About: Assessment of the outbreak risk, mapping and infection behavior of COVID-19: Application of the autoregressive integrated-moving average (ARIMA) and polynomial models

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About: Assessment of the outbreak risk, mapping and infection behavior of COVID-19: Application of the autoregressive integrated-moving average (ARIMA) and polynomial models Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Assessment of the outbreak risk, mapping and infection behavior of COVID-19: Application of the autoregressive integrated-moving average (ARIMA) and polynomial models
Creator	Reza, Hamid Farajzadeh, Zakariya Pouyan, Soheila Sadhasivam, Nitheshnirmal Tiefenbacher, John Id, Bahram Id, Pourghasemi Id, Sedigheh
Source	Medline; PMC
abstract	Infectious disease outbreaks pose a significant threat to human health worldwide. The outbreak of pandemic coronavirus disease 2019 (COVID-19) has caused a global health emergency. Thus, identification of regions with high risk for COVID-19 outbreak and analyzing the behaviour of the infection is a major priority of the governmental organizations and epidemiologists worldwide. The aims of the present study were to analyze the risk factors of coronavirus outbreak for identifying the areas having high risk of infection and to evaluate the behaviour of infection in Fars Province, Iran. A geographic information system (GIS)-based machine learning algorithm (MLA), support vector machine (SVM), was used for the assessment of the outbreak risk of COVID-19 in Fars Province, Iran whereas the daily observations of infected cases were tested in the—polynomial and the autoregressive integrated moving average (ARIMA) models to examine the patterns of virus infestation in the province and in Iran. The results of the disease outbreak in Iran were compared with the data for Iran and the world. Sixteen effective factors were selected for spatial modelling of outbreak risk. The validation outcome reveals that SVM achieved an AUC value of 0.786 (March 20), 0.799 (March 29), and 86.6 (April 10) that displays a good prediction of outbreak risk change detection. The results of the third-degree polynomial and ARIMA models in the province revealed an increasing trend with an evidence of turning, demonstrating extensive quarantines has been effective. The general trends of virus infestation in Iran and Fars Province were similar, although a more volatile growth of the infected cases is expected in the province. The results of this study might assist better programming COVID-19 disease prevention and control and gaining sorts of predictive capability would have wide-ranging benefits.
has issue date	2020-07-28(xsd:dateTime)
bibo:doi	10.1371/journal.pone.0236238
bibo:pmid	32722716
has license	cc0
sha1sum (hex)	8bcc7125f94c2bf952e70a9d2a03b8805ee20174
schema:url	https://doi.org/10.1371/journal.pone.0236238
resource representing a document's title	Assessment of the outbreak risk, mapping and infection behavior of COVID-19: Application of the autoregressive integrated-moving average (ARIMA) and polynomial models
has PubMed Central identifier	PMC7386644
has PubMed identifier	32722716
schema:publication	PLoS One
resource representing a document's body	covid:8bcc7125f94c2bf952e70a9d2a03b8805ee20174#body_text
is schema:about of	named entity 'infestation' named entity 'geographic' named entity 'support vector machine' named entity 'Fars Province' named entity 'models' named entity 'disease prevention' named entity 'extensive' named entity 'SVM' named entity 'expected' named entity 'worldwide' named entity 'polynomial' named entity 'behavior' named entity 'RISK FACTORS' named entity 'VALIDATION' named entity 'A MAJOR' named entity 'CAUSED' named entity 'PRESENT' named entity 'INFECTIOUS DISEASE' named entity 'SVM' named entity 'USED' named entity 'INFECTED' named entity 'VIRUS' named entity 'INTEGRATED' named entity 'virus' named entity 'identifying' named entity 'data' named entity 'Iran' named entity 'Iran' named entity 'system' named entity 'control' named entity 'programming' named entity 'caused' named entity 'governmental organizations' named entity 'effective' named entity 'prediction' named entity 'Iran' named entity 'behaviour' named entity 'change detection' named entity 'outbreak' named entity 'predictive' named entity 'models' named entity 'Assessment' named entity 'COVID-19' named entity 'infection' named entity 'Iran' named entity 'epidemiologists' named entity 'Iran' named entity 'machine learning algorithm' named entity 'March 29' named entity 'COVID' named entity 'March 20' named entity 'April 10' named entity 'Iran' named entity 'virus' named entity 'Iran' named entity 'Fars Province' named entity 'infection' named entity 'polynomial' named entity 'global health' named entity 'evidence' named entity 'Sixteen' named entity 'GWR' named entity 'PWM' named entity 'Iran' named entity 'United States' named entity 'COVID-19 pandemic' named entity 'Fars province' named entity 'support vector machine' named entity 'COVID-19 outbreak' named entity 'Infection' named entity 'Iran' named entity 'SARS-CoV-2' named entity 'Wuhan' named entity 'February 29'

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