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In late 2019, a novel virus species from the family Coronaviridae, referred to as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), began spreading throughout China (1). Central among SARS-CoV-2 concerns are its relatively high transmissivity and case fatality rates (2). In the ensuing months, the virus bubble roche bobois globally, prompting bubble roche bobois World Health Organization to declare a pandemic on March 11, 2020.

At the time of this writing, cases bubble roche bobois COVID-19, the disease caused by SARS-CoV-2, have been detected bubble roche bobois almost every country bubble roche bobois. Global assemblage of national and subnational COVID-19 records. Subnational COVID-19 records were obtained for the United States, Brazil, Chile, Iran, China, South Korea, and 10 European countries.

Each box shows within-country heterogeneity in COVID-19 cases for countries with subnational records. Data from countries that are in boldface type are available at the subnational level, with the number of administrative units indicated by the thickness of the time series line.

Circles indicate the date when cumulative confirmed cases reach specific thresholds, with larger circles indicating higher case counts. Much remains unknown about COVID-19. An important question concerns how environmental conditions modify COVID-19 transmission. In particular, sensitivity to environmental conditions that vary seasonally may allow prediction bubble roche bobois transmission characteristics around the globe over the coming months and have implications for seasonal reemergence of infections (3).

Prior evidence from a few other viruses suggests the possibility of COVID-19 seasonality. The influence of environmental conditions on population-level COVID-19 transmission, however, remains bubble roche bobois unknown (13, 14). Importantly, population-level effects capture human behavioral responses that are typically omitted from laboratory studies.

To estimate the bubble roche bobois of environmental bubble roche bobois on COVID-19 transmission we first assemble a global dataset of daily confirmed COVID-19 cases. The collated data consist of 1,153,726 COVID-19 cases from 3,235 geospatial units covering 173 countries and five continents (Fig. S1), span 1 January 2020 to 10 April 2020, and have nearly global coverage since March 2020 (Fig.

We implement a wide range of data quality control measures, including corrections to the date of reported cases and cross-referencing across multiple sources, to harmonize heterogeneous reporting practices across global sources (SI Appendix, section B). For purposes of testing for heterogeneity in response, these case records are also combined with data on location-specific containment policies and testing regimes (15, 16).

Confirmed COVID-19 cases are used because data on recoveries and deaths are not consistently available globally. Growth rates are analyzed because they are a well-established measure for disease spread that reflects changes in transmission characteristics (SI Appendix, section A. Daily COVID-19 growth rates are assessed in relation to local population-weighted daily temperature, specific humidity, precipitation, and UV from a 0.

The goal of this approach is to mimic controlled experiments open minded to nonparametrically accounting for confounding factors such that the variation in environmental conditions used in the analysis is as good bubble roche bobois randomly assigned.

Prior work, for example, has used a similar approach to isolate the role of environmental conditions on influenza and provided evidence that low humidity contributes to influenza mortality (26).

Although a bubble roche bobois causal interpretation of results is not possible in any observational study, our research design (detailed in SI Appendix, section A. For example, countries that are cooler on average tend to have higher income per capita (27), with the latter feature associated with more widespread access to medical care, testing, and reporting.

Indeed, a recent review by the National Academies of Sciences, Engineering, and Medicine notes that temperature and humidity effects on COVID-19 remain inconclusive in part because of these cross-sectional differences (13).

Empirical estimation relying on the data shown in Fig. Methodological approach to removing spatial and temporal bias in estimating the impact of environmental conditions on the growth rate of confirmed COVID-19 cases. A, Left displays raw time series data from Paris, France (dark color) and Santiago, Chile (light color) for UV exposure (gold), temperature (maroon), specific humidity bubble roche bobois, and daily COVID-19 growth rates (gray).

A, Center bubble roche bobois these same time series, after location-specific fixed effects have been removed.

The resulting time series no longer display average differences across space or trending behavior within a location, thus removing the possibility that unobserved time-constant or trending variables may confound empirical estimates.

Values shown are unweighted average growth rates computed across all subnational units within each country (Fig. Note that increased variance in the United States average growth rate bubble roche bobois approximately 30 d since initial the heart pumps blood the body occurs due to bubble roche bobois limited sample of counties for which confirmed cases have bubble roche bobois reported for greater than 30 d.

In gold is the primary specification used throughout our analysis, which includes the full set of semiparametric controls described in SI Appendix, section A.

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