wang, Y., & Gu, Z. (2020). Spatiotemporal dynamics, nowcasting and forecasting of COVID-19 in the United States. arXiv preprint arXiv:2004.14103. DOI: https://doi.org/10.1090/noti2263
View in Google Scholar

World Health Organization, Coronavirus disease (COVID-19) outbreak. Retrieved form https://www.who.int/emergencies/diseases/novel-coronavirus-2019
View in Google Scholar

(30.05.2020).
View in Google Scholar

Wu, J. T., Leung, K., & Leung, G. M. (2020). Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study. Lancet, 395(10225). doi: 10.1016/S0140-6736(20)30260-9. DOI: https://doi.org/10.1016/S0140-6736(20)30260-9
View in Google Scholar

Xu, C., Yu, Y., Yang, Q., & Lu, Z. (2020). Forecast analysis of the epidemics trend of COVID-19 in the United States by a generalized fractional-order SEIR model. arXiv preprint arXiv:2004.12541. DOI: https://doi.org/10.1101/2020.04.24.20078493
View in Google Scholar

Yan, B., Tang, X., Liu, B., Wang, J., Zhou, Y., Zheng, G., Zou, Q., Lu, Y., & Tu, W. (2020). An improved method of COVID-19 case fitting and prediction based on LSTM. arXiv preprint arXiv:2005.03446.
View in Google Scholar

Yang, C., Sha, D., Liu, Q., Li, Y., Lan, H., Guan, W. W., Hu, T., Li, Z., Zhang, Z., Thompson, J.H., Wang, Z., Wong, D., Ruan, S., Yu, M., Richardson, D., Zhang, L., Hou, R., Zhou, Y., Zhong, C., Tian, Y., Beaini, F., Carte, K., Flynn, C., Liu, W., Pfoser, D., Bao, S., Li, M., Zhang, H., Liu, C., Jiang, J., Du, S., Zhao, L., Lu, M., Li, L., & Zhou,H. (2020). Taking the pulse of COVID-19: a spatiotemporal perspective. arXiv preprint arXiv:2005.04224. DOI: https://doi.org/10.1080/17538947.2020.1809723
View in Google Scholar

Yonar, H, Yonar, A, Tekindal, M. A, & Tekindal. M. (2020). Modeling and forecasting for the number of cases of the COVID-19 pandemic with the curve estimation models, the Box-Jenkins and exponential smoothing methods. Eurasian Journal of Medicine and Oncology, 4(2). doi: 10.14744/ejmo.2020.28273. DOI: https://doi.org/10.14744/ejmo.2020.28273
View in Google Scholar

Yudistira, N. (2020). COVID-19 growth prediction using multivariate long short term memory. arXiv preprint arXiv:2005.04809.
View in Google Scholar

Zhang, Y., Yang, H., Cui, H., & Chen, Q. (2019). Comparison of the ability of ARIMA, WNN and SVM models for drought forecasting in the Sanjiang Plain, China. Natural Resources Research. doi:10.1007/s11053-019-09512-6. DOI: https://doi.org/10.1007/s11053-019-09512-6
View in Google Scholar

Downloads

Published

24-06-2020

Issue

Section

Articles

How to Cite

Kufel, T. (2020). ARIMA-based forecasting of the dynamics of confirmed Covid-19 cases for selected European countries. Equilibrium. Quarterly Journal of Economics and Economic Policy, 15(2), 181-204. https://doi.org/10.24136/eq.2020.009

Similar Articles

1-10 of 197

You may also start an advanced similarity search for this article.