Causal empirical estimates suggest COVID-19 transmission rates are highly seasonal
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Abstract
Nearly every country is now combating the 2019 novel coronavirus (COVID-19). It has been hypothesized that if COVID-19 exhibits seasonality, changing temperatures in the coming months will shift transmission patterns around the world. Such projections, however, require an estimate of the relationship between COVID-19 and temperature at a global scale, and one that isolates the role of temperature from confounding factors, such as public health capacity. This paper provides the first plausibly causal estimates of the relationship between COVID-19 transmission and local temperature using a global sample comprising of 166,686 confirmed new COVID-19 cases from 134 countries from January 22, 2020 to March 15, 2020. We find robust statistical evidence that a 1°C increase in local temperature reduces transmission by 13% [−21%, −4%, 95%CI]. In contrast, we do not find that specific humidity or precipitation influence transmission. Our statistical approach separates effects of climate variation on COVID-19 transmission from other potentially correlated factors, such as differences in public health responses across countries and heterogeneous population densities. Using constructions of expected seasonal temperatures, we project that changing temperatures between March 2020 and July 2020 will cause COVID-19 transmission to fall by 43% on average for Northern Hemisphere countries and to rise by 71% on average for Southern Hemisphere countries. However, these patterns reverse as the boreal winter approaches, with seasonal temperatures in January 2021 increasing average COVID-19 transmission by 59% relative to March 2020 in northern countries and lowering transmission by 2% in southern countries. These findings suggest that Southern Hemisphere countries should expect greater transmission in the coming months. Moreover, Northern Hemisphere countries face a crucial window of opportunity: if contagion-containing policy interventions can dramatically reduce COVID-19 cases with the aid of the approaching warmer months, it may be possible to avoid a second wave of COVID-19 next winter.
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SciScore for 10.1101/2020.03.26.20044420: (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: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our study has a number of important limitations. First, as is true in any empirical study of disease, we can only observe cases that are confirmed. It is very likely that confirmed cases of COVID-19 fall far below actual rates of infection,24 thus suggesting that our findings may represent an under-estimate of the magnitude of the link …
SciScore for 10.1101/2020.03.26.20044420: (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: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our study has a number of important limitations. First, as is true in any empirical study of disease, we can only observe cases that are confirmed. It is very likely that confirmed cases of COVID-19 fall far below actual rates of infection,24 thus suggesting that our findings may represent an under-estimate of the magnitude of the link between infection and local climatic conditions. Relatedly, countries around the world have invested very differently in testing capacity, making such under-reporting heterogeneous across space and time. However, our empirical model is designed to purge estimates of the influence of such differential testing by using a rich set of semi-parametric controls, including fixed effects that vary across both space and time. Second, the estimates we show represent average treatment effects across countries and time periods in our sample. It is possible that the effect of temperature on case rates varies across different policy regimes, such as social distancing and the closure of public transportation systems. While such heterogeneity is an important area for future research, it is important to note that our model, by construction, captures many sources of such heterogeneity through its log-linear form. While we find that temperature has a consistent −13% per 1°C effect on the rate of new cases in a given population, this translates into very different levels of cases in different locations. If public health policies successfully lower baseline COVID-1...
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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