Prediction of daily COVID-19 cases in European countries using automatic ARIMA model

Abstract

The recent pandemic (COVID-19) emerged in Wuhan city of China and after causing a lot of destruction there recently changed its epicenter to Europe. There are countless people affected and reported cases are increasing day by day. Predictive models need to consider previous reported cases and forecast the upcoming number of cases. Automatic ARIMA, one of the predictive models used for forecasting contagions, was used in this study to predict the number of confirmed cases for next 10 days in four top European countries through R package “forecast”. The study finds that Auto ARIMA applied on the sample satisfactorily forecasts the confirmed cases of coronavirus for next ten days. The confirmed cases for the four countries show an increasing trend for the next ten days with Spain with a highest number of expected new confirmed cases, followed by Germany and France. Italy is expected to have lowest number of new confirmed cases among the four countries.

Dimensions

Altmetric

PlumX Metrics

Downloads

Download data is not yet available.

References

1. Dong E, Du H, Gardner L. An interactive web-based dashboard to track COVID-19 in real time. The Lancet Infectious Diseases. 2020.
2. Yu G, Yanfeng P, Rui Y, Yuding F, Danmeng M. 独家|新冠病毒基因测序溯源:警报是何时拉响的. 2020 26.02.
3. Asia B. Coronavirus disease named Covid-19. BBC News China 2020 11.02.
4. CNA. WHO names novel coronavirus as 'COVID-19'. Channel News Asia. 2020 11.02.
5. Chappell B. Coronavirus: COVID-19 Is Now Officially A Pandemic, WHO Says: NPR; 2020 [Available from: https://www.npr.org/sections/goatsandsoda/2020/03/11/814474930/coronavirus-covid-19-is-now-officially-a-pandemic-who-says.
6. BBC. Coronavirus confirmed as pandemic by World Health Organization. BBC World. 2020.
7. Secon H, Woodward A, Mosher D. A comprehensive timeline of the new coronavirus pandemic, from China's first COVID-19 case to the present. Business Recorder. 2020 20.03.
8. BBC. Coronavirus: Europe now epicentre of the pandemic, says WHO. BBC News. 2020 13.03.
9. WHO. Infectious disease outbreaks reported in the Eastern Mediterranean Region in 2018: WHO; 2018 [Available from: https://www.emro.who.int/pandemic-epidemic-diseases/news/infectious-disease-outbreaks-reported-in-the-eastern-mediterranean-region-in-2018.html.
10. WHO. Middle East respiratory syndrome coronavirus (MERS-CoV) – Republic of Korea: WHO; 2015 [Available from: https://www.who.int/csr/don/25-october-2015-mers-korea/en/.
11. WHO. Case‐control study to assess potential risk factors related to human illness caused by Middle East Respiratory Syndrome Coronavirus (MERS‐CoV). Protocol Update. WHO; 2014 28.03.2014.
12. Smith RD. Responding to global infectious disease outbreaks: lessons from SARS on the role of risk perception, communication and management. Social science & medicine. 2006;63(12):3113-23.
13. Control ECfDPa. Situation update for the EU/EEA and the UK: ECDPC; 2020 [Available from: https://www.ecdc.europa.eu/en/cases-2019-ncov-eueea.
14. Wu N, Green B, Ben X, O'Banion S. Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case. arXiv preprint arXiv:200108317. 2020.
15. Contreras J, Espinola R, Nogales FJ, Conejo AJ. ARIMA models to predict next-day electricity prices. IEEE transactions on power systems. 2003;18(3):1014-20.
16. Ediger VŞ, Akar S. ARIMA forecasting of primary energy demand by fuel in Turkey. Energy policy. 2007;35(3):1701-8.
17. Ariyo AA, Adewumi AO, Ayo CK, editors. Stock price prediction using the ARIMA model. 2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation; 2014: IEEE.
