Tags

Type your tag names separated by a space and hit enter

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. 2020; 15(7):e0236238.Plos

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.

Links

PMC Free PDF

Authors+Show Affiliations

Pourghasemi HR
Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, Iran.
Pouyan S
Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, Iran.
Farajzadeh Z
Department of Agricultural Economics, College of Agriculture, Shiraz University, Shiraz, Iran.
Sadhasivam N
Department of Geography, School of Earth Science, Bharathidasan University, Tiruchirappalli, Tamil Nadu, India.
Heidari B
Department of Plant Production and Genetics, School of Agriculture, Shiraz University, Shiraz, Iran.
Babaei S
Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, Iran.
Tiefenbacher JP
Department of Geography, Texas State University, San Marcos, Texas, United States of America.

MeSH

AlgorithmsCOVID-19Communicable Disease ControlCoronavirus InfectionsDisease OutbreaksGeographic Information SystemsHumansIranMachine LearningModels, BiologicalPandemicsPneumonia, ViralRegression AnalysisRisk AssessmentRisk FactorsSupport Vector Machine

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

32722716

Citation

Pourghasemi, Hamid Reza, et al. "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, vol. 15, no. 7, 2020, pp. e0236238.
Pourghasemi HR, Pouyan S, Farajzadeh Z, et al. 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. 2020;15(7):e0236238.
Pourghasemi, 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. https://doi.org/10.1371/journal.pone.0236238
Pourghasemi HR, et al. 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. 2020;15(7):e0236238. PubMed PMID: 32722716.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Assessment of the outbreak risk, mapping and infection behavior of COVID-19: Application of the autoregressive integrated-moving average (ARIMA) and polynomial models. AU - Pourghasemi,Hamid Reza, AU - Pouyan,Soheila, AU - Farajzadeh,Zakariya, AU - Sadhasivam,Nitheshnirmal, AU - Heidari,Bahram, AU - Babaei,Sedigheh, AU - Tiefenbacher,John P, Y1 - 2020/07/28/ PY - 2020/04/15/received PY - 2020/07/01/accepted PY - 2020/7/30/entrez PY - 2020/7/30/pubmed PY - 2020/8/4/medline SP - e0236238 EP - e0236238 JF - PloS one JO - PLoS One VL - 15 IS - 7 N2 - 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. SN - 1932-6203 UR - https://www.unboundmedicine.com/medline/citation/32722716/Assessment_of_the_outbreak_risk_mapping_and_infection_behavior_of_COVID_19:_Application_of_the_autoregressive_integrated_moving_average__ARIMA__and_polynomial_models_ DB - PRIME DP - Unbound Medicine ER -