{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T03:04:54Z","timestamp":1763348694786,"version":"3.41.0"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"21","license":[{"start":{"date-parts":[[2024,8,9]],"date-time":"2024-08-09T00:00:00Z","timestamp":1723161600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,8,9]],"date-time":"2024-08-09T00:00:00Z","timestamp":1723161600000},"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":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-024-19918-x","type":"journal-article","created":{"date-parts":[[2024,8,9]],"date-time":"2024-08-09T06:02:50Z","timestamp":1723183370000},"page":"23369-23400","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["CAST2-Zone Wise Disease Outbreak Control Model for SARS-Cov 2"],"prefix":"10.1007","volume":"84","author":[{"given":"P.","family":"Muthulakshmi","sequence":"first","affiliation":[]},{"given":"K.","family":"Suthendran","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6873-6469","authenticated-orcid":false,"given":"Vinayakumar","family":"Ravi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,8,9]]},"reference":[{"key":"19918_CR1","doi-asserted-by":"publisher","first-page":"111017","DOI":"10.1016\/j.jtbi.2022.111017","volume":"538","author":"JM Calabrese","year":"2022","unstructured":"Calabrese JM, Demers J (2022) How optimal allocation of limited testing capacity changes epidemic dynamics. J Theor Biol 538:111017","journal-title":"J Theor Biol"},{"issue":"7","key":"19918_CR2","doi-asserted-by":"publisher","first-page":"1449","DOI":"10.3390\/v15071449","volume":"15","author":"FJ Candel","year":"2023","unstructured":"Candel FJ, Barreiro P, Salavert M, Cabello A, Fern\u00e1ndez-Ruiz M, P\u00e9rez-Segura P, San Rom\u00e1n J, Berenguer J, C\u00f3rdoba R, Delgado R, Espa\u00f1a PP (2023) Expert consensus: main risk factors for poor prognosis in COVID-19 and the implications for targeted measures against SARS-CoV-2. Viruses 15(7):1449","journal-title":"Viruses"},{"key":"19918_CR3","unstructured":"Kuryliak, Y., Emmerich, M. and Dosyn, D., 2022. Efficient Stochastic Simulation of Network Topology Effects on the Peak Number of Infections in Epidemic Outbreaks. arXiv preprint arXiv:2202.13325."},{"issue":"4","key":"19918_CR4","doi-asserted-by":"publisher","first-page":"569","DOI":"10.3390\/vaccines10040569","volume":"10","author":"T Hussein","year":"2022","unstructured":"Hussein T, Hammad MH, Surakhi O, AlKhanafseh M, Fung PL, Zaidan MA, Wraith D, Ershaidat N (2022) Short-Term and Long-Term COVID-19 Pandemic Forecasting Revisited with the Emergence of OMICRON Variant in Jordan. Vaccines 10(4):569","journal-title":"Vaccines"},{"key":"19918_CR5","doi-asserted-by":"publisher","first-page":"108255","DOI":"10.1016\/j.patcog.2021.108255","volume":"122","author":"A Kumar","year":"2022","unstructured":"Kumar A, Tripathi AR, Satapathy SC, Zhang YD (2022) SARS-Net: COVID-19 detection from chest X-rays by combining graph convolutional network and convolutional neural network. Pattern Recogn 122:108255","journal-title":"Pattern Recogn"},{"key":"19918_CR6","doi-asserted-by":"publisher","first-page":"107970","DOI":"10.1016\/j.cie.2022.107970","volume":"166","author":"D Liu","year":"2022","unstructured":"Liu D, Ding W, Dong ZS, Pedrycz W (2022) Optimizing deep neural networks to predict the effect of social distancing on COVID-19 spread. Comput Ind Eng 166:107970","journal-title":"Comput Ind Eng"},{"key":"19918_CR7","doi-asserted-by":"publisher","unstructured":"Tomy A, Razzanelli M, Di Lauro F, Rus D, Della Santina C (2022) Estimating the state of epidemics spreading with graph neural networks. Nonlinear Dyn 1\u201315. https:\/\/doi.org\/10.1007\/s11071-021-07160-1","DOI":"10.1007\/s11071-021-07160-1"},{"key":"19918_CR8","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1051\/mmnp\/2020022","volume":"15","author":"R Elie","year":"2020","unstructured":"Elie R, Hubert E, Turinici G (2020) Contact rate epidemic control of COVID-19: an equilibrium view. Mathematical Modelling of Natural Phenomena 15:35","journal-title":"Mathematical Modelling of Natural Phenomena"},{"key":"19918_CR9","doi-asserted-by":"publisher","unstructured":"Liu J, Zhang Q, Shen