COVID-19: A Comparison of Time Series Methods to Forecast Percentage of Active Cases per Population
Source abstract
Abstract
The ongoing COVID-19 pandemic has caused worldwide socioeconomic unrest, forcing governments to introduce extreme measures to reduce its spread. Being able to accurately forecast when the outbreak will hit its peak would significantly diminish the impact of the disease, as it would allow governments to alter their policy accordingly and plan ahead for the preventive steps needed such as public health messaging, raising awareness of citizens and increasing the capacity of the health system. This study investigated the accuracy of a variety of time series modeling approaches for coronavirus outbreak detection in ten different countries with the highest number of confirmed cases as of 4 May 2020. For each of these countries, six different time series approaches were developed and compared using two publicly available datasets regarding the progression of the virus in each country and the population of each country, respectively. The results demonstrate that, given data produced using actual testing for a small portion of the population, machine learning time series methods can learn and scale to accurately estimate the percentage of the total population that will become affected in the future.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: COVID-19 epidemiological studies · Data-Driven Disease Surveillance · COVID-19 diagnosis using AI
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Mathematics, University of Patras, 26504 Patras, Greece
References (64)
Show source-provided references
1. World Health Organization (2020, May 02). Naming the Coronavirus Disease (COVID-19) and the Virus that Causes it. World Health Organization. Available online: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/technical-guidance/naming-the-coronavirus-disease-.
2. Coronaviridae Study Group (2020). The species Severe acute respiratory syndrome-related coronavirus: Classifying 2019-nCoV and naming it SARS-CoV-2. Nat. Microbiol., 5, 536.10.1038/s41564-020-0695-z ↗
3. Outbreak of Pneumonia of Unknown Etiology in Wuhan China: The Mystery and the Miracle10.1002/jmv.25678 ↗
4. Fernandes, N. (2020, May 04). Economic Effects of Coronavirus Outbreak (COVID-19) on the World Economy. Available online: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3557504.10.2139/ssrn.3557504 ↗
5. J. CSSE (2020, May 04). Coronavirus COVID-19 Global Cases by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU). Available online: https://coronavirus.jhu.edu/map.html.
6. Mass gathering events and reducing further global spread of COVID-19: A political and public health dilemma10.1016/S0140-6736(20)30681-4 ↗
7. COVID-19: Getting ahead of the epidemic curve by early implementation of social distancing10.7196/SAMJ.2020.v110i4.14720 ↗
8. Coronavirus Disease 2019 (COVID-19): Protecting hospitals from the invisible10.7326/M20-0751 ↗
9. WHO (2020). Laboratory Testing for Coronavirus Disease 2019 (COVID-19) in Suspected Human Cases: Interim Guidance, 2 March 2020; Technical report. WHO.
10. Roser, M., Ritchie, H., and Ortiz-Ospina, E. (2020). Coronavirus Disease (COVID-19)–Statistics and Research. Our World Data, Available online: https://ourworldindata.org/coronavirus.
11. Developing antibody tests for SARS-CoV-210.1016/S0140-6736(20)30788-1 ↗
12. Vogel, G. (2020). New Blood Tests for Antibodies Could Show True Scale of Coronavirus Pandemic. Science, 19, Available online: https://www.sciencemag.org/news/2020/03/new-blood-tests-antibodies-could-show-true-scale-coronavirus-pandemic.10.1126/science.abb8028 ↗
13. Pang, J., Wang, M.X., Ang, I.Y.H., Tan, S.H.X., Lewis, R.F., Chen, J.I.P., Gutierrez, R.A., Gwee, S.X.W., Chua, P.E.Y., and Yang, Q. (2020). Potential rapid diagnostics, vaccine and therapeutics for 2019 novel coronavirus (2019-nCoV): A systematic review. J. Clin. Med., 9.10.3390/jcm9030623 ↗
14. Box, G., and Jenkins, G. (2015). Time Series Analysis Forecasting and Control/’Holden Day, San Francisco, California, 1970, John Wiley & Sons.
15. The Holt–Winters forecasting procedure
16. Forecasting time series with complex seasonal patterns using exponential smoothing10.1198/jasa.2011.tm09771 ↗
17. Forecasting at scale10.1080/00031305.2017.1380080 ↗
18. Salinas, D., Flunkert, V., Gasthaus, J., and Januschowski, T. (2019). DeepAR: Probabilistic forecasting with autoregressive recurrent networks. Int. J. Forecast.10.1016/j.ijforecast.2019.07.001 ↗
19. Alexandrov, A., Benidis, K., Bohlke-Schneider, M., Flunkert, V., Gasthaus, J., Januschowski, T., Maddix, D.C., Rangapuram, S., Salinas, D., and Schulz, J. (2019). Gluonts: Probabilistic time series models in python. arXiv.
20. Oreshkin, B.N., Carpov, D., Chapados, N., and Bengio, Y. (2019). N-BEATS: Neural basis expansion analysis for interpretable time series forecasting. arXiv.
