{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T13:23:44Z","timestamp":1772025824483,"version":"3.50.1"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2020,11,19]],"date-time":"2020-11-19T00:00:00Z","timestamp":1605744000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,11,19]],"date-time":"2020-11-19T00:00:00Z","timestamp":1605744000000},"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":["Soft Comput"],"published-print":{"date-parts":[[2023,3]]},"DOI":"10.1007\/s00500-020-05452-z","type":"journal-article","created":{"date-parts":[[2020,11,19]],"date-time":"2020-11-19T11:02:52Z","timestamp":1605783772000},"page":"2685-2694","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["RETRACTED ARTICLE: A new prediction approach of the COVID-19 virus pandemic behavior with a hybrid ensemble modular nonlinear autoregressive neural network"],"prefix":"10.1007","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5798-1426","authenticated-orcid":false,"given":"Patricia","family":"Melin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Julio Cesar","family":"Monica","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniela","family":"Sanchez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Oscar","family":"Castillo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,11,19]]},"reference":[{"key":"5452_CR1","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1016\/j.egypro.2017.09.609","volume":"134","author":"A Ahmed","year":"2017","unstructured":"Ahmed A, Khalid M (2017) Multi-step ahead wind forecasting using nonlinear autoregressive neural networks. Energy Proc 134:192\u2013204","journal-title":"Energy Proc"},{"key":"5452_CR2","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1016\/j.enconman.2013.07.003","volume":"75","author":"K Benmouiza","year":"2013","unstructured":"Benmouiza K, Cheknane A (2013) Forecasting hourly global solar radiation using hybrid k-means and nonlinear autoregressive neural network models. Energy Convers Manag 75:561\u2013569","journal-title":"Energy Convers Manag"},{"key":"5452_CR3","doi-asserted-by":"publisher","first-page":"129","DOI":"10.12688\/f1000research.22457.2","volume":"9","author":"YW Chen","year":"2020","unstructured":"Chen YW, Yiu C-B, Wong K (2020) Prediction of the SARS-CoV-2 (2019-nCoV) 3C-like protease (3CL pro) structure: virtual screening reveals velpatasvir, ledipasvir, and other drug repurposing candidates. F1000Research 9:129","journal-title":"F1000Research"},{"key":"5452_CR4","doi-asserted-by":"publisher","first-page":"1679","DOI":"10.3390\/ijerph17051679","volume":"17","author":"C Fan","year":"2020","unstructured":"Fan C, Liu L, Guo W, Yang A, Ye C, Jilili M, Ren M, Xu P, Long H, Wang Y (2020) Prediction of epidemic spread of the 2019 novel coronavirus driven by spring festival transportation in China: a population-based study. Int J Environ Res Publ Health 17:1679. https:\/\/doi.org\/10.3390\/ijerph17051679","journal-title":"Int J Environ Res Publ Health"},{"issue":"2","key":"5452_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/biom10020331","volume":"10","author":"GK- Goh","year":"2020","unstructured":"Goh GK-, Keith Dunker A, Foster JA, Uversky VN (2020) Rigidity of the outer shell predicted by a protein intrinsic disorder model sheds light on the COVID-19 (Wuhan-2019-nCoV) infectivity. Biomolecules 10(2):1\u20133","journal-title":"Biomolecules"},{"issue":"4","key":"5452_CR6","doi-asserted-by":"publisher","first-page":"671","DOI":"10.1016\/j.chom.2020.03.002","volume":"27","author":"A Grifoni","year":"2020","unstructured":"Grifoni A, Sidney J, Zhang Y, Scheuermann RH, Peters B, Sette A (2020) A sequence homology and bioinformatic approach can predict candidate targets for immune responses to SARS-CoV-2. Cell Host Microbe 27(4):671\u2013680.e2","journal-title":"Cell Host Microbe"},{"issue":"1","key":"5452_CR7","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1186\/s12916-020-01551-8","volume":"18","author":"Z He","year":"2020","unstructured":"He Z (2020) What further should be done to control COVID-19 outbreaks in addition to cases isolation and contact tracing measures? BMC Med 18(1):80. https:\/\/doi.org\/10.1186\/s12916-020-01551-8","journal-title":"BMC