About: Short-term forecasts and long-term mitigation evaluations for the COVID-19 epidemic in Hubei Province, China

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About: Short-term forecasts and long-term mitigation evaluations for the COVID-19 epidemic in Hubei Province, China 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	Short-term forecasts and long-term mitigation evaluations for the COVID-19 epidemic in Hubei Province, China
Creator	Guo, Xiaolong Cohnstaedt, Lee Scoglio, Caterina Vajdi, Aram Wu, Hongyu Yang, Qihui Yi, Chunlin Cohnstaedt, Vajdi Scoglio, Guo
Source	Elsevier; Medline; PMC
abstract	Abstract As an emerging infectious disease, the 2019 coronavirus disease (COVID-19) has developed into a global pandemic. During the initial spreading of the virus in China, we demonstrated the ensemble Kalman filter performed well as a short-term predictor of the daily cases reported in Wuhan City. Second, we used an individual-level network-based model to reconstruct the epidemic dynamics in Hubei Province and examine the effectiveness of non-pharmaceutical interventions on the epidemic spreading with various scenarios. Our simulation results show that without continued control measures, the epidemic in Hubei Province could have become persistent. Only by continuing to decrease the infection rate through 1) protective measures and 2) social distancing can the actual epidemic trajectory that happened in Hubei Province be reconstructed in simulation. Finally, we simulate the COVID-19 transmission with non-Markovian processes and show how these models produce different epidemic trajectories, compared to those obtained with Markov processes. Since recent studies show that COVID-19 epidemiological parameters do not follow exponential distributions leading to Markov processes, future works need to focus on non-Markovian models to better capture the COVID-19 spreading trajectories. In addition, shortening the infectious period via early case identification and isolation can slow the epidemic spreading significantly.
has issue date	2020-08-13(xsd:dateTime)
bibo:doi	10.1016/j.idm.2020.08.001
bibo:pmid	32835146
has license	els-covid
sha1sum (hex)	2e32a4c99ae1609a98094a4b814592bbd73a7e26
schema:url	https://doi.org/10.1016/j.idm.2020.08.001
resource representing a document's title	Short-term forecasts and long-term mitigation evaluations for the COVID-19 epidemic in Hubei Province, China
has PubMed Central identifier	PMC7425645
has PubMed identifier	32835146
schema:publication	Infectious Disease Modelling
resource representing a document's body	covid:2e32a4c99ae1609a98094a4b814592bbd73a7e26#body_text
is schema:about of	named entity 'actual' named entity 'City' named entity 'OUR' named entity 'DIFFERENT' named entity 'MARKOVIAN MODELS' named entity 'RECONSTRUCTED' named entity 'EPIDEMIOLOGICAL' named entity 'THESE' named entity 'INFECTIOUS PERIOD' named entity 'EARLY' named entity 'LONG-TERM' named entity 'EVALUATIONS' named entity 'FORECASTS' named entity 'COVID-19 EPIDEMIC' named entity 'HUBEI PROVINCE' covid:arg/2e32a4c99ae1609a98094a4b814592bbd73a7e26 named entity 'addition' named entity 'social distancing' named entity 'COVID-19' named entity 'exponential distributions' named entity 'focus' named entity 'case' named entity 'transmission' named entity 'developed' named entity 'measures' named entity 'leading' named entity 'ensemble Kalman filter' named entity 'non-Markovian' named entity 'virus' named entity 'epidemiological' named entity 'ensemble Kalman filter' named entity 'Hubei Province' named entity 'Hubei Province' named entity 'Wuhan City' named entity 'China' named entity 'COVID' named entity 'epidemic' named entity 'Short-term' named entity 'Hubei Province' named entity '95% CI' named entity 'Poisson process' named entity 'Wuhan' named entity 'extended Kalman filter' named entity 'epidemic' named entity 'MAPE' named entity 'epidemic' named entity 'self-quarantine' named entity 'non-Markovian' named entity 'January 27' named entity 'February 10' named entity 'exponential distribution' named entity 'February 12' named entity 'susceptible individual' named entity 'relative error' named entity 'self-quarantine' named entity 'discrete time' named entity 'piecewise constant function' named entity 'January 23' named entity 'epidemic' named entity 'South Korea' named entity 'China' named entity 'February 19' named entity 'January 1' named entity 'Wuhan' named entity 'epidemics' named entity 'Wuhan' named entity 'Hubei Province' named entity 'ensemble Kalman filter' named entity 'January 19' named entity 'exponential distribution' named entity 'lockdown' named entity 'ensemble Kalman filter' named entity 'January 18'

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