Time series modelling to forecast the confirmed and recovered cases of COVID-19.
Autoregressive model
COVID-29
Coronaviruses
Prediction
Two pieces distributions based on the scale mixtures normal distribution
Journal
Travel medicine and infectious disease
ISSN: 1873-0442
Titre abrégé: Travel Med Infect Dis
Pays: Netherlands
ID NLM: 101230758
Informations de publication
Date de publication:
Historique:
received:
09
03
2020
revised:
07
05
2020
accepted:
09
05
2020
pubmed:
15
5
2020
medline:
31
10
2020
entrez:
15
5
2020
Statut:
ppublish
Résumé
Coronaviruses are enveloped RNA viruses from the Coronaviridae family affecting neurological, gastrointestinal, hepatic and respiratory systems. In late 2019 a new member of this family belonging to the Betacoronavirus genera (referred to as COVID-19) originated and spread quickly across the world calling for strict containment plans and policies. In most countries in the world, the outbreak of the disease has been serious and the number of confirmed COVID-19 cases has increased daily, while, fortunately the recovered COVID-19 cases have also increased. Clearly, forecasting the "confirmed" and "recovered" COVID-19 cases helps planning to control the disease and plan for utilization of health care resources. Time series models based on statistical methodology are useful to model time-indexed data and for forecasting. Autoregressive time series models based on two-piece scale mixture normal distributions, called
Identifiants
pubmed: 32405266
doi: 10.1016/j.tmaid.2020.101742
pii: 101742
pmc: PMC7219401
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
101742Informations de copyright
© 2020 Elsevier Ltd. All rights reserved.
Déclaration de conflit d'intérêts
The authors declare no conflict of interest.
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