{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T13:01:51Z","timestamp":1763643711526,"version":"3.40.3"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031232350"},{"type":"electronic","value":"9783031232367"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-23236-7_32","type":"book-chapter","created":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T01:22:43Z","timestamp":1672536163000},"page":"457-469","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Forecasting Omicron Variant of Covid-19 with ANN Model in European Countries \u2013 Number of Cases, Deaths, and ICU Patients"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8623-7943","authenticated-orcid":false,"given":"Kathleen","family":"Carvalho","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4709-1718","authenticated-orcid":false,"given":"Luis Paulo","family":"Reis","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6679-5702","authenticated-orcid":false,"given":"Jo\u00e3o Paulo","family":"Teixeira","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,1,1]]},"reference":[{"doi-asserted-by":"publisher","unstructured":"Dairi, A., Harrou, F., Zeroual, A., Hittawe, M.M., Sun, Y.: Comparative study of machine learning methods for COVID-19 transmission forecasting. J. Biomed. Inform. 118, 103791 (2021). https:\/\/doi.org\/10.1016\/j.jbi.2021.103791. (WE - Science Citation Index Expanded (SCI-EXPANDED))","key":"32_CR1","DOI":"10.1016\/j.jbi.2021.103791"},{"doi-asserted-by":"publisher","unstructured":"Elsheikh, A.H., Saba, A.I., Panchal, H., Shanmugan, S., Alsaleh, N.A., Ahmadein, M.: Artificial intelligence for forecasting the prevalence of COVID-19 pandemic: an overview. In: HEALTHCARE, vol. 9, no. 12 (2021). https:\/\/doi.org\/10.3390\/healthcare9121614. (WE - Science Citation Index Expanded (SCI-EXPANDED). WE - Social Science Citation Index (SSCI))","key":"32_CR2","DOI":"10.3390\/healthcare9121614"},{"issue":"1","key":"32_CR3","doi-asserted-by":"publisher","first-page":"587","DOI":"10.1016\/j.aej.2020.09.037WE-ScienceCitationIndexExpanded(SCI-EXPANDED)","volume":"60","author":"J Farooq","year":"2021","unstructured":"Farooq, J., Bazaz, M.A.: A deep learning algorithm for modeling and forecasting of COVID-19 in five worst affected states of India. ALEXANDRIA Eng. J. 60(1), 587\u2013596 (2021). https:\/\/doi.org\/10.1016\/j.aej.2020.09.037WE-ScienceCitationIndexExpanded(SCI-EXPANDED)","journal-title":"ALEXANDRIA Eng. J."},{"issue":"3","key":"32_CR4","doi-asserted-by":"publisher","first-page":"2397","DOI":"10.32604\/cmc.2021.014042WE-ScienceCitationIndexExpanded(SCI-EXPANDED)","volume":"66","author":"R Zagrouba","year":"2021","unstructured":"Zagrouba, R., et al.: Modelling and simulation of COVID-19 outbreak prediction using supervised machine learning. C. Mater. Contin. 66(3), 2397\u20132407 (2021). https:\/\/doi.org\/10.32604\/cmc.2021.014042WE-ScienceCitationIndexExpanded(SCI-EXPANDED)","journal-title":"C. Mater. Contin."},{"unstructured":"Gallagher, J.: Covid vaccine update: those that work - and the others on the way. BBC (2021)","key":"32_CR5"},{"issue":"24","key":"32_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/ijerph17249404","volume":"17","author":"K Krstic","year":"2020","unstructured":"Krstic, K., Westerman, R., Chattu, V.K., Ekkert, N.V., Jakovljevic, M.: Corona- triggered global macroeconomic crisis of the early 2020s. Int. J. Environ. Res. Public Health 17(24), 1\u20139 (2020). https:\/\/doi.org\/10.3390\/ijerph17249404","journal-title":"Int. J. Environ. Res. Public Health"},{"doi-asserted-by":"publisher","unstructured":"Siami-Namini, S., Tavakoli, N., Siami Namin, A.: A comparison of ARIMA and LSTM in forecasting time series. In: Proceedings of 17th IEEE International Conference on Machine Learning Application ICMLA 2018, pp. 1394\u20131401 (2019). https:\/\/doi.org\/10.1109\/ICMLA.2018.00227","key":"32_CR7","DOI":"10.1109\/ICMLA.2018.00227"},{"doi-asserted-by":"publisher","unstructured":"Chung, C.F.: A generalized fractionally integrated autoregressive moving average process. J. Time Ser. Anal. 17, 111\u2013140 (1996). https:\/\/doi.org\/10.1111\/j.1467-9892.1996.tb00268.x","key":"32_CR8","DOI":"10.1111\/j.1467-9892.1996.tb00268.x"},{"issue":"2","key":"32_CR9","doi-asserted-by":"publisher","first-page":"585","DOI":"10.1016\/j.asoc.2006.03.002","volume":"7","author":"A Jain","year":"2007","unstructured":"Jain, A., Kumar, A.M.: Hybrid neural network models for hydrologic time series forecasting. Appl. Soft Comput. J. 7(2), 585\u2013592 (2007). https:\/\/doi.org\/10.1016\/j.asoc.2006.03.002","journal-title":"Appl. Soft Comput. J."},{"doi-asserted-by":"publisher","unstructured":"Jatob\u00e1, M., Santos, J., Gutierriz, I., Moscon, D., Fernandes. P.O., Teixeira, J.P.: Evolution of artificial intelligence research in human resources. Proc. Comput. Sci. Elsevier. 164, 137\u2013142 (2019). https:\/\/doi.org\/10.1016\/j.procs.2019.12.165","key":"32_CR10","DOI":"10.1016\/j.procs.2019.12.165"},{"doi-asserted-by":"publisher","unstructured":"Guedes, V., et al.: Transfer learning with audioset to voice pathologies identification in continuous speech. Proc. Comput. Sci. Elsevier. 