21. Modeling seasonal leptospirosis transmission and its association with rainfall and temperature in Thailand using time–series and ARIMAX analyses10.1016/S1995-7645(12)60095-9 ↗
22. The role of El Ni no southern oscillation (ENSO) on variations of monthly Plasmodium falciparum malaria cases at the cayenne general hospital, 1996–2009, French Guiana10.1186/1475-2875-10-100 ↗
23. Time series analysis of influenza incidence in Chinese provinces from 2004 to 201110.1097/MD.0000000000003929 ↗
24. Adhikari, R., and Agrawal, R.K. (2013). An introductory study on time series modeling and forecasting. arXiv.
25. Tempel: Time-series mutation prediction of influenza A viruses via attention-based recurrent neural networks10.1093/bioinformatics/btaa050 ↗
26. Lee, K., Agrawal, A., and Choudhary, A. (2017, January 23–26). Forecasting Influenza Levels Using Real-time Social Media Streams. Proceedings of the 2017 IEEE International Conference on Healthcare Informatics (ICHI), Park City, UT, USA.10.1109/ICHI.2017.68 ↗
27. Predicting seasonal influenza epidemics using cross-hemisphere influenza surveillance data and local Internet query data
28. Soebiyanto, R.P., Adimi, F., and Kiang, R.K. (2010). Modeling and predicting seasonal influenza transmission in warm regions using climatological parameters. PLoS ONE, 5.10.1371/journal.pone.0009450 ↗
29. Monitoring mortality as an indicator of influenza in Catalonia, Spain10.1136/jech.50.3.293 ↗
30. Real-time forecasts of the COVID-19 epidemic in China from 5 February to 24 February 2020
31. Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions10.21037/jtd.2020.02.64 ↗
32. Trend and forecasting of the COVID-19 outbreak in China
33. Hu, Z., Ge, Q., Jin, L., and Xiong, M. (2020). Artificial intelligence forecasting of covid-19 in china. arXiv.10.18562/IJEE.054 ↗
34. Al-qaness, M.A., Ewees, A.A., Fan, H., and Abd El Aziz, M. (2020). Optimization method for forecasting confirmed cases of covid-19 in China. J. Clin. Med., 9.10.3390/jcm9030674 ↗
35. Analysis and forecast of COVID-19 spreading in China, Italy and France10.1016/j.chaos.2020.109761 ↗
36. Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: A modelling study10.1016/S0140-6736(20)30260-9 ↗
37. Anastassopoulou, C., Russo, L., Tsakris, A., and Siettos, C. (2020). Data-based analysis, modelling and forecasting of the COVID-19 outbreak. PLoS ONE, 15.10.1101/2020.02.11.20022186 ↗
38. Estimation of the reproductive number of novel coronavirus (COVID-19) and the probable outbreak size on the Diamond Princess cruise ship: A data-driven analysis10.1016/j.ijid.2020.02.033 ↗
39. IHME COVID-19 Health Service Utilization Forecasting Team (2020). Forecasting COVID-19 impact on hospital bed-days, ICU-days, ventilator-days and deaths by US state in the next 4 months. medRxiv.
40. Petropoulos, F., and Makridakis, S. (2020). Forecasting the novel coronavirus COVID-19. PLoS ONE, 15.10.1371/journal.pone.0231236 ↗
41. Why do we sometimes get nonsense-correlations between Time-Series?–a study in sampling and the nature of time-series10.2307/2341482 ↗
42. A Study in Analysis of Stationary Time Series10.2307/2980009 ↗
43. General exponential smoothing and the equivalent ARMA process10.1002/for.3980030312 ↗
44. Kane, M.J., Price, N., Scotch, M., and Rabinowitz, P. (2014). Comparison of ARIMA and Random Forest time series models for prediction of avian influenza H5N1 outbreaks. BMC Bioinform., 15.10.1186/1471-2105-15-276 ↗
45. Time series forecasting using a hybrid ARIMA and neural network model10.1016/S0925-2312(01)00702-0 ↗
46. Time series forecasting using holt-winters exponential smoothing
47. Holt-Winters forecasting: Some practical issues
48. Robust forecasting with exponential and Holt–Winters smoothing10.1002/for.1125 ↗
49. The modeling and seasonal adjustment of weekly observations10.1080/07350015.1997.10524713 ↗
50. An analysis of transformations10.1111/j.2517-6161.1964.tb00553.x ↗
51. Hyndman, R., Koehler, A.B., Ord, J.K., and Snyder, R.D. (2008). Forecasting with Exponential Smoothing: The State Space Approach, Springer Science & Business Media.10.1007/978-3-540-71918-2 ↗
52. Estimation procedures for structural time series models10.1002/for.3980090203 ↗
53. Hutchinson, G.E. (1978). An Introduction to Population Ecology, Number 504: 51 HUT; John Wiley & Sons.
54. Harvey, A.C., and Shephard, N. (1993). Estimation and Testing of Stochastic Variance Models; Technical report. Suntory and Toyota International Centres for Economics and Related.
55. Hochreiter, S., and Schmidhuber, J. (1997). LSTM Can Solve Hard Long Time Lag Problems. Advances in Neural Information Processing Systems, MIT Press.
56. Graves, A. (2013). Generating sequences with recurrent neural networks. arXiv.10.1007/978-3-642-24797-2_3 ↗
57. Oord, A.v.d., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A., and Kavukcuoglu, K. (2016). Wavenet: A generative model for raw audio. arXiv.
58. Zaremba, W., Sutskever, I., and Vinyals, O. (2014). Recurrent neural network regularization. arXiv.
59. Deep learning10.1038/nature14539 ↗
60. STL: A seasonal-trend decomposition
61. (2020, May 04). Novel Corona Virus 2019 Dataset. Available online: https://www.kaggle.com/sudalairajkumar/novel-corona-virus-2019-dataset.
62. (2020, May 04). Population by Country Dataset—2020. Available online: https://www.kaggle.com/tanuprabhu/population-by-country-2020.
63. A comparison of alternative tests of significance for the problem of m rankings10.1214/aoms/1177731944 ↗
64. A simple sequentially rejective multiple test procedure