Mechanistic theory predicts the effects of temperature and humidity on inactivation of SARS-CoV-2 and other enveloped viruses
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Abstract
Environmental conditions affect virus inactivation rate and transmission potential. Understanding those effects is critical for anticipating and mitigating epidemic spread. Ambient temperature and humidity strongly affect the inactivation rate of enveloped viruses, but a mechanistic, quantitative theory of those effects has been elusive. We measure the stability of the enveloped respiratory virus SARS-CoV-2 on an inert surface at nine temperature and humidity conditions and develop a mechanistic model to explain and predict how temperature and humidity alter virus inactivation. We find SARS-CoV-2 survives longest at low temperatures and extreme relative humidities; median estimated virus half-life is over 24 hours at 10 °C and 40 % RH, but approximately 1.5 hours at 27 °C and 65 % RH. Our mechanistic model uses simple chemistry to explain the increase in virus inactivation rate with increased temperature and the U-shaped dependence of inactivation rate on relative humidity. The model accurately predicts quantitative measurements from existing studies of five different human coronaviruses (including SARS-CoV-2), suggesting that shared mechanisms may determine environmental stability for many enveloped viruses. Our results indicate scenarios of particular transmission risk, point to pandemic mitigation strategies, and open new frontiers in the mechanistic study of virus transmission.
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SciScore for 10.1101/2020.10.16.341883: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Visualization: We created plots in R version using ggplot2 [61], ggdist [30], and tidybayes [31], and created original schematics using BioRender.com. ggplot2suggested: (ggplot2, RRID:SCR_014601)BioRendersuggested: (Biorender, RRID:SCR_018361)Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not …
SciScore for 10.1101/2020.10.16.341883: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Visualization: We created plots in R version using ggplot2 [61], ggdist [30], and tidybayes [31], and created original schematics using BioRender.com. ggplot2suggested: (ggplot2, RRID:SCR_014601)BioRendersuggested: (Biorender, RRID:SCR_018361)Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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SciScore for 10.1101/2020.10.16.341883: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Experimental Models: Cell Lines Sentences Resources We quantified viable virus by end-point titration on Vero E6 cells as described previously11,51 , and inferred posterior distributions for titers and exponential decay rates directly from raw titration data using Bayesian statistical models (see Statistical analyses and mathematical modelling below). Vero E6suggested: NoneSoftware and Algorithms Sentences Resources Visualization We created plots in R using ggplot256 , ggdist57 , and tidybayes58 , and created original … SciScore for 10.1101/2020.10.16.341883: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Experimental Models: Cell Lines Sentences Resources We quantified viable virus by end-point titration on Vero E6 cells as described previously11,51 , and inferred posterior distributions for titers and exponential decay rates directly from raw titration data using Bayesian statistical models (see Statistical analyses and mathematical modelling below). Vero E6suggested: NoneSoftware and Algorithms Sentences Resources Visualization We created plots in R using ggplot256 , ggdist57 , and tidybayes58 , and created original schematics using BioRender.com. BioRendersuggested: (Biorender, RRID:SCR_018361)Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
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