About: A non-central beta model to forecast and evaluate pandemics time series

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About: A non-central beta model to forecast and evaluate pandemics time series 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	A non-central beta model to forecast and evaluate pandemics time series
Creator	Gonçalves Júnior, Jucier Araújo Da Silva, Taciana Firmino, Alves Paulino De Sales, Jair Renato, Paulo
Source	Elsevier; PMC
abstract	Government, researchers, and health professionals have been challenged to model, forecast, and evaluate pandemics time series (e.g. new coronavirus SARS-CoV-2, COVID-19). The main difficulty is the level of novelty imposed by these phenomena. Information from previous epidemics is only partially relevant. Further, the spread is local-dependent, reflecting a number of social, political, economic, and environmental dynamic factors. The present paper aims to provide a relatively simple way to model, forecast, and evaluate the time incidence of a pandemic. The proposed framework makes use of the non-central beta (NCB) probability density function. Specifically, a probabilistic optimisation algorithm searches for the best NCB model of the pandemic, according to the mean square error metric. The resulting model allows one to infer, among others, the general peak date, the ending date, and the total number of cases as well as to compare the level of difficult imposed by the pandemic among territories. Case studies involving COVID-19 incidence time series from countries around the world suggest the usefulness of the proposed framework in comparison with some of the main epidemic models from the literature (e.g. SIR, SIS, SEIR) and established time series formalisms (e.g. exponential smoothing - ETS, autoregressive integrated moving average - ARIMA).
has issue date	2020-08-23(xsd:dateTime)
bibo:doi	10.1016/j.chaos.2020.110211
has license	no-cc
sha1sum (hex)	1c35f6ce3f00db0e8249c129124d5ba86588127a
schema:url	https://doi.org/10.1016/j.chaos.2020.110211
resource representing a document's title	A non-central beta model to forecast and evaluate pandemics time series
has PubMed Central identifier	PMC7443326
schema:publication	Chaos Solitons Fractals
resource representing a document's body	covid:1c35f6ce3f00db0e8249c129124d5ba86588127a#body_text
is schema:about of	named entity 'literature' named entity 'simple' named entity 'researchers' named entity 'beta' named entity 'non-central' named entity 'LIMITED' named entity 'SARS-COV-2' named entity 'EXPONENTIAL' named entity 'general' named entity 'environmental' named entity 'novelty' named entity 'cases' named entity 'pandemic' named entity 'dynamic' named entity 'NCB' named entity 'development' named entity 'reflecting' named entity 'SARS-CoV-2' named entity 'time series forecasting' named entity 'artificial neural networks' named entity 'Iran' named entity 'time series' named entity 'United Kingdom' named entity 'skewness' named entity 'Argentina' named entity 'Germany' named entity 'Johns Hopkins University' named entity 'epidemic' named entity 'Johns Hopkins university' named entity 'India' named entity 'Italy' named entity 'time series' named entity 'Intel i7' named entity 'time series' named entity 'ARIMA' named entity 'infection' named entity 'India' named entity 'time series' named entity 'Spanish flu' named entity 'COVID' named entity 'COVID' named entity 'pneumonia' named entity 'China' named entity 'data sets' named entity 'United Kingdom' named entity 'time series' named entity 'Brazil' named entity 'COVID-19' named entity 'infection' named entity 'death rate' named entity 'COVID' named entity 'Argentina' named entity 'Relative Variance' named entity 'United Kingdom' named entity 'negative skew' named entity 'Korea' named entity 'SARS' named entity 'COVID' named entity 'Chikungunya' named entity 'SEIR' named entity 'kurtosis' named entity 'COVID' named entity 'Korea' named entity 'time series' named entity 'probability density functions' named entity 'COVID-19' named entity 'sustainable development' named entity 'coronavirus' named entity 'COVID' named entity 'Germany' named entity 'time series' named entity 'epidemic' named entity 'measles' named entity 'neuro-fuzzy' named entity 'COVID-19' named entity 'COVID' named entity 'dashed' named entity 'Italy' named entity 'France'

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