Dataset Information

UV, ozone, and COVID-19 transmission in Ontario, Canada using generalised linear models.

ABSTRACT:

Background

Quantifying the impact of environmental factors on COVID-19 transmission is crucial in preventing more cases. Ultraviolet (UV) radiation and ozone (O₃) have reported antimicrobial properties but few studies have examined associations with community infectivity of COVID-19. Research suggests UV light can be preventative while the effect of O₃ is contested. We sought to determine the relationship between UV, O₃, and COVID-19 incidence in Ontario, Canada.

Methods

In our time series analyses, we calculated daily incidence rates and reproductive number (R_t) from 34,975 cases between January and June 2020 across 34 Ontario Public Health Units. We used generalised linear models, adjusting for potential confounders, to calculate point estimates (PE) and 95% confidence intervals (CI) for UV and O₃. Analyses were further stratified by age groups and outbreaks at institutions versus community.

Results

We found that 1-week averaged UV was significantly associated with a 13% decrease (95% CI: 0.80-0.96) in overall COVID-19 R_t, per unit increase. A negative association with UV was also significant among community outbreaks (PE: 0.88, 95% CI: 0.81-0.96) but not institutional outbreaks (PE: 0.94, 95% CI: 0.85-1.03). A positive association of O₃ with COVID-19 incidence is strongly suggested among institutional outbreak cases (PE: 1.06, 95% CI: 1.00-1.13).

Conclusion

Our study found evidence to support the hypothesis that higher UV reduced transmission of COVID-19 and some evidence that ground-level O₃ positively influenced COVID-19 transmission. Setting of infection should be strongly considered as a factor in future research. UV and O₃ may explain some of COVID-19's seasonal behaviour.

SUBMITTER: To T

PROVIDER: S-EPMC7787508 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Publications

UV, ozone, and COVID-19 transmission in Ontario, Canada using generalised linear models.

To Teresa T Zhang Kimball K Maguire Bryan B Terebessy Emilie E Fong Ivy I Parikh Supriya S Zhu Jingqin J Su Yushan Y

Environmental research 20210106

<h4>Background</h4>Quantifying the impact of environmental factors on COVID-19 transmission is crucial in preventing more cases. Ultraviolet (UV) radiation and ozone (O<sub>3</sub>) have reported antimicrobial properties but few studies have examined associations with community infectivity of COVID-19. Research suggests UV light can be preventative while the effect of O<sub>3</sub> is contested. We sought to determine the relationship between UV, O<sub>3</sub>, and COVID-19 incidence in Ontario, ...[more]

PMID: 33359457

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Project description:Introduction: This study aimed to produce community-level geo-spatial mapping of confirmed COVID-19 cases in Ontario Canada in near real-time to support decision-making. This was accomplished by area-to-area geostatistical analysis, space-time integration, and spatial interpolation of COVID-19 positive individuals. Methods: COVID-19 cases and locations were curated for geostatistical analyses from March 2020 through June 2021, corresponding to the first, second, and third waves of infections. Daily cases were aggregated according to designated forward sortation area (FSA), and postal codes (PC) in municipal regions Hamilton, Kitchener/Waterloo, London, Ottawa, Toronto, and Windsor/Essex county. Hotspots were identified with area-to-area tests including Getis-Ord Gi*, Global Moran's I spatial autocorrelation, and Local Moran's I asymmetric clustering and outlier analyses. Case counts were also interpolated across geographic regions by Empirical Bayesian Kriging, which localizes high concentrations of COVID-19 positive tests, independent of FSA or PC boundaries. The Geostatistical Disease Epidemiology Toolbox, which is freely-available software, automates the identification of these regions and produces digital maps for public health professionals to assist in pandemic management of contact tracing and distribution of other resources. Results: This study provided indicators in real-time of likely, community-level disease transmission through innovative geospatial analyses of COVID-19 incidence data. Municipal and provincial results were validated by comparisons with known outbreaks at long-term care and other high density residences and on farms. PC-level analyses revealed hotspots at higher geospatial resolution than public reports of FSAs, and often sooner. Results of different tests and kriging were compared to determine consistency among hotspot assignments. Concurrent or consecutive hotspots in close proximity suggested potential community transmission of COVID-19 from cluster and outlier analysis of neighboring PCs and by kriging. Results were also stratified by population based-categories (sex, age, and presence/absence of comorbidities). Conclusions: Earlier recognition of hotspots could reduce public health burdens of COVID-19 and expedite contact tracing.

Dataset Information

UV, ozone, and COVID-19 transmission in Ontario, Canada using generalised linear models.

Background

Methods

Results

Conclusion

Publications

UV, ozone, and COVID-19 transmission in Ontario, Canada using generalised linear models.

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