About: Disease management with ARIMA model in time series

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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	Disease management with ARIMA model in time series
Creator	Sato, Renato
topic	covid:1b8a085682d10dad4baf90417a8cbcde144cc8c9#this
Source	Medline; PMC
abstract	The evaluation of infectious and noninfectious disease management can be done through the use of a time series analysis. In this study, we expect to measure the results and prevent intervention effects on the disease. Clinical studies have benefited from the use of these techniques, particularly for the wide applicability of the ARIMA model. This study briefly presents the process of using the ARIMA model. This analytical tool offers a great contribution for researchers and healthcare managers in the evaluation of healthcare interventions in specific populations.
has issue date	2013-01-01(xsd:dateTime)
bibo:doi	10.1590/s1679-45082013000100024
bibo:pmid	23579758
has license	cc-by
sha1sum (hex)	1b8a085682d10dad4baf90417a8cbcde144cc8c9
schema:url	https://doi.org/10.1590/s1679-45082013000100024
resource representing a document's title	Disease management with ARIMA model in time series
has PubMed Central identifier	PMC4872983
has PubMed identifier	23579758
schema:publication	Einstein (Sao Paulo)
resource representing a document's body	covid:1b8a085682d10dad4baf90417a8cbcde144cc8c9#body_text
is http://vocab.deri.ie/void#inDataset of	proxy:http/ns.inria.fr/covid19/1b8a085682d10dad4baf90417a8cbcde144cc8c9
is schema:about of	named entity 'prevent' named entity 'ARIMA' named entity 'INTERVENTIONS' named entity 'HAVE' named entity 'RESEARCHERS' named entity 'TECHNIQUES' named entity 'CLINICAL STUDIES' named entity 'DISEASE' named entity 'GREAT' named entity 'EVALUATION' named entity 'TOOL' named entity 'ANALYTICAL' named entity 'USING' named entity 'EFFECTS' named entity 'PROCESS' named entity 'NONINFECTIOUS' named entity 'PREVENT' named entity 'PRESENTS' named entity 'MODEL' named entity 'AUTHOR' named entity 'MANAGERS' covid:arg/1b8a085682d10dad4baf90417a8cbcde144cc8c9 named entity 'RUA' named entity 'WIDE' named entity 'ZIP CODE' named entity 'SATO' named entity 'NAIR' named entity 'VILA' named entity 'TO MEASURE' named entity 'ARIMA' named entity 'INFECTIOUS' named entity 'DISEASE MANAGEMENT' named entity 'RESULTS' named entity 'TIME SERIES ANALYSIS' named entity 'STUDY' named entity 'USE OF' named entity '330' named entity 'POPULATIONS' named entity 'INTERVENTION' named entity 'HEALTHCARE' named entity 'SPECIFIC' named entity 'CONTRIBUTION' named entity 'THESE' named entity 'researchers' named entity 'process' named entity 'tool' named entity 'Sato' named entity 'Clinical studies' named entity 'time series analysis' named entity 'analytical tool' named entity 'Zip code' named entity 'severe acute respiratory syndrome' named entity 'autoregressive' named entity 'ARIMA' named entity 'Gwilym Jenkins' named entity 'time series analysis' named entity 'ARIMA' named entity 'kidney' named entity 'ARIMA' named entity 'statistically significant' named entity 'white noise' named entity 'antimicrobial resistance' named entity 'white noise' named entity 'George Box' named entity 'schematic diagram' named entity 'emergency department' named entity 'PACF' named entity 'time series' named entity 'time series' named entity 'Time series' named entity 'ARIMA' named entity 'infectious disease' named entity 'epidemic' named entity 'electronic medical records' named entity 'healthcare organization' named entity 'autocorrelation function' named entity 'epidemic' named entity 'time series' named entity 'time series'

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