{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T18:13:09Z","timestamp":1780596789972,"version":"3.54.1"},"reference-count":32,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2024,9,10]],"date-time":"2024-09-10T00:00:00Z","timestamp":1725926400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004499","name":"Hubei University of Automotive Technology","doi-asserted-by":"publisher","award":["BK202209"],"award-info":[{"award-number":["BK202209"]}],"id":[{"id":"10.13039\/501100004499","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004499","name":"Hubei University of Automotive Technology","doi-asserted-by":"publisher","award":["2023EHA018"],"award-info":[{"award-number":["2023EHA018"]}],"id":[{"id":"10.13039\/501100004499","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010580","name":"Hubei Provincial Bureau of Science and Technology","doi-asserted-by":"publisher","award":["BK202209"],"award-info":[{"award-number":["BK202209"]}],"id":[{"id":"10.13039\/501100010580","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010580","name":"Hubei Provincial Bureau of Science and Technology","doi-asserted-by":"publisher","award":["2023EHA018"],"award-info":[{"award-number":["2023EHA018"]}],"id":[{"id":"10.13039\/501100010580","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>The mainstream compartmental models require stochastic parameterization to estimate the transmission parameters between compartments, whose calculation depend upon detailed statistics on epidemiological characteristics, which are expensive, economically and resource-wise, to collect. In addition, infectious diseases spread in three dimensions: temporal, spatial, and mobile, i.e., they affect a population through not only the time progression of infection, but also the geographic distribution and physical mobility of the population. However, the parameterization process for the mainstream compartmental models does not effectively capture the spatial and mobile dimensions. As an alternative, deep learning techniques are utilized in estimating these stochastic parameters with greatly reduced dependency on data particularity and with a built-in temporal\u2013spatial\u2013mobile process that models the geographic distribution and physical mobility of the population. In particular, we apply DNN (Deep Neural Network) and LSTM (Long-Short Term Memory) techniques to estimate the transmission parameters in a customized compartmental model, then feed the estimated transmission parameters to the compartmental model to predict the development of the Omicron epidemic in China over the 28 days for the period between 4 June and 1 July 2022. The average levels of predication accuracy of the model are 98% and 92% for the number of infections and deaths, respectively. We establish that deep learning techniques provide an alternative to the prevalent compartmental modes and demonstrate the efficacy and potential of applying deep learning methodologies in predicting the dynamics of infectious diseases.<\/jats:p>","DOI":"10.3390\/bioengineering11090906","type":"journal-article","created":{"date-parts":[[2024,9,10]],"date-time":"2024-09-10T09:10:57Z","timestamp":1725959457000},"page":"906","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A Deep Learning-Enhanced Compartmental Model and Its Application in Modeling Omicron in China"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5318-0725","authenticated-orcid":false,"given":"Qi","family":"Deng","sequence":"first","affiliation":[{"name":"College of Artificial Intelligence, Hubei University of Automotive Technology, Shiyan 442002, China"},{"name":"Jack Welch College of Business and Technology, Sacred Heart University, Fairfield, CT 06825, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guifang","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Respiratory Diseases and Critical Medicine, Huashan Hospital, Fudan University, Shanghai 200040, China"},{"name":"Department of Respiratory Diseases and Critical Medicine, Quzhou Hospital, Wenzhou Medical University, Quzhou 325015, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e356","DOI":"10.1016\/S2468-2667(22)00040-8","article-title":"Maintaining face mask use before and after achieving different COVID-19 vaccination coverage levels: A modelling study","volume":"7","author":"Bartsch","year":"2022","journal-title":"Lancet Public Health"},{"key":"ref_2","unstructured":"COVID-19 Cumulative Infection Collaborators (2022). 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