Med"},{"issue":"3","key":"5452_CR8","doi-asserted-by":"publisher","first-page":"246","DOI":"10.3855\/jidc.12585","volume":"14","author":"R Huang","year":"2020","unstructured":"Huang R, Liu M, Ding Y (2020) Spatial-temporal distribution of COVID-19 in China and its prediction: a data-driven modeling analysis. J Infect Develop Countries 14(3):246\u2013253","journal-title":"J Infect Develop Countries"},{"issue":"5","key":"5452_CR9","doi-asserted-by":"publisher","first-page":"554","DOI":"10.1016\/j.jinf.2020.02.026","volume":"80","author":"IM Ibrahim","year":"2020","unstructured":"Ibrahim IM, Abdelmalek DH, Elshahat ME, Elfiky AA (2020) COVID-19 spike-host cell receptor GRP78 binding site prediction. J Infect 80(5):554\u2013562. https:\/\/doi.org\/10.1016\/j.jinf.2020.02.026","journal-title":"J Infect"},{"issue":"C","key":"5452_CR10","doi-asserted-by":"publisher","first-page":"101922","DOI":"10.1016\/j.tre.2020.101922","volume":"136","author":"D Ivanov","year":"2020","unstructured":"Ivanov D (2020) Predicting the impacts of epidemic outbreaks on global supply chains: a simulation-based analysis on the coronavirus outbreak (COVID-19\/SARS-CoV-2) case. Transp Res Part E Logist Transp Rev 136(C):101922. https:\/\/doi.org\/10.1016\/j.tre.2020.101922","journal-title":"Transp Res Part E Logist Transp Rev"},{"key":"5452_CR11","doi-asserted-by":"crossref","unstructured":"Le TT, Pham BT, Ly HB, Shirzadi A, Le LM (2020) Development of 48-hour precipitation forecasting mdel using nonlinear autoregressive neural network. In: CIGOS 2019, Innovation for sustainable infrastructure, pp 1191\u20131196. Springer, Singapore","DOI":"10.1007\/978-981-15-0802-8_191"},{"issue":"7","key":"5452_CR12","doi-asserted-by":"publisher","first-page":"614","DOI":"10.1002\/acs.2267","volume":"26","author":"BS Leon","year":"2012","unstructured":"Leon BS, Alanis AY, Sanchez EN, Ruiz-Velazquez E, Ornelas-Tellez F (2012) Inverse optimal neural control for a class of discrete-time nonlinear positive systems. Int J Adapt Control Signal Process 26(7):614\u2013629","journal-title":"Int J Adapt Control Signal Process"},{"key":"5452_CR13","first-page":"282","volume":"5","author":"L Li","year":"2020","unstructured":"Li L, Yang Z, Dang Z, Meng C, Huang J, Meng H, Wang D, Chen G, Zhang J, Peng H, Shao Y (2020a) Propagation analysis and prediction of the COVID-19. Infect Dis Model 5:282\u2013292","journal-title":"Infect Dis Model"},{"issue":"4","key":"5452_CR14","first-page":"469","volume":"80","author":"Q Li","year":"2020","unstructured":"Li Q, Feng W, Quan Y (2020b) Trend and forecasting of the COVID-19 outbreak in China. J Infect 80(4):469\u2013496","journal-title":"J Infect"},{"key":"5452_CR15","doi-asserted-by":"publisher","first-page":"50","DOI":"10.3390\/biology9030050","volume":"9","author":"Z Liu","year":"2020","unstructured":"Liu Z, Magal P, Seydi O, Webb G (2020) Understanding unreported cases in the COVID-19 epidemicoutbreak in Wuhan, China, and the importance of major public health interventions. Biology 9:50. https:\/\/doi.org\/10.3390\/biology9030050","journal-title":"Biology"},{"key":"5452_CR16","doi-asserted-by":"publisher","first-page":"594","DOI":"10.1016\/j.ins.2017.09.031","volume":"460\u2013461","author":"P Melin","year":"2018","unstructured":"Melin P, S\u00e1nchez D (2018) Multi-objective optimization for modular granular neural networks applied to pattern recognition. Inf Sci 460\u2013461:594\u2013610","journal-title":"Inf Sci"},{"issue":"4","key":"5452_CR17","doi-asserted-by":"publisher","first-page":"1217","DOI":"10.1016\/j.asoc.2006.01.009","volume":"7","author":"PA Melin","year":"2007","unstructured":"Melin PA, Mancilla A, Lopez M, Mendoza O (2007) A hybrid modular neural network architecture with fuzzy Sugeno integration for time series forecasting. Appl Soft Comput 7(4):1217\u20131226","journal-title":"Appl Soft Comput"},{"issue":"3","key":"5452_CR18","doi-asserted-by":"publisher","first-page":"3494","DOI":"10.1016\/j.eswa.2011.09.040","volume":"39","author":"P Melin","year":"2012","unstructured":"Melin P, Soto J, Castillo O, Soria J (2012a) A new approach for time series prediction using ensembles of ANFIS models. Expert Syst Appl 39(3):3494\u20133506","journal-title":"Expert Syst Appl"},{"key":"5452_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ins.2012.02.027","volume":"197","author":"P