A, Gao Y, Hou J, Wang B, Yan T (2022) (2022) A Novel Light Field Image Compression Method Using EPI Restoration Neural Network. Biomed Res Int 2022(1):8324438. https:\/\/doi.org\/10.1155\/2022\/8324438","DOI":"10.1155\/2022\/8324438"},{"key":"19918_CR10","doi-asserted-by":"publisher","first-page":"e998","DOI":"10.7717\/peerj-cs.998","volume":"8","author":"A Bramantoro","year":"2022","unstructured":"Bramantoro A, Virdyna I (2022) Classification of divorce causes during the COVID-19 pandemic using convolutional neural networks. PeerJ Computer Science 8:e998","journal-title":"PeerJ Computer Science"},{"key":"19918_CR11","first-page":"102731","volume":"108","author":"Y Xiao","year":"2022","unstructured":"Xiao Y, Yuan Q, He J, Zhang Q, Sun J, Su X, Wu J, Zhang L (2022) Space-time super-resolution for satellite video: A joint framework based on multi-scale spatial-temporal transformer. Int J Appl Earth Obs Geoinf 108:102731","journal-title":"Int J Appl Earth Obs Geoinf"},{"key":"19918_CR12","first-page":"102670","volume":"106","author":"B Sisheber","year":"2022","unstructured":"Sisheber B, Marshall M, Ayalew D, Nelson A (2022) Tracking crop phenology in a highly dynamic landscape with knowledge-based Landsat\u2013MODIS data fusion. Int J Appl Earth Obs Geoinf 106:102670","journal-title":"Int J Appl Earth Obs Geoinf"},{"issue":"5","key":"19918_CR13","doi-asserted-by":"publisher","first-page":"3086","DOI":"10.1109\/TNSE.2022.3151502","volume":"10","author":"Y Wu","year":"2022","unstructured":"Wu Y, Guo H, Chakraborty C, Khosravi M, Berretti S, Wan S (2022) Edge computing-driven low-light image dynamic enhancement for object detection. IEEE Trans Netw Sci Eng 10(5):3086\u20133098. https:\/\/doi.org\/10.1109\/TNSE.2022.3151502","journal-title":"IEEE Trans Netw Sci Eng"},{"issue":"4","key":"19918_CR14","doi-asserted-by":"publisher","first-page":"e00783","DOI":"10.1128\/spectrum.00783-22","volume":"10","author":"H Tian","year":"2022","unstructured":"Tian H, Zhao L, Koski TM, Sun J (2022) Microhabitat governs the microbiota of the pinewood nematode and its vector beetle: implication for the prevalence of pine wilt disease. Microbiology Spectrum 10(4):e00783-e822","journal-title":"Microbiology Spectrum"},{"key":"19918_CR15","volume-title":"Snow-Covered Tires Generate Microhabitats That Enhance Overwintering Survival of Aedes albopictus (Diptera: Culicidae) in the Midwest","author":"KM Susong","year":"2022","unstructured":"Susong KM, Tucker BJ, Bron GM, Irwin P, Kirsch JM, Vimont D, Stone C, Paskewitz SM, Bartholomay LC (2022) Snow-Covered Tires Generate Microhabitats That Enhance Overwintering Survival of Aedes albopictus (Diptera: Culicidae) in the Midwest. Environmental Entomology, USA"},{"key":"19918_CR16","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1016\/j.media.2018.06.001","volume":"48","author":"S Parisot","year":"2018","unstructured":"Parisot S, Ktena SI, Ferrante E, Lee M, Guerrero R, Glocker B, Rueckert D (2018) Disease prediction using graph convolutional networks: application to autism spectrum disorder and Alzheimer\u2019s disease. Med Image Anal 48:117\u2013130","journal-title":"Med Image Anal"},{"key":"19918_CR17","doi-asserted-by":"publisher","first-page":"34717","DOI":"10.1109\/ACCESS.2020.2974687","volume":"8","author":"MA Khan","year":"2020","unstructured":"Khan MA (2020) An IoT framework for heart disease prediction based on the MDCNN classifier. IEEE Access 8:34717\u201334727","journal-title":"IEEE Access"},{"issue":"7","key":"19918_CR18","doi-asserted-by":"publisher","first-page":"1462","DOI":"10.1002\/jemt.23702","volume":"84","author":"T Saba","year":"2021","unstructured":"Saba T, Abunadi I, Shahzad MN, Khan AR (2021) Machine learning techniques to detect and forecast the daily total COVID-19 infected and death cases under different lockdown types. Microsc Res Tech 84(7):1462\u20131474","journal-title":"Microsc Res Tech"},{"issue":"9","key":"19918_CR19","doi-asserted-by":"publisher","first-page":"e0257234","DOI":"10.1371\/journal.pone.0257234","volume":"16","author":"MA Quiroz-Ju\u00e1rez","year":"2021","unstructured":"Quiroz-Ju\u00e1rez MA, Torres-G\u00f3mez A, Hoyo-Ulloa I, Le\u00f3n-Montiel RDJ, U\u2019Ren AB (2021) Identification of high-risk COVID-19 patients using machine learning. PLoS ONE 16(9):e0257234","journal-title":"PLoS ONE"},{"issue":"3","key":"19918_CR20","first-page":"731","volume":"12","author":"AMUD Khanday","year":"2020","unstructured":"Khanday AMUD, Rabani ST, Khan QR, Rouf N, Din MU, M. (2020) Machine learning-based approaches for detecting COVID-19 using clinical text data. Int J Inf Technol 12(3):731\u2013739","journal-title":"Int J Inf Technol"},{"key":"19918_CR21","doi-asserted-by":"publisher","unstructured":"Ngoc KM, Lee M (2021) Forecasting COVID-19 confirmed cases in South Korea using Spatio-Temporal Graph Neural Networks. Int J Contents 17(3). https:\/\/doi.org\/10.5392\/IJoC.2021.17.3.001","DOI":"10.5392\/IJoC.2021.17.3.001"},{"issue":"7","key":"19918_CR22","doi-asserted-by":"publisher","first-page":"3834","DOI":"10.3390\/ijerph18073834","volume":"18","author":"MR Davahli","year":"2021","unstructured":"Davahli MR, Fiok K, Karwowski W, Aljuaid AM, Taiar R (2021) Predicting the dynamics of the COVID-19 pandemic in the United States using graph theory-based neural networks. Int J Environ Res Public Health 18(7):3834","journal-title":"Int J Environ Res Public Health"},{"key":"19918_CR23","doi-asserted-by":"publisher","first-page":"116366","DOI":"10.1016\/j.eswa.2021.116366","volume":"192","author":"M Scarpiniti","year":"2022","unstructured":"Scarpiniti M, Ahrabi SS, Baccarelli E, Piazzo L, Momenzadeh A (2022) A novel unsupervised approach based on the hidden features of Deep Denoising Autoencoders for COVID-19 disease detection. Expert Syst Appl 192:116366","journal-title":"Expert Syst Appl"},{"issue":"3","key":"19918_CR24","doi-asserted-by":"publisher","first-page":"5817","DOI":"10.32604\/cmc.2023.036830","volume":"75","author":"W Chung","year":"2023","unstructured":"Chung W, Moon J, Kim D, Hwang E (2023) Graph Construction Method for GNN-Based Multivariate Time-Series Forecasting. Comput Mater Contin 75(3):5817\u20135836. https:\/\/doi.org\/10.32604\/cmc.2023.036830","journal-title":"Comput Mater Contin"},{"issue":"13","key":"19918_CR25","doi-asserted-by":"publisher","first-page":"2250199","DOI":"10.1142\/S0218127422501991","volume":"32","author":"W Paxson","year":"2022","unstructured":"Paxson W, Shen BW (2022) A KdV\u2013SIR Equation and Its Analytical Solutions for Solitary Epidemic Waves. International Journal of Bifurcation and Chaos 32(13):2250199","journal-title":"International Journal of Bifurcation and Chaos"},{"issue":"1","key":"19918_CR26","doi-asserted-by":"publisher","first-page":"e230040","DOI":"10.1175\/AIES-D-23-0040.1","volume":"3","author":"Z Zhen","year":"2024","unstructured":"Zhen Z, Lee H, Segovia-Dominguez I, Huang M, Chen Y, Garay M, Crichton D, Gel YR (2024) Environmental Justice and Lessons Learned from COVID-19 Outcomes\u2014Uncovering Hidden Patterns with Geometric Deep Learning and New NASA Satellite Data. Artificial Intelligence for the Earth Systems 3(1):e230040","journal-title":"Artificial Intelligence for the Earth Systems"},{"issue":"7","key":"19918_CR27","doi-asserted-by":"publisher","first-page":"2515","DOI":"10.1007\/s00521-020-05145-6","volume":"33","author":"M Braik","year":"2021","unstructured":"Braik M, Sheta A, Al-Hiary H (2021) A novel meta-heuristic search algorithm for solving optimization problems: capuchin search algorithm. Neural Comput Appl 33(7):2515\u20132547","journal-title":"Neural Comput Appl"},{"key":"19918_CR28","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1016\/j.ins.2021.10.070","volume":"583","author":"IA Zamfirache","year":"2022","unstructured":"Zamfirache IA, Precup RE, Roman RC, Petriu EM (2022) Reinforcement Learning-based control using Q-learning and gravitational search algorithm with experimental validation on a nonlinear servo system. Inf Sci 583:99\u2013120","journal-title":"Inf Sci"},{"issue":"1","key":"19918_CR29","first-page":"222","volume":"35","author":"MH Mozaffari","year":"2016","unstructured":"Mozaffari MH, Abdy H, Zahiri SH (2016) IPO: an inclined plane system optimization algorithm. Computing and Informatics 35(1):222\u2013240","journal-title":"Computing and