A Predictive Model of the Temperature-Dependent Inactivation of Coronaviruses
Te Faye Yap, Zhen Liu, Rachel A. Shveda, Daniel J. Preston
Received Date: 1st May 20
The COVID-19 pandemic has stressed healthcare systems and supply lines, forcing medical doctors to risk infection by decontaminating and reusing medical personal protective equipment intended only for a single use. 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 mitigate the COVID-19 pandemic and future outbreaks. This paper introduces a thermodynamic model that synthesizes existing data into an analytical framework built on first principles, including the Arrhenius equation and the rate law, to accurately predict the temperature-dependent inactivation of coronaviruses. The model provides much-needed thermal sterilization guidelines for personal protective equipment, including masks, and will also allow 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.
Read in full at ChemRxiv.
This is an abstract of a preprint hosted on an independent third party site. It has not been peer reviewed but is currently under consideration at Nature Communications.