Efficacy and self-similarity of SARS-CoV-2 thermal decontamination.

Dry heat decontamination has been shown to effectively inactivate viruses without compromising the integrity of delicate personal protective equipment (PPE), allowing safe reuse and helping to alleviate shortages of PPE that have arisen due to COVID-19. Unfortunately, current thermal decontamination guidelines rely on empirical data which are often sparse, limited to a specific virus, and unable to provide fundamental insight into the underlying inactivation reaction. In this work, we experimentally quantified dry heat decontamination of SARS-CoV-2 on disposable masks and validated a model that treats the inactivation reaction as thermal degradation of macromolecules. Furthermore, upon nondimensionalization, all of the experimental data collapse onto a unified curve, revealing that the thermally driven decontamination process exhibits self-similar behavior. Our results show that heating surgical masks to 70 °C for 5 min inactivates over 99.9% of SARS-CoV-2. We also characterized the chemical and physical properties of disposable masks after heat treatment and did not observe degradation. The model presented in this work enables extrapolation of results beyond specific temperatures to provide guidelines for safe PPE decontamination. The modeling framework and self-similar behavior are expected to extend to most viruses-including yet-unencountered novel viruses-while accounting for a range of environmental conditions.

Effect of daily temperature fluctuations on virus lifetime.

Epidemiological studies based on statistical methods indicate inverse correlations between virus lifetime and both (i) daily mean temperature and (ii) diurnal temperature range (DTR). While thermodynamic models have been used to predict the effect of constant-temperature surroundings on virus inactivation rate, the relationship between virus lifetime and DTR has not been explained using first principles. Here, we model the inactivation of viruses based on temperature-dependent chemical kinetics with a time-varying temperature profile to account for the daily mean temperature and DTR simultaneously. The exponential Arrhenius relationship governing the rate of virus inactivation causes fluctuations above the daily mean temperature during daytime to increase the instantaneous rate of inactivation by a much greater magnitude than the corresponding decrease in inactivation rate during nighttime. This asymmetric behavior results in shorter predicted virus lifetimes when considering DTR and consequently reveals a potential physical mechanism for the inverse correlation observed between the number of cases and DTR reported in statistical epidemiological studies. In light of the ongoing COVID-19 pandemic, a case study on the effect of daily mean temperature and DTR on the lifetime of SARS-CoV-2 was performed for the five most populous cities in the United States. In Los Angeles, where mean monthly temperature fluctuations are low (DTR ≈ 7 °C), accounting for DTR decreases predicted SARS-CoV-2 lifetimes by only 10%; conversely, accounting for DTR for a similar mean temperature but larger mean monthly temperature fluctuations in Phoenix (DTR ≈ 15 °C) decreases predicted lifetimes by 50%. The modeling framework presented here provides insight into the independent effects of mean temperature and DTR on virus lifetime, and a significant impact on transmission rate is expected, especially for viruses that pose a high risk of fomite-mediated transmission.

A predictive model of the temperature-dependent inactivation of coronaviruses.

The 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.