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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

revised on 02.07.2020, 18:13 and posted on 03.07.2020, 06:04 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.


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


Email Address of Submitting Author


Rice University


United States

ORCID For Submitting Author


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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