tetano
Editor, Senior Moderator
Environ Res
. 2021 Mar 5;110977.
doi: 10.1016/j.envres.2021.110977. Online ahead of print.
Influence of Temperature, and of Relative and Absolute Humidity on COVID-19 Incidence in England - A Multi-City Time-Series Study
Luise N Nottmeyer[SUP] 1 [/SUP], Francesco Sera[SUP] 2 [/SUP]
Affiliations
Abstract
Background: SARS-CoV-2 caused the COVID-19 pandemic in 2020. The virus is likely to show seasonal dynamics in European climates as this is often the case for respiratory viruses and for coronaviruses. Analysing the association with meteorological factors will be helpful to anticipate how cases will develop with changing seasons.
Methods: Routinely measured ambient daily mean temperature, absolute humidity, and relative humidity were the explanatory variables. Test-positive COVID-19 cases represented the outcome variable. The analysis included 54 English cities. A two-stage meta-regression was conducted using a quasi-Poisson-distributed generalised linear modelling approach at first stage including distributed lag non-linear model elements. Thereby, the authors investigate the explanatory variables' non-linear effects as well as the non-linear effects across lags.
Results: This study found a non-linear association of COVID-19 cases with temperature. At 11.9?C there was 1.62-times (95%-CI: 1.44; 1.81) the risk of cases compared to the temperature-level with the smallest risk (21.8?C). Absolute humidity exhibited a 1.61-times (95%-CI: 1.41; 1.83) elevated risk at 6.6 g/m[SUP]3[/SUP] compared to the centering at 15.1 g/m[SUP]3[/SUP]. When adjusting for temperature RH shows a 1.41-fold increase in risk of COVID-19 incidence (95%-CI: 1.09; 1.81) at 60.7% in respect to 87.6%.
Conclusion: The analysis suggests that in England meteorological variables likely influence COVID-19 case development. These results reinforce the importance of non-pharmaceutical interventions (e.g., social distancing and mask use) during all seasons, especially with cold and dry weather conditions.
Keywords: COVID-19; Distributed Lag Non-linear Model; Humidity; Meta-analysis; Temperature.
. 2021 Mar 5;110977.
doi: 10.1016/j.envres.2021.110977. Online ahead of print.
Influence of Temperature, and of Relative and Absolute Humidity on COVID-19 Incidence in England - A Multi-City Time-Series Study
Luise N Nottmeyer[SUP] 1 [/SUP], Francesco Sera[SUP] 2 [/SUP]
Affiliations
- PMID: 33684415
- DOI: 10.1016/j.envres.2021.110977
Abstract
Background: SARS-CoV-2 caused the COVID-19 pandemic in 2020. The virus is likely to show seasonal dynamics in European climates as this is often the case for respiratory viruses and for coronaviruses. Analysing the association with meteorological factors will be helpful to anticipate how cases will develop with changing seasons.
Methods: Routinely measured ambient daily mean temperature, absolute humidity, and relative humidity were the explanatory variables. Test-positive COVID-19 cases represented the outcome variable. The analysis included 54 English cities. A two-stage meta-regression was conducted using a quasi-Poisson-distributed generalised linear modelling approach at first stage including distributed lag non-linear model elements. Thereby, the authors investigate the explanatory variables' non-linear effects as well as the non-linear effects across lags.
Results: This study found a non-linear association of COVID-19 cases with temperature. At 11.9?C there was 1.62-times (95%-CI: 1.44; 1.81) the risk of cases compared to the temperature-level with the smallest risk (21.8?C). Absolute humidity exhibited a 1.61-times (95%-CI: 1.41; 1.83) elevated risk at 6.6 g/m[SUP]3[/SUP] compared to the centering at 15.1 g/m[SUP]3[/SUP]. When adjusting for temperature RH shows a 1.41-fold increase in risk of COVID-19 incidence (95%-CI: 1.09; 1.81) at 60.7% in respect to 87.6%.
Conclusion: The analysis suggests that in England meteorological variables likely influence COVID-19 case development. These results reinforce the importance of non-pharmaceutical interventions (e.g., social distancing and mask use) during all seasons, especially with cold and dry weather conditions.
Keywords: COVID-19; Distributed Lag Non-linear Model; Humidity; Meta-analysis; Temperature.