Melin","year":"2012","unstructured":"Melin P, S\u00e1nchez D, Castillo O (2012b) Genetic optimization of modular neural networks with fuzzy response integration for human recognition. Inf Sci 197:1\u201319","journal-title":"Inf Sci"},{"issue":"109917","key":"5452_CR20","first-page":"1","volume":"138","author":"P Melin","year":"2020","unstructured":"Melin P, Monica JC, Sanchez D, Castillo O (2020a) Analysis of spatial spread relationships of coronavirus (COVID-19) pandemic in the world using self organizing maps. Chaos, Solitons Fractals 138(109917):1\u20137","journal-title":"Chaos, Solitons Fractals"},{"key":"5452_CR21","doi-asserted-by":"publisher","first-page":"181","DOI":"10.3390\/healthcare8020181","volume":"8","author":"P Melin","year":"2020","unstructured":"Melin P, Monica JC, Sanchez D, Castillo O (2020b) Multiple ensemble neural network models with fuzzy response aggregation for predicting COVID-19 time series: the case of Mexico. Healthcare 8:181","journal-title":"Healthcare"},{"key":"5452_CR22","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4471-0453-7","volume-title":"Neural networks for modelling and control of dynamic systems: a practitioner\u2019s handbook. Advanced textbooks in control and signal processing","author":"M Norgaard","year":"2000","unstructured":"Norgaard M, Ravn O, Poulsen NK, Hansen LK (2000) Neural networks for modelling and control of dynamic systems: a practitioner\u2019s handbook. Advanced textbooks in control and signal processing. Springer, Berlin"},{"key":"5452_CR23","first-page":"271","volume":"5","author":"WC Roda","year":"2020","unstructured":"Roda WC, Varughese MB, Han D, Li MY (2020) Why is it difficult to accurately predict the COVID-19 epidemic? Infect Dis Model 5:271\u2013281","journal-title":"Infect Dis Model"},{"key":"5452_CR24","first-page":"256","volume":"5","author":"K Roosa","year":"2020","unstructured":"Roosa K, Lee Y, Luo R, Kirpich A, Rothenberg R, Hyman JM, Yan P, Chowell G (2020) Real-time forecasts of the COVID-19 epidemic in China from February 5th to February 24th, 2020. Infect Dis Model 5:256\u2013263","journal-title":"Infect Dis Model"},{"key":"5452_CR25","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.energy.2018.01.007","volume":"148","author":"A Safari","year":"2018","unstructured":"Safari A, Davallou M (2018) Oil price forecasting using a hybrid model. Energy 148:49\u201358","journal-title":"Energy"},{"key":"5452_CR26","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1016\/j.engappai.2017.06.007","volume":"64","author":"D S\u00e1nchez","year":"2017","unstructured":"S\u00e1nchez D, Melin P, Castillo O (2017) Optimization of modular granular neural networks using a firefly algorithm for human recognition. Eng Appl AI 64:172\u2013186","journal-title":"Eng Appl AI"},{"issue":"3","key":"5452_CR27","doi-asserted-by":"publisher","first-page":"3229","DOI":"10.3233\/JIFS-191198","volume":"38","author":"D S\u00e1nchez","year":"2020","unstructured":"S\u00e1nchez D, Melin P, Castillo O (2020) Comparison of particle swarm optimization variants with fuzzy dynamic parameter adaptation for modular granular neural networks for human recognition. J Intell Fuzzy Syst 38(3):3229\u20133252","journal-title":"J Intell Fuzzy Syst"},{"key":"5452_CR28","doi-asserted-by":"crossref","unstructured":"Sarkar R, Julai S, Hossain S, Chong WT, Rahman M (2019) A comparative study of activation functions of NAR and NARX neural network for long-term wind speed forecasting in Malaysia. Math Probl Eng","DOI":"10.1155\/2019\/6403081"},{"issue":"3","key":"5452_CR29","first-page":"211","volume":"11","author":"J Soto","year":"2014","unstructured":"Soto J, Melin P, Castillo O (2014) Time series prediction using ensembles of ANFIS models with genetic optimization of interval type-2 and type-1 fuzzy integrators. Int J Hybrid Intell Syst 11(3):211\u2013226","journal-title":"Int J Hybrid Intell Syst"},{"issue":"5","key":"5452_CR30","doi-asserted-by":"publisher","first-page":"1629","DOI":"10.1007\/s40815-019-00642-w","volume":"21","author":"J Soto","year":"2019","unstructured":"Soto J, Castillo O, Melin P, Pedrycz W (2019) A new approach to multiple time series prediction using MIMO fuzzy aggregation models with