About: Modeling the trend of coronavirus disease 2019 and restoration of operational capability of metropolitan medical service in China: a machine learning and mathematical model-based analysis

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About: Modeling the trend of coronavirus disease 2019 and restoration of operational capability of metropolitan medical service in China: a machine learning and mathematical model-based analysis Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Modeling the trend of coronavirus disease 2019 and restoration of operational capability of metropolitan medical service in China: a machine learning and mathematical model-based analysis
Creator	Yin, Xin Zhang, Hao Liang, Huiying Huang, Shuai Liu, Zeye Lu, Wenlong Su, Zhanhao
source	Medline; PMC
abstract	BACKGROUND: To contain the outbreak of coronavirus disease 2019 (COVID-19) in China, many unprecedented intervention measures are adopted by the government. However, these measures may interfere in the normal medical service. We sought to model the trend of COVID-19 and estimate the restoration of operational capability of metropolitan medical service in China. METHODS: Real-time data of COVID-19 and population mobility data were extracted from open sources. SEIR (Susceptible, Exposed, Infectious, Recovered) and neural network models (NNs) were built to model disease trends in Wuhan, Beijing, Shanghai and Guangzhou. Combined with public transportation data, Autoregressive Integrated Moving Average (ARIMA) model was used to estimate the accumulated demands for nonlocal hospitalization during the epidemic period in Beijing, Shanghai and Guangzhou. RESULTS: The number of infected people and deaths would increase by 45% and 567% respectively, given that the government only has implemented traffic control in Wuhan without additional medical professionals. The epidemic of Wuhan (measured by cumulative confirmed cases) was predicted to reach turning point at the end of March and end in later April, 2020. The outbreak in Beijing, Shanghai and Guangzhou was predicted to end at the end of March and the medical service could be fully back to normal in middle of April. During the epidemic, the number of nonlocal inpatient hospitalizations decreased by 69.86%, 57.41% and 66.85% in Beijing, Shanghai and Guangzhou respectively. After the end of epidemic, medical centers located in these metropolises may face 58,799 (95% CI 48926–67,232) additional hospitalization needs in the first month. CONCLUSION: The COVID-19 epidemic in China has been effectively contained and medical service across the country is expected to return to normal in April. However, the huge unmet medical needs for other diseases could result in massive migration of patients and their families, bringing tremendous challenges for medical service in major metropolis and disease control for the potential asymptomatic virus carrier.
has issue date	2020-05-06(xsd:dateTime)
bibo:doi	10.1186/s41256-020-00145-4
bibo:pmid	32391439
has license	cc-by
sha1sum (hex)	fe72acc2584f86459acf0883c1bd47d78377bfa3
schema:url	https://doi.org/10.1186/s41256-020-00145-4
resource representing a document's title	Modeling the trend of coronavirus disease 2019 and restoration of operational capability of metropolitan medical service in China: a machine learning and mathematical model-based analysis
has PubMed Central identifier	PMC7200323
has PubMed identifier	32391439
schema:publication	Glob Health Res Policy
resource representing a document's body	covid:fe72acc2584f86459acf0883c1bd47d78377bfa3#body_text
is schema:about of	named entity 'BACKGROUND' named entity 'MEDICAL SERVICE' named entity 'CHINA' named entity 'OUTBREAK' named entity 'ESTIMATE' named entity 'service in China' named entity 'mathematical model' named entity 'coronavirus disease 2019' named entity 'COVID' named entity 'mathematical model' named entity 'medical institutions' named entity 'medical service' named entity 'Wuhan' named entity 'SEIR' named entity 'Chinese lunar calendar' named entity 'surgical treatment' named entity 'neural network' named entity 'epidemiological' named entity 'medical professionals' named entity 'epidemic' named entity 'time series prediction' named entity 'public health emergency' named entity 'quarantine' named entity 'Guangzhou' named entity 'epidemic' named entity 'infectious diseases' named entity 'Hubei province' named entity 'Guangzhou' named entity 'incubation period' named entity 'epidemic' named entity 'social distancing' named entity 'ARIMA' named entity 'mainland China' named entity 'Hubei province' named entity 'inflection point' named entity 'SEIR' named entity 'epidemic' named entity 'Shanghai' named entity 'medical service' named entity 'SEIR' named entity 'Beijing' named entity 'Wuhan' named entity 'Hubei' named entity 'predictive model' named entity 'epidemic' named entity 'epidemic' named entity 'medical service' named entity 'medical care' named entity 'medical professionals' named entity 'medical service' named entity 'SEIR' named entity 'Beijing' named entity 'Wuhan' named entity 'Shanghai' named entity 'Beijing' named entity 'COVID' named entity 'Autoregressive Integrated Moving Average model' named entity 'epidemic' named entity 'quarantine' named entity 'Baidu' named entity 'Wuhan' named entity 'cumulant' named entity 'Guangzhou' named entity 'epidemic' named entity 'quarantine' named entity 'SEIR' named entity 'Guangzhou' named entity 'medical service' named entity 'mathematical model' named entity 'COVID-19' named entity 'epidemic' named entity '2, 3' named entity 'Guangzhou' named entity 'epidemic' named entity '15,000'

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