The role of ambient parameters on transmission rates of the COVID-19 outbreak: A machine learning model.
BACKGROUND: In recent years the relationship between ambient air temperature and the prevalence of viral infection has been under investigation. OBJECTIVE: The study was aimed at providing the statistical and machine learning-based analysis to investigate the influence of climatic factors on frequen...
| Publicado en: | Work Vol. 70; no. 2; pp. 377 - 386 |
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| Autores principales: | , , , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=153409441&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 153409441 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10519815 3RC jtl: Work issn: 10519815 maglogo: N pubinfo: dt: 2021 vid: 70 iid: 2 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 153409441 153409441 153409441 10.3233/WOR-210463 153409441 ppf: 377 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: The role of ambient parameters on transmission rates of the COVID-19 outbreak: A machine learning model. aug: au: Jamshidnezhad, Amir Hosseini, Seyed Ahmad Ghavamabadi, Leila Ibrahimi Marashi, Seyed Mahdi Hossaeini Mousavi, Hediye Zilae, Marzieh Dehaghi, Behzad Fouladi affil: Department of Health Information Technology, School of Allied Medical Sciences, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran sug: subj: COVID-19 Transmission COVID-19 Pandemic Iran Machine Learning Climate Human Iran Neural Networks (Computer) Environment Temperature Humidity Funding Source Pearson's Correlation Coefficient Multiple Regression Data Analysis Software ab: BACKGROUND: In recent years the relationship between ambient air temperature and the prevalence of viral infection has been under investigation. OBJECTIVE: The study was aimed at providing the statistical and machine learning-based analysis to investigate the influence of climatic factors on frequency of COVID-19 confirmed cases in Iran. METHOD: The data of confirmed cases of COVID-19 and some climatic factors related to 31 provinces of Iran between 04/03/2020 and 05/05/2020 was gathered from official resources. In order to investigate the important climatic factors on the frequency of confirmed cases of COVID-19 in all studied cities, a model based on an artificial neural network (ANN) was developed. RESULTS: The proposed ANN model showed accuracy rates of 87.25%and 86.4%in the training and testing stage, respectively, for classification of COVID-19 confirmed cases. The results showed that in the city of Ahvaz, despite the increase in temperature, the coefficient of determination R2 has been increasing. CONCLUSION: This study clearly showed that, with increasing outdoor temperature, the use of air conditioning systems to set a comfort zone temperature is unavoidable. Thus, the number of positive cases of COVID-19 increases. Also, this study shows the role of closed-air cycle condition in the indoor environment of tropical cities. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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