On the Environmental Determinants of COVID‐19 Seasonality
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Abstract
Viral respiratory diseases (VRDs), such as influenza and COVID‐19, are thought to spread faster during winter than during summer. It has been previously argued that cold and dry conditions are more conducive to the transmission of VRDs than warm and humid climates, although this relationship appears restricted to temperate regions and the causal relationship is not well understood. The severe acute respiratory syndrome coronavirus 2 causing COVID‐19 has emerged as a serious global public health problem after the first COVID‐19 reports in Wuhan, China, in late 2019. It is still unclear whether this novel respiratory disease will ultimately prove to be a seasonal endemic disease. Here, we suggest that air drying capacity (ADC; an atmospheric state variable that controls the fate/evolution of the virus‐laden droplets) and ultraviolet radiation (UV) are probable environmental determinants in shaping the transmission of COVID‐19 at the seasonal time scale. These variables, unlike temperature and humidity, provide a physically based framework consistent with the apparent seasonal variability in COVID‐19 and prevalent across a broad range of climates (e.g., Germany and India). Since this disease is known to be influenced by the compounding effect of social, biological, and environmental determinants, this study does not claim that these environmental determinants exclusively shape the seasonality of COVID‐19. However, we argue that ADC and UV play a significant role in COVID‐19 dynamics at the seasonal scale. These findings could help guide the development of a sound adaptation strategy against the pandemic over the coming seasons.
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SciScore for 10.1101/2021.03.01.21252243: (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:Before concluding, it is important to mention that this study naturally presents several important caveats. First, as for any COVID-19-related study, the quality, extensiveness and uniformity of the data is subject to caution. COVID-19 case data is strongly impacted by testing rates and policies, which differ in space (between different …
SciScore for 10.1101/2021.03.01.21252243: (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:Before concluding, it is important to mention that this study naturally presents several important caveats. First, as for any COVID-19-related study, the quality, extensiveness and uniformity of the data is subject to caution. COVID-19 case data is strongly impacted by testing rates and policies, which differ in space (between different countries) but also in time, since testing protocols have evolved over time. Although this study applied the threshold of at least 10,000 cumulative COVID-19 tests per 1 million people to discard countries with unrepresentative data, but considering all countries without quality control may provide other conclusions that are inherent with high uncertainty. Influenza data also suffers from the same biases, which are inevitable in any global study. Additionally, the spread of COVID-19 is largely shaped by policy measures, such as social distancing, mask mandates, school closures, and event cancellations (Bherwani et al., 2020; Poirier et al., 2020). Finally, we did not consider the effects of indoor environments (e.g., heating and air conditioning), which may also influence the seasonality of VRD (e.g., Xie et al., 2007; Shaman and Kohn, 2009). As always in such cases, one must be careful in concluding about the role of environmental variables in shaping VRD dynamics (Carlson et al., 2020; Zaitchik et al., 2020). Even though vaccines have recently been developed and are currently being administered, and though it is hoped that SARS-Cov-2 will so...
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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