About: Using the contact network model and Metropolis-Hastings sampling to reconstruct the COVID-19 spread on the “Diamond Princess”

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About: Using the contact network model and Metropolis-Hastings sampling to reconstruct the COVID-19 spread on the “Diamond Princess” Goto Sponge NotDistinct Permalink

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

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Using the contact network model and Metropolis-Hastings sampling to reconstruct the COVID-19 spread on the “Diamond Princess”
Creator	Li, Xin Liu, Feng Zhu, Cn, Gaofeng Zhu, Gaofeng
Source	Elsevier; Medline; PMC
abstract	Traditional compartmental models such as SIR (susceptible, infected, recovered) assume that the epidemic transmits in a homogeneous population, but the real contact patterns in epidemics are heterogeneous. Employing a more realistic model that considers heterogeneous contact is consequently necessary. Here, we use a contact network to reconstruct unprotected, protected contact, and airborne spread to simulate the two-stages outbreak of COVID-19 (coronavirus disease 2019) on the “Diamond Princess” cruise ship. We employ Bayesian inference and Metropolis-Hastings sampling to estimate the model parameters and quantify the uncertainties by the ensemble simulation technique. During the early epidemic with intensive social contacts, the results reveal that the average transmissibility [Formula: see text] was 0.026 and the basic reproductive number [Formula: see text] was 6.94, triple that in the WHO report, indicating that all people would be infected in one month. The [Formula: see text] and [Formula: see text] decreased to 0.0007 and 0.2 when quarantine was implemented. The reconstruction suggests that diluting the airborne virus concentration in closed settings is useful in addition to isolation, and high-risk susceptible should follow rigorous prevention measures in case exposed. This study can provide useful implications for control and prevention measures for the other cruise ships and closed settings.
has issue date	2020-05-05(xsd:dateTime)
bibo:doi	10.1016/j.scib.2020.04.043
bibo:pmid	32373394
has license	no-cc
sha1sum (hex)	506c014c54d10d042392c98afb6b17a41711d69a
schema:url	https://doi.org/10.1016/j.scib.2020.04.043
resource representing a document's title	Using the contact network model and Metropolis-Hastings sampling to reconstruct the COVID-19 spread on the “Diamond Princess”
has PubMed Central identifier	PMC7198438
has PubMed identifier	32373394
schema:publication	Sci Bull (Beijing)
resource representing a document's body	covid:506c014c54d10d042392c98afb6b17a41711d69a#body_text
is schema:about of	named entity 'contact' named entity 'outbreak' named entity 'MODEL PARAMETERS' named entity 'EXPOSED' named entity 'REPORT' named entity 'DILUTING' named entity 'COVID-19' named entity 'MODEL' named entity 'BUT' named entity 'MODELS' named entity 'ENSEMBLE' named entity 'CLOSED' named entity 'ADDITION' named entity 'SPREAD' named entity 'SAMPLING' named entity 'NECESSARY' named entity 'SIR' named entity 'The' named entity 'reconstruction' named entity 'employ' named entity 'uncertainties' named entity 'COVID' named entity 'Bayesian inference' named entity 'epidemic' named entity 'COVID-19' named entity 'patterns' named entity 'infection rate' named entity 'SARS outbreak' named entity 'coronavirus' named entity 'Hong Kong' named entity 'probabilistic' named entity 'virus' named entity 'spread of the disease' named entity 'infection prevention' named entity 'epidemic' named entity 'network models' named entity 'network theory' named entity 'transmissibility' named entity 'probability space' named entity 'epidemic' named entity 'epidemic' named entity 'aerosol' named entity 'Hong Kong' named entity 'transmissibility' named entity 'COVID-19' named entity 'epidemic' named entity 'COVID-19' named entity 'public gatherings' named entity 'epidemics' named entity 'Amoy Gardens' named entity 'epidemiological' named entity 'epidemic' named entity 'small-world network' named entity 'Mathematical modeling' named entity 'probability' named entity 'transmissibility' named entity 'epidemic model' named entity 'reproductive number' named entity 'epidemic model' named entity 'aerosols' named entity 'data assimilation' named entity 'Bayesian inference' named entity '20 January' named entity 'Yokohama' named entity 'infection rate' named entity 'transmissibility' named entity '19 February' named entity 'incubation' named entity 'SARS' named entity 'coronavirus' named entity 'time interval' named entity 'epidemics' named entity 'transmissibility' named entity 'Japan' named entity 'randomness' named entity 'prevention and control' named entity 'cruise ship'

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