Temperature and population density influence SARS-CoV-2 transmission in the absence of nonpharmaceutical interventions
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Abstract
There is still much to be understood about the factors influencing the ecology and epidemiology of COVID-19. In particular, whether environmental variation is likely to drive seasonal changes in SARS-CoV-2 transmission dynamics is largely unknown. We investigate the effects of the environment on SARS-CoV-2 transmission rates across the United States and then incorporate the most important environmental parameters into an epidemiological model. We show that temperature and population density can be important factors in transmission but only in the absence of mobility-restricting policy measures, although particularly strong policy measures may be required to mitigate the highest population densities. Our findings improve our understanding of the drivers of COVID-19 transmission and highlight areas in which policy decisions can be proactive.
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SciScore for 10.1101/2020.09.12.20193250: (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 InvLogit is the inverse logit transformation applied to a series of hierarchically-nested terms (αk, , and ) multiplied by Google mobility data36 (Xt,m,k) with a weekly AR(2) autocorrelated error term for each state (e; see Unwin et al.35 for more details). Google mobilitysuggested: NoneResults from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:An important caveat to this, however, is the collinearity between temperature, absolute humidity, and to a lesser degree, UV levels. The …
SciScore for 10.1101/2020.09.12.20193250: (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 InvLogit is the inverse logit transformation applied to a series of hierarchically-nested terms (αk, , and ) multiplied by Google mobility data36 (Xt,m,k) with a weekly AR(2) autocorrelated error term for each state (e; see Unwin et al.35 for more details). Google mobilitysuggested: NoneResults from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:An important caveat to this, however, is the collinearity between temperature, absolute humidity, and to a lesser degree, UV levels. The strong correlations between these environmental drivers mean that we are unable to discern the effects of each in a single model and therefore we focus on temperature as the most reliable environmental predictor. After accounting for the effect of population density on transmission (table 1), temperature’s effect is striking (figure 2). We also tested the effects of our predictor variables on Rt for times where strict lockdown measures were in place. When these mobility restrictions are in place, we observe no significant effects of temperature on Rt, i.e. the effects of lockdown dampen any environmental effects so as to make them inconsequential (figure 1b; supplementary table S3). Furthermore, under lockdown conditions the overall transmission rates are vastly reduced. Through our epidemiological modelling approach we are able to account for these effects (as mobility changes are explicitly incorporated), and find that higher population densities and lower temperatures drive increased Rt. Moreover, the formulation of our epidemiological model ensures that under high mobility reductions, changes in environment have little effect on Rt, mirroring our regression findings (see Methods and supplementary figure S2). The precise physiological mechanisms for temperature-dependant inactivation in SARS-CoV-2 are still not known, but animal models fo...
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 10.1101/2020.09.12.20193250: (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
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
An important caveat to this, however, is the collinearity between temperature, absolute humidity, and to a lesser degree, UV levels. The strong correlations between these environmental drivers mean that we are unable to discern the effects of each in a single model and therefore we focus on temperature as the most reliable environmental predictor. After accounting for the effect of population density on transmission (table 1), …
SciScore for 10.1101/2020.09.12.20193250: (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
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
An important caveat to this, however, is the collinearity between temperature, absolute humidity, and to a lesser degree, UV levels. The strong correlations between these environmental drivers mean that we are unable to discern the effects of each in a single model and therefore we focus on temperature as the most reliable environmental predictor. After accounting for the effect of population density on transmission (table 1), temperature’s effect is striking (figure 2). We also tested the effects of our predictor variables on Rt for times where strict lockdown measures were in place. When these mobility restrictions are in place, we observe no significant effects of temperature on Rt , i.e. the effects of lockdown dampen any environmental effects so as to make them inconsequential (figure 1b; supplementary table S3). Furthermore, under lockdown conditions the overall transmission rates are vastly reduced. Through our epidemiological modelling approach we are able to account for these effects (as mobility changes are explicitly incorporated), and find that higher population densities and lower temperatures drive increased Rt . Moreover, the formula- tion of our epidemiological model ensures that under high mobility reductions, changes in environment have little effect on Rt , mirroring our regression findings (see Methods and supplementary figure S2). The precise physiological mechanisms for temperature-dependant inactivation in SARS- CoV-2 are still not known, but animal mod...
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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