{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T13:25:56Z","timestamp":1762953956607,"version":"3.28.0"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"13","license":[{"start":{"date-parts":[[2024,2,20]],"date-time":"2024-02-20T00:00:00Z","timestamp":1708387200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,2,20]],"date-time":"2024-02-20T00:00:00Z","timestamp":1708387200000},"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":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2024,5]]},"DOI":"10.1007\/s00521-024-09449-9","type":"journal-article","created":{"date-parts":[[2024,2,20]],"date-time":"2024-02-20T10:02:38Z","timestamp":1708423358000},"page":"7119-7131","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["BO\u2013SHAP\u2013BLS: a novel machine learning framework for accurate forecasting of COVID-19 testing capabilities"],"prefix":"10.1007","volume":"36","author":[{"given":"Choujun","family":"Zhan","sequence":"first","affiliation":[]},{"given":"Lingfeng","family":"Miao","sequence":"additional","affiliation":[]},{"given":"Junyan","family":"Lin","sequence":"additional","affiliation":[]},{"given":"Minghao","family":"Tan","sequence":"additional","affiliation":[]},{"given":"Kim Fung","family":"Tsang","sequence":"additional","affiliation":[]},{"given":"Tianyong","family":"Hao","sequence":"additional","affiliation":[]},{"given":"Hu","family":"Min","sequence":"additional","affiliation":[]},{"given":"Xuejiao","family":"Zhao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,2,20]]},"reference":[{"issue":"9393","key":"9449_CR1","doi-asserted-by":"publisher","first-page":"1353","DOI":"10.1016\/S0140-6736(03)14630-2","volume":"362","author":"N Zhong","year":"2003","unstructured":"Zhong N, Zheng B, Li Y, Poon L, Xie Z, Chan K, Li P, Tan S, Chang Q, Xie J et al (2003) Epidemiology and cause of severe acute respiratory syndrome (SARS) in Guangdong, People\u2019s Republic of China, in February, 2003. Lancet 362(9393):1353\u20131358","journal-title":"Lancet"},{"key":"9449_CR2","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1016\/j.ijid.2020.01.009","volume":"91","author":"DS Hui","year":"2020","unstructured":"Hui DS, Azhar EI, Madani TA, Ntoumi F, Kock R, Dar O, Ippolito G, Mchugh TD, Memish ZA, Drosten C et al (2020) The continuing 2019-ncov epidemic threat of novel coronaviruses to global health-the latest 2019 novel coronavirus outbreak in Wuhan, China. Int J Infect Dis 91:264\u2013266","journal-title":"Int J Infect Dis"},{"key":"9449_CR3","doi-asserted-by":"crossref","unstructured":"Gallo\u00a0Marin B, Aghagoli G, Lavine K, Yang L, Siff EJ, Chiang SS, Salazar-Mather TP, Dumenco L, Savaria MC, Aung SN, et\u00a0al (2021) Predictors of covid-19 severity: a literature review. Rev Med Virol 31(1):1\u201310","DOI":"10.1002\/rmv.2146"},{"issue":"9","key":"9449_CR4","doi-asserted-by":"publisher","first-page":"666","DOI":"10.1038\/s41560-020-0662-1","volume":"5","author":"S Ou","year":"2020","unstructured":"Ou S, He X, Ji W, Chen W, Sui L, Gan Y, Lu Z, Lin Z, Deng S, Przesmitzki S et al (2020) Machine learning model to project the impact of covid-19 on us motor gasoline demand. Nat Energy 5(9):666\u2013673","journal-title":"Nat Energy"},{"key":"9449_CR5","doi-asserted-by":"crossref","unstructured":"DeFilippis E, Impink SM, Singell M, Polzer JT, Sadun R (2020) Collaborating during coronavirus: the impact of covid-19 on the nature of work. Tech. rep, National Bureau of Economic Research","DOI":"10.3386\/w27612"},{"issue":"11","key":"9449_CR6","doi-asserted-by":"publisher","first-page":"1247","DOI":"10.1016\/S1473-3099(20)30553-3","volume":"20","author":"HS