164, 662\u2013669 (2019). https:\/\/doi.org\/10.1016\/j.procs.2019.12.233","key":"32_CR11","DOI":"10.1016\/j.procs.2019.12.233"},{"issue":"2021","key":"32_CR12","doi-asserted-by":"publisher","first-page":"940","DOI":"10.1016\/j.procs.2021.01.250","volume":"181","author":"PH Borghi","year":"2021","unstructured":"Borghi, P.H., Zakordonets, O., Teixeira, J.P.: A COVID-19 time series forecasting model based on MLP ANN. Proc. Comput. Sci. 181(2021), 940\u2013947 (2021). https:\/\/doi.org\/10.1016\/j.procs.2021.01.250","journal-title":"Proc. Comput. Sci."},{"doi-asserted-by":"publisher","unstructured":"Wieczorek, M., Si\u0142ka, J., W\u00f3zniak, M.: Neural network powered COVID-19 spread forecasting model. Chaos Solit. Fractals. 140, 110203 (2020). https:\/\/doi.org\/10.1016\/j.chaos.2020.110203","key":"32_CR13","DOI":"10.1016\/j.chaos.2020.110203"},{"key":"32_CR14","doi-asserted-by":"publisher","first-page":"53","DOI":"10.22034\/GJESM.2019.06.SI.06","volume":"6","author":"SK Tamang","year":"2020","unstructured":"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. 6, 53\u201364 (2020). https:\/\/doi.org\/10.22034\/GJESM.2019.06.SI.06","journal-title":"Glob. J. Environ. Sci. Manag."},{"key":"32_CR15","doi-asserted-by":"publisher","first-page":"973","DOI":"10.1016\/j.procs.2021.01.254","volume":"181","author":"LS Oliveira","year":"2021","unstructured":"Oliveira, L.S., Gruetzmacher, S.B., Teixeira, J.P.: COVID-19 time series prediction. Proc. Comput. Sci. 181, 973\u2013980 (2021). https:\/\/doi.org\/10.1016\/j.procs.2021.01.254","journal-title":"Proc. Comput. Sci."},{"doi-asserted-by":"publisher","unstructured":"Carvalho, K., Vicente, J.P., Jakovljevic, M., Teixeira, J.P.R.: Analysis and forecasting incidence, intensive care unit admissions, and projected mortality attributable to COVID-19 in Portugal, the UK, Germany, Italy, and France. Predictions for 4 weeks ahead. Bioengineering. 8(6), 84 (2021). https:\/\/doi.org\/10.3390\/bioengineering8060084","key":"32_CR16","DOI":"10.3390\/bioengineering8060084"},{"unstructured":"Worldometer, \u201cCOVID-19 Coronavirus Pandemic. https:\/\/www.worldometers.info\/coronavirus\/","key":"32_CR17"},{"unstructured":"Coronavirus Pandemic (COVID-19). Our World in Data. https:\/\/ourworldindata.org\/","key":"32_CR18"},{"unstructured":"Covid-19 - Dire\u00e7\u00e3o-Geral da Sa\u00fade. https:\/\/covid19.min-saude.pt\/","key":"32_CR19"},{"unstructured":"Data.gouv.fr. https:\/\/www.data.gouv.fr\/fr\/","key":"32_CR20"},{"unstructured":"Italian Department of Civil Protection. https:\/\/github.com\/pcm-dpc\/COVID-19","key":"32_CR21"},{"unstructured":"Department of Health and Social Care: 30 million people in UK receive first dose of coronavirus (COVID-19) vaccine. Dep. Heal. Soc. Care (2021). https:\/\/www.gov.uk\/government\/news\/30-million-people-in-uk-receive-first-dose-of-coronavirus-covid-19-vaccine","key":"32_CR22"},{"unstructured":"Achternbosch, Y., et al.: Alle Corona-F\u00e4lle in den Landkreisen, Bundesl\u00e4ndern und weltweit (2021)","key":"32_CR23"},{"unstructured":"Germany Federal Ministry of Health: Current information for travellers (2022). https:\/\/www.zusammengegencorona.de\/en\/current-information-for-travellers\/","key":"32_CR24"},{"unstructured":"Portuguesa, R.: M\u00e1scaras passam a ser obrigat\u00f3rias e, apenas tr\u00eas casos (2022). https:\/\/www.portugal.gov.pt\/pt\/gc23\/comunicacao\/noticia?i=mascaras-passam-a-ser-%0Aobrigatorias-em-apenas-tres-casos%0A","key":"32_CR25"},{"unstructured":"Fran\u00e7aise, R.: End of vaccination pass and indoor mask (2022). https:\/\/www.service-public.fr\/particuliers\/actualites\/A15543?lang=en","key":"32_CR26"},{"issue":"2","key":"32_CR27","doi-asserted-by":"publisher","first-page":"431","DOI":"10.1137\/0111030","volume":"11","author":"DW Marquardt","year":"1963","unstructured":"Marquardt, D.W.: An algorithm for least-squares estimation of nonlinear parameters. J. Soc. Ind. Appl. Math. 11(2), 431\u2013441 (1963). https:\/\/doi.org\/10.1137\/0111030","journal-title":"J. Soc. Ind. Appl. Math."}],"container-title":["Communications in Computer and Information Science","Optimization, Learning Algorithms and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-23236-7_32","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T02:31:22Z","timestamp":1672540282000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-23236-7_32"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031232350","9783031232367"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-23236-7_32","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"1 January 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"OL2A","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Optimization, Learning Algorithms and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bragan\u00e7a","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ol2a2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/ol2a.ipb.pt\/EN_index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"145","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"53","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"37% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}