About: Risk map of the novel coronavirus (2019-nCoV) in China: proportionate control is needed

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About: Risk map of the novel coronavirus (2019-nCoV) in China: proportionate control is needed Goto Sponge NotDistinct Permalink

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

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Risk map of the novel coronavirus (2019-nCoV) in China: proportionate control is needed
Creator	Li, Xinhai Sun, Yuehua Zhao, Xumao Lou, Yingqiang
Source	MedRxiv
abstract	Background China is running a national level antivirus campaign against the novel coronavirus (2019-nCoV). Strict control measures are being enforced in either the populated areas and remote regions. While the virus is closed to be under control, tremendous economic loss has been caused. Methods and findings We assessed the pandemic risk of 2019-nCoV for all cities/regions in China using the random forest algorithm, taking into account the effect of five factors: the accumulative and increased numbers of confirmed cases, total population, population density, and GDP. We defined four levels of the risk, corresponding to the four response levels to public health emergencies in China. The classification system has good consistency among cities in China, as the error rate of the confusion matrix is 1.58%. Conclusions The pandemic risk of 2019-nCoV is dramatically different among the 442 cities/regions. We recommend to adopt proportionate control policy according to the risk level to reduce unnecessary economic loss.
has issue date	2020-02-18(xsd:dateTime)
bibo:doi	10.1101/2020.02.16.20023838
has license	medrxiv
sha1sum (hex)	fbebe4b66073c44cface2e842754bce26e3e2913
schema:url	https://doi.org/10.1101/2020.02.16.20023838
resource representing a document's title	Risk map of the novel coronavirus (2019-nCoV) in China: proportionate control is needed
resource representing a document's body	covid:fbebe4b66073c44cface2e842754bce26e3e2913#body_text
is schema:about of	named entity 'CONTROL' named entity 'EITHER' named entity 'IS RUNNING' named entity 'NATIONAL LEVEL' named entity 'CHINA' named entity 'CAUSED' named entity 'ECONOMIC LOSS' named entity 'UNDER CONTROL' named entity 'CAMPAIGN' named entity 'MEASURES' named entity 'NOVEL CORONAVIRUS' named entity 'AREAS' named entity 'REGIONS' named entity 'REMOTE' named entity 'CLOSED' named entity 'BEING' named entity 'VIRUS' covid:arg/fbebe4b66073c44cface2e842754bce26e3e2913 named entity 'closed' named entity 'coronavirus' named entity '2019-nCoV' named entity 'GDP' named entity 'preprint' named entity 'infection' named entity '2019-nCoV' named entity 'infectious diseases' named entity 'preprint' named entity '2019-nCoV' named entity '39,249' named entity 'preprint' named entity 'virus transmission' named entity '2019-nCoV' named entity 'virus' named entity 'random forest' named entity 'China' named entity 'CC-BY 4.0 International license' named entity 'preprint' named entity 'CC-BY 4.0 International license' named entity 'province-level municipalities' named entity 'public health emergencies' named entity 'coronavirus' named entity 'GDP' named entity 'preprint' named entity '2019-nCoV' named entity 'infection' named entity 'public health emergencies' named entity 'random forest' named entity 'medRxiv' named entity '2019-nCoV' named entity 'normal life' named entity 'Singapore' named entity 'probability' named entity 'peer-reviewed' named entity 'mainland China' named entity 'Popi' named entity 'coronavirus' named entity 'coronavirus outbreak' named entity 'public health emergencies' named entity 'Qinghai Province' named entity 'preprint' named entity 'GDP' named entity 'virus' named entity 'Random forest' named entity 'medRxiv' named entity 'Gansu Province' named entity 'preprint' named entity 'Mongolia' named entity 'public health emergencies' named entity 'Xinjiang Uygur Autonomous Region' named entity '2019-nCoV' named entity 'random forest' named entity 'infection' named entity '58 million' named entity 'Singapore' named entity 'CC-BY 4.0 International license' named entity 'medRxiv' named entity '2019-nCoV' named entity '2019-nCoV' named entity 'global outbreak' named entity '2019-nCoV' named entity 'GDPi' named entity 'machine learning'

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