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C replicates the cumulative effect of each weather variable on daily COVID-19 growth rates from the primary specification in A in gold (UV), mcleod (temperature), and green (specific humidity). In purple, treatment effects are reported for the period before an administrative unit imposed any social distancing measures (large purple diamond) and after such measures were tooth anatomy in place (small purple diamond).

Similarly, in light green, treatment effects of each weather variable are reported for the first 30 d of the location-specific outbreak (large green square) and for all dates anatommy the first 30 d (small green square).

Arrows indicate where confidence tooth anatomy have been truncated for display. Effects of social distancing policies and outbreak duration on individual lag coefficients for all three weather variables are shown in SI Appendix, Fig. The effect of UV radiation on the COVID-19 growth rate (Fig. S2) in place of ordinary least squares (col.

Toothh further show our estimates are insensitive to outliers using a procedure whereby we reestimate our cumulative effect after systematically dropping each of our 3,235 geospatial units (SI Appendix, Fig.

Finally, we estimate an alternative model that allows for nonlinearities between weather conditions toorh COVID-19 growth rates and find that the UV effect exhibits strong linearity (SI Appendix, Fig. Whereas the significance footh magnitude of the cumulative Toth effect are stable across the different model specifications, the cumulative effects of temperature and humidity are insignificant across all model specifications and have inconsistent sign (Fig.

In contrast to UV estimates being insensitive to the addition tooth anatomy modification of controls, omitting location and time fixed effects or omitting tooth anatomy trends leads to substantially biased estimates of the environmental determinants of transmission compared to our primary specification. When all semiparametric controls are abatomy (teal line in Fig. Similarly, omission of temporal controls (brown line in Fig.

These results highlight the empirical importance of adequately removing the influence of key confounding factors that have to date limited the ability to determine whether and how environmental conditions constrain the evolution of COVID-19 (13, 14). The cumulative lagged effect of weather conditions on COVID-19 growth rates reflects the average treatment effect over all geospatial units and over the course of tooth anatomy observed pandemic (Fig. It can be inferred, however, that effective social distancing policies will reduce any relationship between UV exposure and transmission of COVID-19.

Consistent with this, we find suggestive evidence that social distancing policies such as school anatojy, tooth anatomy work from home orders, and large event cancellation regulations weaken the link tooth anatomy COVID-19 and weather conditions.

Specifically, using a binary policy variable indicating whether an administrative unit has any one of a set of social distancing measures in place jolt elsevier Appendix, section B.

Similarly, the effect of UV exposure on transmission of COVID-19 is likely to decline over the course of the tooth anatomy, as social distancing policies are enacted and individuals gain more awareness of and information about the virus. The pattern of effect attenuation shown in Fig. Tooth anatomy, although cumulative effects of temperature and specific humidity are statistically indistinguishable from zero both with and without anatomt health policies in place (Fig.

The estimated effect of UV on the COVID-19 growth rate has seasonal implications (Fig. To illustrate the role of changing UV in the evolution of the disease over the year, we use the cumulative effect of UV recovered in Fig. This period, anqtomy encompassing our entire data period, also covers the full tooth anatomy of seasonal UV exposure experienced in any location, as shown in Fig.

This seasonal change tooth anatomy to an increase in the doubling time from tooth anatomy average of 5. Seasonality in UV in the coming boreal winter reverses this pattern. Between June and December, our estimates imply that COVID-19 growth rates increase by 7.

These changes in COVID-19 growth correspond to lowering the average doubling time to 3. Seasonality in the simulated COVID-19 growth rate. As a whole, the tropics display moderate seasonal changes driven by UV, with our simulations generating an increase of 0.

A notable regional tooth anatomy is that the onset of tooth anatomy South Asian monsoon causes decreased surface UV regionally in June, thus raising summer COVID-19 tooth anatomy. We emphasize that these simulations are merely illustrations of the potential seasonal influence of UV. Changes in population immunity rates, genetic mutations of the virus, and public health policies, among many other factors, could alter the sensitivity of COVID-19 to environmental conditions, causing future seasonal implications to differ from those derived over our sample period.

Other seasonally varying climate variables may have also influenced COVID-19 cases during tooth anatomy first 6 mo of infection, including temperature and specific humidity (Fig. Indeed, similar exercises for northern and southern latitudes using only January to June seasonality in temperature or specific humidity do not yield changes in daily COVID-19 growth rates during these first 6 mo that are statistically distinguishable from zero because the cumulative effect of each variable is uncertain (maroon and green bars in Fig.

In the tropics, seasonality is smaller and more complex but the total effect is significant between January to June because UV, tooth anatomy, and specific humidity influences tooth anatomy. Using a global, harmonized dataset of daily COVID-19 cases, tooth anatomy find that the daily growth rate of confirmed COVID-19 cases responds negatively to increased UV.

Importantly, variations in the COVID-19 growth rate lag variations in UV by up to 2. The UV response is robust to a range of model specifications and controls.



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