modular neural networks. Int J Fuzzy Syst 21(5):1629\u20131648","journal-title":"Int J Fuzzy Syst"},{"key":"5452_CR31","unstructured":"The Humanitarian Data Exchange (HDX). https:\/\/data.humdata.org\/dataset\/novel-coronavirus-2019-ncov-cases. Accessed 01 11 2020"},{"issue":"8","key":"5452_CR32","doi-asserted-by":"publisher","first-page":"e2000028","DOI":"10.1002\/minf.202000028","volume":"39","author":"A Ton","year":"2020","unstructured":"Ton A, Gentile F, Hsing M, Ban F, Cherkasov A (2020) Rapid identification of potential inhibitors of SARS-CoV-2 main protease by deep docking of 1.3 billion compounds. Mol. Inform 39(8):e2000028. https:\/\/doi.org\/10.1002\/minf.202000028","journal-title":"Inform"},{"issue":"1","key":"5452_CR33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41421-019-0132-8","volume":"6","author":"H Wang","year":"2020","unstructured":"Wang H, Wang Z, Dong Y, Chang R, Xu C, Yu X, Zhang S, Tsamlag L, Shang M, Huang J, Wang Y, Xu G, Shen T, Zhang X, Cai Y (2020) Phase-adjusted estimation of the number of Coronavirus Disease 2019 cases in Wuhan. China. Cell Discov 6(1):1\u201310","journal-title":"China. Cell Discov"},{"key":"5452_CR34","doi-asserted-by":"crossref","unstructured":"Yadav V, Nath S, Malik H (2019) Forecasting of nitrogen dioxide at one day ahead using nonlinear autoregressive neural network for environmental applications. In: Applications of artificial intelligence techniques in engineering, pp 615\u2013623. Springer, Singapore","DOI":"10.1007\/978-981-13-1819-1_58"},{"key":"5452_CR35","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1016\/j.ijid.2020.02.033","volume":"93","author":"S Zhang","year":"2020","unstructured":"Zhang S, Diao M, Yu W, Pei L, Lin Z, Chen D (2020) Estimation of the reproductive number of novel coronavirus (COVID-19) and the probable outbreak size on the Diamond Princess cruise ship: a data-driven analysis. Int J Infect Dis 93:201\u2013204","journal-title":"Int J Infect Dis"},{"issue":"1","key":"5452_CR36","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1111\/jebm.12376","volume":"13","author":"T Zhou","year":"2020","unstructured":"Zhou T, Liu Q, Yang Z, Liao J, Yang K, Bai W, Lu X, Zhang W (2020) Preliminary prediction of the basic reproduction number of the Wuhan novel coronavirus 2019-nCoV. J Evid-Based Med 13(1):3\u20137","journal-title":"J Evid-Based Med"}],"updated-by":[{"DOI":"10.1007\/s00500-023-08964-6","type":"retraction","label":"Retraction","source":"retraction-watch","updated":{"date-parts":[[2023,7,10]],"date-time":"2023-07-10T00:00:00Z","timestamp":1688947200000},"record-id":"45977"},{"DOI":"10.1007\/s00500-023-08964-6","type":"retraction","label":"Retraction","source":"publisher","updated":{"date-parts":[[2023,7,10]],"date-time":"2023-07-10T00:00:00Z","timestamp":1688947200000}}],"container-title":["Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-020-05452-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00500-020-05452-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-020-05452-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,10]],"date-time":"2023-07-10T13:35:31Z","timestamp":1688996131000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00500-020-05452-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,19]]},"references-count":36,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2023,3]]}},"alternative-id":["5452"],"URL":"https:\/\/doi.org\/10.1007\/s00500-020-05452-z","relation":{},"ISSN":["1432-7643","1433-7479"],"issn-type":[{"value":"1432-7643","type":"print"},{"value":"1433-7479","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,19]]},"assertion":[{"value":"19 November 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 July 2023","order":2,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Correction","order":3,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"This article has been retracted. Please see the Retraction Notice for more detail:","order":4,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"https:\/\/doi.org\/10.1007\/s00500-023-08964-6","URL":"https:\/\/doi.org\/10.1007\/s00500-023-08964-6","order":5,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"All the authors in the paper have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}