About: Application of the ARIMA model on the COVID-2019 epidemic dataset

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About: Application of the ARIMA model on the COVID-2019 epidemic dataset 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	Application of the ARIMA model on the COVID-2019 epidemic dataset
Creator	Angeletti, Silvia Benvenuto, Domenico Ciccozzi, Massimo Giovanetti, Marta Vassallo, Lazzaro
Source	Elsevier; Medline; PMC
abstract	Coronavirus disease 2019 (COVID-2019) has been recognized as a global threat, and several studies are being conducted using various mathematical models to predict the probable evolution of this epidemic. These mathematical models based on various factors and analyses are subject to potential bias. Here, we propose a simple econometric model that could be useful to predict the spread of COVID-2019. We performed Auto Regressive Integrated Moving Average (ARIMA) model prediction on the Johns Hopkins epidemiological data to predict the epidemiological trend of the prevalence and incidence of COVID-2019. For further comparison or for future perspective, case definition and data collection have to be maintained in real time.
has issue date	2020-02-26(xsd:dateTime)
bibo:doi	10.1016/j.dib.2020.105340
bibo:pmid	32181302
has license	cc-by
sha1sum (hex)	69f32253d39ff23be2ba2ed03622505cf02fda48
schema:url	https://doi.org/10.1016/j.dib.2020.105340
resource representing a document's title	Application of the ARIMA model on the COVID-2019 epidemic dataset
has PubMed Central identifier	PMC7063124
has PubMed identifier	32181302
schema:publication	Data Brief
resource representing a document's body	covid:69f32253d39ff23be2ba2ed03622505cf02fda48#body_text
is schema:about of	named entity 'HERE' named entity 'EPIDEMIOLOGICAL' named entity 'VARIOUS' named entity 'SPREAD' named entity 'PROBABLE' named entity 'CORONAVIRUS DISEASE 2019' named entity 'COMPARISON' named entity 'TREND' named entity 'REAL TIME' named entity 'USEFUL' named entity 'DATA COLLECTION' named entity 'GLOBAL' named entity 'SIMPLE' named entity 'THESE' named entity 'POTENTIAL' named entity 'MODEL' named entity 'REGRESSIVE' named entity 'DATA' named entity 'probable' named entity 'predict' named entity '2019' named entity 'model' named entity '2019' named entity 'PERFORMED' named entity 'USING' named entity 'BEING' named entity 'FUTURE' named entity 'MOVING AVERAGE' named entity 'PREVALENCE' named entity 'ECONOMETRIC MODEL' named entity 'PERSPECTIVE' named entity 'MAINTAINED' named entity 'MATHEMATICAL MODELS' named entity 'HAVE' named entity 'HOPKINS' named entity 'MODEL' named entity 'DATASET' named entity 'EPIDEMIC' named entity 'ARIMA' named entity 'APPLICATION' named entity 'SUBJECT' named entity 'PREDICT' named entity 'AUTO' named entity 'INTEGRATED' named entity 'STUDIES' named entity 'CASE DEFINITION' named entity 'BASED' named entity 'THREAT' named entity 'EVOLUTION' named entity '2019' named entity 'PREDICTION' named entity 'BIAS' named entity 'EPIDEMIC' named entity 'COULD BE' named entity 'INCIDENCE' named entity 'factors' named entity 'ARIMA' named entity '2019' named entity 'prevalence' named entity 'model' named entity 'epidemic' named entity '2019' named entity 'Coronavirus disease 2019' named entity 'Moving Average' named entity 'mathematical models' named entity 'case definition' named entity 'ARIMA' named entity 'epidemic' named entity 'correlogram' named entity 'data collection' named entity 'ARIMA' named entity 'mathematical models' named entity 'COVID-2019' named entity 'autoregressive (AR) model' named entity 'COVID-2019' named entity 'ARIMA' named entity 'significance level'

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