18. Faruk DÖ. A hybrid neural network and ARIMA model for water quality time series prediction. Engineering applications of artificial intelligence. 2010;23(4):586-94.
19. Van Der Voort M, Dougherty M, Watson S. Combining Kohonen maps with ARIMA time series models to forecast traffic flow. Transportation Research Part C: Emerging Technologies. 1996;4(5):307-18.
20. Cong J, Ren M, Xie S, Wang P. Predicting Seasonal Influenza Based on SARIMA Model, in Mainland China from 2005 to 2018. International Journal of Environmental Research and Public Health. 2019;16(23):4760.
21. Su K, Xu L, Li G, Ruan X, Li X, Deng P, et al. Forecasting influenza activity using self-adaptive AI model and multi-source data in Chongqing, China. EBioMedicine. 2019;47:284-92.
22. Tapak L, Hamidi O, Fathian M, Karami M. Comparative evaluation of time series models for predicting influenza outbreaks: application of influenza-like illness data from sentinel sites of healthcare centers in Iran. BMC research notes. 2019;12(1):353.
23. Stoeldraijer L. Mortality forecasting in the context of non-linear past mortality trends: an evaluation: Rijksuniversiteit Groningen; 2019.
24. Kim TH, Hong KJ, Do Shin S, Park GJ, Kim S, Hong N. Forecasting respiratory infectious outbreaks using ED-based syndromic surveillance for febrile ED visits in a Metropolitan City. The American journal of emergency medicine. 2019;37(2):183-8.
25. Wang L, Chen J, Marathe M, editors. DEFSI: Deep learning based epidemic forecasting with synthetic information. Proceedings of the AAAI Conference on Artificial Intelligence; 2019.
26. Benvenuto D, Giovanetti M, Vassallo L, Angeletti S, Ciccozzi M. Application of the ARIMA model on the COVID-2019 epidemic dataset. Data in brief. 2020:105340.
27. Roosa K, Lee Y, Luo R, Kirpich A, Rothenberg R, Hyman J, et al. Real-time forecasts of the COVID-19 epidemic in China from February 5th to February 24th, 2020. Infectious Disease Modelling. 2020;5:256-63.
28. Box GE, Jenkins GM. Time series analysis: Forecasting and control San Francisco. Calif: Holden-Day. 1976.
29. Zhang GP. Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing. 2003;50:159-75.
30. Brockwell PJ, Davis RA. Introduction to time series and forecasting: springer; 2016.
31. Hipel KW, McLeod AI, Lennox WC. Advances in Box‐Jenkins modeling: 1. Model construction. Water Resources Research. 1977;13(3):567-75.
32. Hyndman RJ, Athanasopoulos G, Bergmeir C, Caceres G, Chhay L, O'Hara-Wild M, et al. Package ‘forecast’. Online] https://cran r-project org/web/packages/forecast/forecast pdf. 2020.
33. Wang X, Smith K, Hyndman R. Characteristic-based clustering for time series data. Data mining and knowledge Discovery. 2006;13(3):335-64.
34. Jain G, Mallick B. A study of time series models ARIMA and ETS. Available at SSRN 2898968. 2017.
35. Dhamo E, Puka L, editors. Using the R-package to forecast time series: ARIMA models and Application. INTERNATIONAL CONFERENCE Economic & Social Challenges and Problems 2010 Facing Impact of Global Crisis; 2010.
36. Pravilović S, Appice A. The intelligent forecasting model of time series. Automation, Control and Intelligent Systems. 2014;1(4):90.
Published
2020-07-08
Info
Issue
Section
Original Articles
Keywords:
Prediction, COVID-19, Auto ARIMA, Europe
Statistics
  • Abstract views: 341

  • PDF: 87
How to Cite
Awan, T. M., & Aslam, F. (2020). Prediction of daily COVID-19 cases in European countries using automatic ARIMA model. Journal of Public Health Research, 9(3). https://doi.org/10.4081/jphr.2020.1765