Badr","year":"2020","unstructured":"Badr HS, Du H, Marshall M, Dong E, Squire MM, Gardner LM (2020) Association between mobility patterns and covid-19 transmission in the USA: a mathematical modelling study. Lancet Infect Dis 20(11):1247\u20131254","journal-title":"Lancet Infect Dis"},{"issue":"6","key":"9449_CR7","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1038\/s41562-020-0896-8","volume":"4","author":"D Guan","year":"2020","unstructured":"Guan D, Wang D, Hallegatte S, Davis SJ, Huo J, Li S, Bai Y, Lei T, Xue Q, Coffman D et al (2020) Global supply-chain effects of covid-19 control measures. Nat Hum Behav 4(6):577\u2013587","journal-title":"Nat Hum Behav"},{"key":"9449_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2020.139133","volume":"731","author":"Y Wang","year":"2020","unstructured":"Wang Y, Yuan Y, Wang Q, Liu C, Zhi Q, Cao J (2020) Changes in air quality related to the control of coronavirus in China: implications for traffic and industrial emissions. Sci Total Environ 731:139133","journal-title":"Sci Total Environ"},{"issue":"5","key":"9449_CR9","doi-asserted-by":"publisher","first-page":"638","DOI":"10.1016\/j.jiph.2021.01.019","volume":"14","author":"N Zaki","year":"2021","unstructured":"Zaki N, Mohamed EA (2021) The estimations of the covid-19 incubation period: a scoping reviews of the literature. J Infect Public Health 14(5):638\u2013646","journal-title":"J Infect Public Health"},{"key":"9449_CR10","doi-asserted-by":"crossref","unstructured":"He X, Luo L, Tang X, Wang Q (2023) Healthcare, vol.\u00a011 (MDPI, 2023), p 393","DOI":"10.3390\/healthcare11030393"},{"key":"9449_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2020.110121","volume":"140","author":"A Zeroual","year":"2020","unstructured":"Zeroual A, Harrou F, Dairi A, Sun Y (2020) Deep learning methods for forecasting covid-19 time-series data: a comparative study. Chaos Solitons Fractals 140:110121","journal-title":"Chaos Solitons Fractals"},{"key":"9449_CR12","doi-asserted-by":"crossref","unstructured":"Abbasimehr H, Paki R, Bahrini A (2022) A novel approach based on combining deep learning models with statistical methods for covid-19 time series forecasting. Neural Comput Appl, pp 1\u201315","DOI":"10.1007\/s00521-021-06548-9"},{"key":"9449_CR13","doi-asserted-by":"crossref","unstructured":"Malki Z, Atlam ES, Ewis A, Dagnew G, Alzighaibi AR, ELmarhomy G, Elhosseini MA, Hassanien AE, Gad I (2021) Arima models for predicting the end of covid-19 pandemic and the risk of second rebound. Neural Comput Appl 33:2929\u20132948","DOI":"10.1007\/s00521-020-05434-0"},{"key":"9449_CR14","doi-asserted-by":"crossref","unstructured":"Kumar N, Susan S (2020) 2020 11th international conference on computing, communication and networking technologies (ICCCNT) (IEEE, 2020), pp 1\u20137","DOI":"10.1109\/ICCCNT49239.2020.9225391"},{"issue":"4","key":"9449_CR15","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1109\/TCE.2021.3130228","volume":"67","author":"S Hassantabar","year":"2021","unstructured":"Hassantabar S, Stefano N, Ghanakota V, Ferrari A, Nicola GN, Bruno R, Marino IR, Hamidouche K, Jha NK (2021) Coviddeep: SARS-cov-2\/covid-19 test based on wearable medical sensors and efficient neural networks. IEEE Trans Consum Electron 67(4):244\u2013256","journal-title":"IEEE Trans Consum Electron"},{"issue":"6","key":"9449_CR16","doi-asserted-by":"publisher","first-page":"855","DOI":"10.1038\/s41591-020-0883-7","volume":"26","author":"G Giordano","year":"2020","unstructured":"Giordano G, Blanchini F, Bruno R, Colaneri P, Di Filippo A, Di Matteo A, Colaneri M (2020) Modelling the covid-19 epidemic and implementation of population-wide interventions in Italy. Nat Med 26(6):855\u2013860","journal-title":"Nat Med"},{"key":"9449_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2020.110511","volume":"142","author":"H Abbasimehr","year":"2021","unstructured":"Abbasimehr H, Paki R (2021) Prediction of covid-19 confirmed cases combining deep learning methods and Bayesian optimization. Chaos Solitons Fractals 142:110511","journal-title":"Chaos Solitons Fractals"},{"issue":"3","key":"9449_CR18","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0230405","volume":"15","author":"C Anastassopoulou","year":"2020","unstructured":"Anastassopoulou C, Russo L, Tsakris A, Siettos C (2020) Data-based analysis, modelling and forecasting of the covid-19 outbreak. PLoS ONE 15(3):e0230405","journal-title":"PLoS ONE"},{"issue":"22","key":"9449_CR19","doi-asserted-by":"publisher","first-page":"1884","DOI":"10.1016\/j.scib.2020.08.002","volume":"65","author":"J Huang","year":"2020","unstructured":"Huang J, Zhang L, Liu X, Wei Y, Liu C, Lian X, Huang Z, Chou J, Liu X, Li X et al (2020) Global prediction system for covid-19 pandemic. Sci Bull 65(22):1884","journal-title":"Sci Bull"},{"issue":"5","key":"9449_CR20","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1016\/S1473-3099(20)30120-1","volume":"20","author":"E Dong","year":"2020","unstructured":"Dong E, Du H, Gardner L (2020) An interactive web-based dashboard to track covid-19 in real time. Lancet Infect Dis 20(5):533\u2013534","journal-title":"Lancet Infect Dis"},{"key":"9449_CR21","doi-asserted-by":"crossref","unstructured":"He F, Zhou J, Feng ZK, Liu G, Yang Y (2019) A hybrid short-term load forecasting model based on variational mode decomposition and long short-term memory networks considering relevant factors with bayesian optimization algorithm. Appl Energy 237:103\u2013116","DOI":"10.1016\/j.apenergy.2019.01.055"},{"issue":"1","key":"9449_CR22","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1109\/JPROC.2015.2494218","volume":"104","author":"B Shahriari","year":"2015","unstructured":"Shahriari B, Swersky K, Wang Z, Adams RP, De Freitas N (2015) Taking the human out of the loop: a review of Bayesian optimization. Proc IEEE 104(1):148\u2013175","journal-title":"Proc IEEE"},{"key":"9449_CR23","unstructured":"Snoek J, Larochelle H, Adams RP (2012) Practical bayesian optimization of machine learning algorithms. Adv Neural Inf Process Syst 25"},{"key":"9449_CR24","doi-asserted-by":"crossref","unstructured":"Quakulinski L, Koumpis A, Beyan OD (2022) 2022 Fourth International Conference on Transdisciplinary AI (TransAI) (IEEE, 2022), pp 116\u2013121","DOI":"10.1109\/TransAI54797.2022.00027"},{"key":"9449_CR25","unstructured":"Lundberg SM, Lee SI (2017) A unified approach to interpreting model predictions. Adv Neural Inf Process Syst 30"},{"issue":"1","key":"9449_CR26","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1006\/game.1994.1008","volume":"6","author":"AS Nowak","year":"1994","unstructured":"Nowak AS, Radzik T (1994) The Shapley value for n-person games in generalized characteristic function form. Games Econ Behav 6(1):150\u2013161","journal-title":"Games Econ Behav"},{"issue":"1","key":"9449_CR27","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1109\/TNNLS.2017.2716952","volume":"29","author":"CP Chen","year":"2017","unstructured":"Chen CP, Liu Z (2017) Broad learning system: an effective and efficient incremental learning system without the need for deep architecture. IEEE Trans Neural Netw Learn Syst 