About: Extended SIR Prediction of the Epidemics Trend of COVID-19 in Italy and Compared With Hunan, China

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Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Extended SIR Prediction of the Epidemics Trend of COVID-19 in Italy and Compared With Hunan, China
Creator	Li, Jing Yang, Song Kozlakidis, Zisis Fuyin, Kou Jianwei, Wang Ke, Han Miao, Liu Penggang, Tai Shanshan, Yang Shengshu, Wang Wangping, Jia Wenzhe, Cao Yao, He Hattaf, Khalid Ayse, Morocco Bilge, Humeyra
Source	Medline; PMC; WHO
abstract	Background: Coronavirus Disease 2019 (COVID-19) is currently a global public health threat. Outside of China, Italy is one of the countries suffering the most with the COVID-19 epidemic. It is important to predict the epidemic trend of the COVID-19 epidemic in Italy to help develop public health strategies. Methods: We used time-series data of COVID-19 from Jan 22 2020 to Apr 02 2020. An infectious disease dynamic extended susceptible-infected-removed (eSIR) model, which covers the effects of different intervention measures in dissimilar periods, was applied to estimate the epidemic trend in Italy. The basic reproductive number was estimated using Markov Chain Monte Carlo methods and presented using the resulting posterior mean and 95% credible interval (CI). Hunan, with a similar total population number to Italy, was used as a comparative item. Results: In the eSIR model, we estimated that the mean of basic reproductive number for COVID-19 was 4.34 (95% CI, 3.04–6.00) in Italy and 3.16 (95% CI, 1.73–5.25) in Hunan. There would be a total of 182 051 infected cases (95%CI:116 114–274 378) under the current country blockade and the endpoint would be Aug 05 in Italy. Conclusion: Italy's current strict measures can efficaciously prevent the further spread of COVID-19 and should be maintained. Necessary strict public health measures should be implemented as soon as possible in other European countries with a high number of COVID-19 cases. The most effective strategy needs to be confirmed in further studies.
has issue date	2020-05-06(xsd:dateTime)
bibo:doi	10.3389/fmed.2020.00169
bibo:pmid	32435645
has license	cc-by
sha1sum (hex)	62df9d74126a930729486710da29cec93eb0c046
schema:url	https://doi.org/10.3389/fmed.2020.00169
resource representing a document's title	Extended SIR Prediction of the Epidemics Trend of COVID-19 in Italy and Compared With Hunan, China
has PubMed Central identifier	PMC7218168
has PubMed identifier	32435645
schema:publication	Front Med (Lausanne)
resource representing a document's body	covid:62df9d74126a930729486710da29cec93eb0c046#body_text
is schema:about of	named entity 'INFECTED' named entity 'MONTE CARLO METHODS' named entity 'MARKOV CHAIN MONTE CARLO' named entity 'REPRODUCTIVE' named entity 'MODEL' named entity 'Markov Chain Monte Carlo' named entity 'infectious disease' named entity 'epidemic' named entity 'Epidemics' named entity 'quarantine' named entity 'Italy' named entity 'Italy' named entity 'Italy' named entity 'vector' named entity 'preventative measures' named entity 'infectivity' named entity 'transmission rate' named entity 'SIR model' named entity 'subclinical' named entity 'Italy' named entity 'global public health' named entity 'Italian' named entity 'time series' named entity 'Hubei' named entity 'real number' named entity 'R software' named entity 'SIR model' named entity 'SEIR' named entity 'COVID' named entity 'Hunan' named entity 'MCMC' named entity '2019-nCoV' named entity 'Italy' named entity 'epidemiological' named entity 'Japan' named entity 'epidemic' named entity 'World Health Organization' named entity 'superspreaders' named entity 'Epidemic' named entity 'Hubei' named entity 'incubation period' named entity 'epidemiological model' named entity 'prevention and control' named entity 'reproduction number' named entity 'infectious disease' named entity 'COVID' named entity 'Corona Virus Disease 2019' named entity 'epidemic' named entity 'credible intervals' named entity 'Chinese government' named entity '0.9' named entity 'China' named entity 'epidemic' named entity 'Hunan' named entity 'COVID' named entity 'social distancing' named entity 'SIR model' named entity 'SIR model' named entity 'Johns Hopkins University' named entity 'Global public health' named entity 'data analysis' named entity 'COVID' named entity 'scientific research' named entity 'Hunan' named entity 'epidemiological model' named entity 'public health'

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