A predictive model of the temperature-dependent inactivation of coronaviruses

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dc.citation.articleNumber060601en_US
dc.citation.issueNumber6en_US
dc.citation.journalTitleApplied Physics Lettersen_US
dc.citation.volumeNumber117en_US
dc.contributor.authorYap, Te Fayeen_US
dc.contributor.authorLiu, Zhenen_US
dc.contributor.authorShveda, Rachel A.en_US
dc.contributor.authorPreston, Daniel J.en_US
dc.date.accessioned2020-10-09T14:32:06Zen_US
dc.date.available2020-10-09T14:32:06Zen_US
dc.date.issued2020en_US
dc.description.abstractThe COVID-19 pandemic has stressed healthcare systems and supply lines, forcing medical doctors to risk infection by decontaminating and reusing single-use personal protective equipment. The uncertain future of the pandemic is compounded by limited data on the ability of the responsible virus, SARS-CoV-2, to survive across various climates, preventing epidemiologists from accurately modeling its spread. However, a detailed thermodynamic analysis of experimental data on the inactivation of SARS-CoV-2 and related coronaviruses can enable a fundamental understanding of their thermal degradation that will help model the COVID-19 pandemic and mitigate future outbreaks. This work introduces a thermodynamic model that synthesizes existing data into an analytical framework built on first principles, including the rate law for a first-order reaction and the Arrhenius equation, to accurately predict the temperature-dependent inactivation of coronaviruses. The model provides much-needed thermal decontamination guidelines for personal protective equipment, including masks. For example, at 70 °C, a 3-log (99.9%) reduction in virus concentration can be achieved, on average, in 3 min (under the same conditions, a more conservative decontamination time of 39 min represents the upper limit of a 95% interval) and can be performed in most home ovens without reducing the efficacy of typical N95 masks as shown in recent experimental reports. This model will also allow for epidemiologists to incorporate the lifetime of SARS-CoV-2 as a continuous function of environmental temperature into models forecasting the spread of the pandemic across different climates and seasons.en_US
dc.identifier.citationYap, Te Faye, Liu, Zhen, Shveda, Rachel A., et al.. "A predictive model of the temperature-dependent inactivation of coronaviruses." <i>Applied Physics Letters,</i> 117, no. 6 (2020) AIP: https://doi.org/10.1063/5.0020782.en_US
dc.identifier.doihttps://doi.org/10.1063/5.0020782en_US
dc.identifier.urihttps://hdl.handle.net/1911/109402en_US
dc.language.isoengen_US
dc.publisherAIPen_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.titleA predictive model of the temperature-dependent inactivation of coronavirusesen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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