The COVID-19 pandemic has led to an unprecedented shortage in single-use personal protective equipment (PPE), forcing healthcare workers to risk their lives by working without proper PPE or reusing single-use PPE while tending to multiple patients. Among the various decontamination methods reported in the literature, dry heat decontamination has been shown to effectively inactivate viruses, which could enable safe reuse of PPE and lower the risk of infection; however, detailed guidelines are not available, and thermal inactivation experiments on the responsible virus, SARS-CoV-2, are constrained to limited data points at temperatures that are not necessarily suitable for decontamination of delicate PPE. This lack of information on the lifetime of the virus as a function of temperature also hinders efforts by epidemiologists to understand its spread throughout different regions of the world. In this work, we applied a regression-based approach to aggregate existing experimental data in the literature on virus thermal inactivation into a thermodynamic framework built on first principles, including the rate law and Arrhenius equation, which we then used to model the lifetime of SARS-CoV-2 as a continuous function of temperature. This model can be used to provide dry heat decontamination guidelines for PPE at temperatures attainable by common household heating devices without reducing the efficacy of typical N95 masks. We show that the average time needed to achieve a 99.9% reduction in viable SARS-CoV-2 at 70 °C is 3 minutes (a conservative estimate at the same temperature, based on a 95% prediction interval, is 39 minutes). We expect that this model could also serve as an additional tool for epidemiologists to more accurately predict the lifetime of SARS-CoV-2 across climates and seasons based on a physical understanding of how temperature affects the inactivation rates of viruses.

Data-driven model of the thermal inactivation of SARS-CoV-2

ACS Fall 2021

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