29(1):10\u201324","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"2","key":"9449_CR28","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1016\/0925-2312(94)90053-1","volume":"6","author":"YH Pao","year":"1994","unstructured":"Pao YH, Park GH, Sobajic DJ (1994) Learning and generalization characteristics of the random vector functional-link net. Neurocomputing 6(2):163\u2013180","journal-title":"Neurocomputing"},{"issue":"6","key":"9449_CR29","doi-asserted-by":"publisher","first-page":"1320","DOI":"10.1109\/72.471375","volume":"6","author":"B Igelnik","year":"1995","unstructured":"Igelnik B, Pao YH (1995) Stochastic choice of basis functions in adaptive function approximation and the functional-link net. IEEE Trans Neural Netw 6(6):1320\u20131329","journal-title":"IEEE Trans Neural Netw"},{"issue":"9","key":"9449_CR30","doi-asserted-by":"publisher","first-page":"8922","DOI":"10.1109\/TCYB.2021.3061094","volume":"52","author":"X Gong","year":"2021","unstructured":"Gong X, Zhang T, Chen CP, Liu Z (2021) Research review for broad learning system: algorithms, theory, and applications. IEEE Trans Cybern 52(9):8922\u20138950","journal-title":"IEEE Trans Cybern"},{"key":"9449_CR31","unstructured":"John Hopkins University. Coronavirus map. https:\/\/coronavirus.jhu.edu\/map.html"},{"issue":"1","key":"9449_CR32","doi-asserted-by":"publisher","first-page":"865","DOI":"10.3390\/ijerph20010865","volume":"20","author":"Z Zhang","year":"2023","unstructured":"Zhang Z, Li X, Lyu K, Zhao X, Zhang F, Liu D, Zhao Y, Gao F, Hu J, Xu D (2023) Exploring the transmission path, influencing factors and risk of aerosol transmission of SARS-cov-2 at xi\u2019an Xianyang international airport. Int J Environ Res Public Health 20(1):865","journal-title":"Int J Environ Res Public Health"},{"key":"9449_CR33","doi-asserted-by":"crossref","unstructured":"Zhan C, Jiang W, Min H, Gao Y, Tse C (2023) Human migration-based graph convolutional network for pm2. 5 forecasting in post-covid-19 pandemic age. Neural Comput Appl 35(9):6457\u20136470","DOI":"10.1007\/s00521-022-07876-0"},{"issue":"1","key":"9449_CR34","doi-asserted-by":"publisher","first-page":"9332","DOI":"10.1038\/s41598-022-13371-2","volume":"12","author":"A Dutta","year":"2022","unstructured":"Dutta A (2022) Covid-19 waves: variant dynamics and control. Sci Rep 12(1):9332","journal-title":"Sci Rep"},{"issue":"1","key":"9449_CR35","first-page":"9","volume":"136","author":"DAA Gnana","year":"2016","unstructured":"Gnana DAA, Balamurugan SAA, Leavline EJ (2016) Literature review on feature selection methods for high-dimensional data. Int J Comput Appl 136(1):9\u201317","journal-title":"Int J Comput Appl"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09449-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-09449-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09449-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,11]],"date-time":"2024-11-11T22:56:12Z","timestamp":1731365772000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-09449-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,20]]},"references-count":35,"journal-issue":{"issue":"13","published-print":{"date-parts":[[2024,5]]}},"alternative-id":["9449"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-09449-9","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2024,2,20]]},"assertion":[{"value":"9 August 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 January 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 February 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All authors declare that they have no conflict of interest to this work.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}