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A_Predictive_Model_of_the_Temperature-Dependent_Inactivation_of_Coronaviruses_v3.pdf (1.63 MB)

A Predictive Model of the Temperature-Dependent Inactivation of Coronaviruses

preprint
revised on 02.07.2020 and posted on 03.07.2020 by Te Faye Yap, Zhen Liu, Rachel A. Shveda, Daniel Preston
The COVID-19 pandemic has stressed healthcare systems and supply lines, forcing medical doctors to risk infection by decontaminating and reusing single-use medical 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 paper introduces a thermodynamic model that synthesizes existing data into an analytical framework built on first principles, including the rate law 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 in ≈ 3 minutes and can be performed in most home ovens without reducing the efficacy of typical N95 masks. The 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 coronaviruses across different climates and seasons.

Funding

This work was supported by the National Science Foundation under grant CBET-2030023.

History

Email Address of Submitting Author

djp@rice.edu

Institution

Rice University

Country

United States

ORCID For Submitting Author

0000-0002-0096-0285

Declaration of Conflict of Interest

The authors declare no competing financial interest.

Version Notes

Under consideration at Applied Physics Letters. Version 3 submitted 23 June 2020; added uncertainty analysis to predictions of virus lifetime. Version 2 submitted 1 May 2020; added data for SARS-CoV-2 (Chin 2020, van Doremalen 2020) and SARS-CoV-1 (van Doremalen 2020). Version 1 submitted 19 April 2020.

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