Skip to main content
Elsevier - PMC COVID-19 Collection logoLink to Elsevier - PMC COVID-19 Collection
. 2022 Jan 10;196:1021–1027. doi: 10.1016/j.procs.2021.12.105

COVID-19 Time Series Forecasting – Twenty Days Ahead

Kathleen CM de Carvalho a,b, João Paulo Vicente a,b, João Paulo Teixeira a
PMCID: PMC8745940  PMID: 35035627

Abstract

The new Coronavirus, responsible for the COVID-19 disease, is the most discussed topic in the current days, and the forecast numbers of new cases and deaths are the most important source of data in governmental decision-making. The present work presents a prediction model with two different approaches concerning the input data, by using Artificial Neural Networks (ANN). The use of a substantial mitigation procedure adopted (mandatory use of masks) was experimented as an input to the network, in order to evaluate the improvement in the results. The ANN forecasting model was demonstrated to predict with higher accuracy within the next twenty days using the information about the mandatory use of face masks. The final results showed that the twenty days ahead forecasting was made with an error of 24,7% and 1,6% for the number of cumulative cases of infection and deaths for Brazil, and 37,9% and 33,8% for Portuguese time series, respectively.

Keywords: Brazil COVID-19 forecasting, Portugal COVID-19 forecasting, COVID-19 time series, Mitigation procedures, Use of face masks

References

  • 1.World Health Organization, Advice on the use of masks in the context of COVID-19, World Heal. Organ. (2020) 1–16. 10.1093/jiaa077. [DOI]
  • 2.C. Felisoni De Angelo, R. Zwicker, N.M.M. Dias Fouto, M.R. Luppe, Séries temporais e redes neurais: uma análise comparativa de técnicas na previsão de vendas do varejo brasileiro. (Portuguese), Brazilian Bus. Rev. (Portuguese Ed. 8 (2011) 1–21.
  • 3.J.P. Teixeira, P.O. Fernandes, Forecasting of a Non-Seasonal Tourism Time Series with ANN, Proc. 14th Int. Work. Time Ser. – ITISE. (2014) 724–734. http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=ORCID&SrcApp=OrcidOrg&DestLinkType=FullRecord&DestApp=WOS_CPL&KeyUT=WOS:000359136600081&KeyUID=WOS:000359136600081.
  • 4.A. Azadeh, S.F. Ghaderi, S. Sohrabkhani, Forecasting electrical consumption by integration of Neural Network, time series and ANOVA, Appl. Math. Comput. 186 (2007) 1753–1761. 10.1016/j.amc.2006.08.094. [DOI]
  • 5.Qi M., Zhang G.P. An investigation of model selection criteria for neural network time series forecasting. Eur. J. Oper. Res. 2001;132:666–680. doi: 10.1016/S0377-2217(00)00171-5. [DOI] [Google Scholar]
  • 6.Zhang G.P., Qi M. Neural network forecasting for seasonal and trend time series. Eur. J. Oper. Res. 2005;160:501–514. doi: 10.1016/j.ejor.2003.08.037. [DOI] [Google Scholar]
  • 7.J.P. Teixeira, D. Freitas, Segmental durations predicted with a neural network, EUROSPEECH 2003 - 8th Eur. Conf. Speech Commun. Technol. (2003) 169–172.
  • 8.M. Wieczorek, J. Siłka, M. Wozniak, Neural network powered COVID-19 spread forecasting model, (2020). https://doi.org/ 10.1016/j.chaos.2020.110203. [DOI] [PMC free article] [PubMed]
  • 9.Tamang S.K., Singh P.D., Datta B. Forecasting of Covid-19 cases based on prediction using artificial neural network curve fitting technique. Glob. J. Environ. Sci. Manag. 2020;6:53–64. doi: 10.22034/GJESM.2019.06.SI.06. [DOI] [Google Scholar]
  • 10.Borghi P.H., Zakordonets O., Teixeira J.P. A COVID-19 time series forecasting model based on MLP ANN. Procedia Comput. Sci. 2021;181:940–947. doi: 10.1016/j.procs.2021.01.250. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.de Oliveira L.S., Gruetzmacher S.B., Teixeira J.P. COVID-19 Time Series Prediction. Procedia Comput. Sci. 2021;181:973–980. doi: 10.1016/j.procs.2021.01.254. [DOI] [Google Scholar]
  • 12.K. Carvalho, J.P. Vicente, M. Jakovljevic, J.P. Teixeira, Forecasted Incidence, Intensive Care Unit Admissions and Projected Mortality attributable to Covid-19 in Portugal, UK, Germany, Italy and France – 4 weeks Ahead, (2021). 10.20944/PREPRINTS202105.0116.V1. [DOI] [PMC free article] [PubMed]
  • 13.Michel Bielecki, R. Züst, D. Siegrist, D. Meyerhofer, G.A.G. Crameri1, Z.G. Stanga, A. Stettbacher, T.W. Buehrer, J.W. Deuel, Social distancing alters the clinical course of COVID-19 in young adults: A comparative cohort study, Oxford Univ. Press Infect. Dis. Soc. Am. (2020). 10.1093/gerona/gly169/5057054. [DOI] [PMC free article] [PubMed]
  • 14.Worldometer, COVID-19 Coronavirus Pandemic, (n.d.). https://www.worldometers.info/coronavirus/,.
  • 15.Masks4all, What Countries Require Public Mask Usage To Help Contain COVID-19?, (n.d.). https://masks4all.co/pt/what-countries-require-masks-in-public/.
  • 16.da Silva I.N., Spatti D.H., Flauzino R.A., Liboni L.H.B., dos R. Alves S.F. Artificial Neural Networks: Apractical course. Springer International Publishing; 2017. [Google Scholar]
  • 17.T.S. Serafim, Presença da variante do Reino Unido é estimada em cerca de 60% em Lisboa, Público. (2021).
  • 18.Challen R., Brooks-Pollock E., Read J.M., Dyson L., Tsaneva-Atanasova K., Danon L. Risk of mortality in patients infected with SARS-CoV-2 variant of concern 202012/1: Matched cohort study. BMJ. 2021;372:1–10. doi: 10.1136/bmj.n579. [DOI] [PMC free article] [PubMed] [Google Scholar]

Articles from Procedia Computer Science are provided here courtesy of Elsevier

RESOURCES