Show simple item record

dc.contributor.authorPourghasemi, Hamid Reza ( Orcid Icon 0000-0003-2328-2998 )
dc.contributor.authorPouyan, Soheila ( )
dc.contributor.authorFarajzadeh, Zakariya ( Orcid Icon 0000-0002-5971-947X )
dc.contributor.authorSadhasivam, Nitheshnirmal ( )
dc.contributor.authorHeidari, Bahram ( Orcid Icon 0000-0002-5856-4592 )
dc.contributor.authorBabaei, Sedigheh ( Orcid Icon 0000-0002-4138-0658 )
dc.contributor.authorTiefenbacher, John ( Orcid Icon 0000-0001-9342-6550 )
dc.identifier.citationPourghasemi, H. R., Pouyan, S., Farajzadeh, Z., Sadhasivam, N., Heidari, B., Babaei, S., & Tiefenbacher, J. P. (2020). Assessment of the outbreak risk, mapping and infection behavior of COVID-19: Application of the autoregressive integrated-moving average (ARIMA) and polynomial models. PLoS One, 15(7), e0236238.en_US
dc.description.abstractInfectious 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.en_US
dc.format.extent25 pages
dc.format.medium1 file (.pdf)
dc.publisherPublic Library of Scienceen_US
dc.sourcePLoS One, 2020, Vol. 15, No. 7, Article e0236238.
dc.subjectMedical risk factorsen_US
dc.subjectSARS CoV 2en_US
dc.subjectSupport vector machinesen_US
dc.titleAssessment of the outbreak risk, mapping and infection behavior of COVID-19: Application of the autoregressive integrated-moving average (ARIMA) and polynomial modelsen_US

Public Domain Mark
This work is made available under the Creative Commons CC0 public domain dedication.




This item appears in the following Collection(s)

Show simple item record