Informatics"},{"issue":"3","key":"19918_CR30","doi-asserted-by":"publisher","first-page":"1663","DOI":"10.1007\/s11831-022-09849-x","volume":"30","author":"J Nayak","year":"2023","unstructured":"Nayak J, Swapnarekha H, Naik B, Dhiman G, Vimal S (2023) 25 years of particle swarm optimization: Flourishing voyage of two decades. Archives of Computational Methods in Engineering 30(3):1663\u20131725","journal-title":"Archives of Computational Methods in Engineering"},{"issue":"1","key":"19918_CR31","doi-asserted-by":"publisher","first-page":"7","DOI":"10.52158\/jacost.v1i1.9","volume":"1","author":"AF Watratan","year":"2020","unstructured":"Watratan AF, Moeis D (2020) Implementasi Algoritma Naive Bayes Untuk Memprediksi Tingkat Penyebaran Covid-19 Di Indonesia. Journal of Applied Computer Science and Technology 1(1):7\u201314","journal-title":"Journal of Applied Computer Science and Technology"},{"key":"19918_CR32","doi-asserted-by":"publisher","first-page":"pp. 765","DOI":"10.1007\/978-981-16-5301-8_54","volume-title":"Soft Computing for Security Applications: Proceedings of ICSCS 2021","author":"P May Raju","year":"2022","unstructured":"May Raju P, Gupta GP (2022) Intrusion detection framework using an improved deep reinforcement learning technique for IoT network. Soft Computing for Security Applications: Proceedings of ICSCS 2021. pp 765\u2013779"},{"issue":"9","key":"19918_CR33","doi-asserted-by":"publisher","first-page":"16854","DOI":"10.1109\/TITS.2021.3106042","volume":"23","author":"Y Jing","year":"2021","unstructured":"Jing Y, Guo S, Chen F, Wang X, Li K (2021) Dynamic differential pricing of high-speed railway based on improved GBDT train classification and bootstrap time node determination. IEEE Trans Intell Transp Syst 23(9):16854\u201316866","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"19918_CR34","first-page":"pp. 297","volume-title":"Proceedings of the IEEE\/CVF winter conference on applications of computer vision","author":"T Yu","year":"2022","unstructured":"Springer Singapore, Yu T, Li X, Cai Y, Sun M, Li P (2022) S2-mlp: Spatial-shift mlp architecture for vision. Proceedings of the IEEE\/CVF winter conference on applications of computer vision. pp 297\u2013306"},{"issue":"2","key":"19918_CR35","doi-asserted-by":"publisher","first-page":"46","DOI":"10.24086\/cuesj.v5n2y2021.pp46-51","volume":"5","author":"BN Mohammed","year":"2021","unstructured":"Mohammed BN, Al-Mukhtar FH, Yousif RZ, Almashhadani YS (2021) Automatic classification of COVID-19 chest X-ray images using local binary pattern and binary particle swarm optimization for feature selection. Cihan University-Erbil Scientific Journal 5(2):46\u201351","journal-title":"Cihan University-Erbil Scientific Journal"},{"key":"19918_CR36","doi-asserted-by":"publisher","first-page":"592","DOI":"10.1016\/j.neucom.2020.07.144","volume":"452","author":"X Yu","year":"2021","unstructured":"Yu X, Lu S, Guo L, Wang SH, Zhang YD (2021) ResGNet-C: A graph convolutional neural network for detection of COVID-19. Neurocomputing 452:592\u2013605","journal-title":"Neurocomputing"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-19918-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-024-19918-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-19918-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T14:02:58Z","timestamp":1751464978000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-024-19918-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,9]]},"references-count":36,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["19918"],"URL":"https:\/\/doi.org\/10.1007\/s11042-024-19918-x","relation":{},"ISSN":["1573-7721"],"issn-type":[{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2024,8,9]]},"assertion":[{"value":"2 August 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 June 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 July 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 August 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"None.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of potential conflicts of interest"}},{"value":"None.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"None.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Research involving Human Participants and\/or Animals"}}]}}