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dc.contributor.authorHe, Junyu
dc.contributor.authorChen, Guangwei
dc.contributor.authorJiang, Yutong
dc.contributor.authorJin, Runjie
dc.contributor.authorShortridge, Ashton
dc.contributor.authorAgusti, Susana
dc.contributor.authorHe, Mingjun
dc.contributor.authorWu, Jiaping
dc.contributor.authorDuarte, Carlos M.
dc.contributor.authorChristakos, George
dc.date.accessioned2020-08-11T10:54:08Z
dc.date.available2020-08-11T10:54:08Z
dc.date.issued2020-08-03
dc.date.submitted2020-07-05
dc.identifier.citationHe, J., Chen, G., Jiang, Y., Jin, R., Shortridge, A., Agusti, S., … Christakos, G. (2020). Comparative infection modeling and control of COVID-19 transmission patterns in China, South Korea, Italy and Iran. Science of The Total Environment, 141447. doi:10.1016/j.scitotenv.2020.141447
dc.identifier.issn0048-9697
dc.identifier.doi10.1016/j.scitotenv.2020.141447
dc.identifier.urihttp://hdl.handle.net/10754/664546
dc.description.abstractThe COVID-19 has become a pandemic. The timing and nature of the COVID-19 pandemic response and control varied among the regions and from one country to the other, and their role in affecting the spread of the disease has been debated. The focus of this work is on the early phase of the disease when control measures can be most effective. We proposed a modified susceptible-exposed-infected-removed model (SEIR) model based on temporal moving windows to quantify COVID-19 transmission patterns and compare the temporal progress of disease spread in six representative regions worldwide: three Chinese regions (Zhejiang, Guangdong and Xinjiang) vs. three countries (South Korea, Italy and Iran). It was found that in the early phase of COVID-19 spread the disease follows a certain empirical law that is common in all regions considered. Simulations of the imposition of strong social distancing measures were used to evaluate the impact that these measures might have had on the duration and severity of COVID-19 outbreaks in the three countries. Measure-dependent transmission rates followed a modified normal distribution (empirical law) in the three Chinese regions. These rates responded quickly to the launch of the 1st-level Response to Major Public Health Emergency in each region, peaking after 1–2 days, reaching their inflection points after 10–19 days, and dropping to zero after 11–18 days since the 1st-level response was launched. By March 29th, the mortality rates were 0.08% (Zhejiang), 0.54% (Guangdong) and 3.95% (Xinjiang). Subsequent modeling simulations were based on the working assumption that similar infection transmission control measures were taken in South Korea as in Zhejiang on February 25th, in Italy as in Guangdong on February 25th, and in Iran as in Xinjiang on March 8th. The results showed that by June 15th the accumulated infection cases could have been reduced by 32.49% (South Korea), 98.16% (Italy) and 85.73% (Iran). The surface air temperature showed stronger association with transmission rate of COVID-19 than surface relative humidity. On the basis of these findings, disease control measures were shown to be particularly effective in flattening and shrinking the COVID-10 case curve, which could effectively reduce the severity of the disease and mitigate medical burden. The proposed empirical law and the SEIR-temporal moving window model can also be used to study infectious disease outbreaks worldwide.
dc.description.sponsorshipWe would like to thank Mr. Jimi He (The Chinese University of Hong Kong, Shenzhen) for his assistance with the R software coding. This work was supported in part by the National Science Foundation of China (41671399) and the Science and Technology Department of Zhejiang Province (2016C04004).
dc.publisherElsevier BV
dc.relation.urlhttps://linkinghub.elsevier.com/retrieve/pii/S0048969720349767
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Science of The Total Environment. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Science of The Total Environment, [747, , (2020-08-03)] DOI: 10.1016/j.scitotenv.2020.141447 . © 2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleComparative infection modeling and control of COVID-19 transmission patterns in China, South Korea, Italy and Iran
dc.typeArticle
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Division
dc.contributor.departmentMarine Science Program
dc.contributor.departmentRed Sea Research Center (RSRC)
dc.identifier.journalScience of The Total Environment
dc.rights.embargodate2022-08-06
dc.eprint.versionPost-print
dc.contributor.institutionOcean College, Zhejiang University, Zhoushan, China
dc.contributor.institutionOcean Academy, Zhejiang University, Zhoushan, China
dc.contributor.institutionDepartment of Geography, Environment and Spatial Sciences, Michigan State University, East Lansing, U.S.A
dc.contributor.institutionDepartment of Geography, San Diego State University, San Diego, USA
dc.identifier.volume747
dc.identifier.pages141447
kaust.personAgusti, Susana
kaust.personDuarte, Carlos M.
dc.date.accepted2020-08-01
dc.identifier.eid2-s2.0-85088949348


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