tetano
Editor, Senior Moderator
Spat Spatiotemporal Epidemiol
. 2025 Feb:52:100699.
doi: 10.1016/j.sste.2024.100699. Epub 2024 Nov 26. Association between urban green space and transmission of COVID-19 in Oslo, Norway: A Bayesian SIR modeling approach
Halvor Kjellesvig[SUP] 1 [/SUP], Suleman Atique[SUP] 2 [/SUP], Lars Böcker[SUP] 3 [/SUP], Geir Aamodt[SUP] 2 [/SUP]
Affiliations
Background: Access to green spaces can provide opportunities for physical activities and social interactions in urban areas during times with strict social distancing. In particular COVID-19 transmission is reduced in ventilated areas. During several waves of the pandemic, this study explores the association between access to urban green spaces and COVID-19 transmission at the district level in Norway's capital, Oslo.
Methods: We used daily numbers of confirmed laboratory PCR tests on district levels reported from the second to the fifth wave of the COVID-19 pandemic, from October 15, 2020 to April 15, 2022 in Oslo. We included the population's access to urban green spaces using two objective measurements: percentage of green area (%Ga) and vegetation cover (NDVI) using 300 and 1000 m buffers. The socio-demographic variables percentage of low-income population, average life expectancy and population density were also included. A Bayesian Susceptible-Infected-Removed (SIR) model was used to take advantage of the daily updated data on COVID-19 incidence and account for spatial and temporal dependencies in the statistical analysis.
Results: We found that low income as well as population density were significantly associated with incidence of COVID-19, but for the second and third waves only. For the second wave, a one percent increase in the proportion with low income at district level increased the risk of COVID-19 by 7 % (95 % CI: 3 % - 11 %) We did not find associations between access to green space and incidence rate for any of the buffer sizes. The second and third waves were more governed by socio-demographic factors than the fourth and fifth wave.
Conclusions: Incidence rate of COVID-19 was not associated with access to green space, but to the socio-demographic variables; income, population density, and life expectancy. Access to green space is equally distributed among districts in Oslo which may explain our findings.
Keywords: Bayesian; COVID-19; Green space; NIMBLE; Natural environment; SARS-CoV-2.
. 2025 Feb:52:100699.
doi: 10.1016/j.sste.2024.100699. Epub 2024 Nov 26. Association between urban green space and transmission of COVID-19 in Oslo, Norway: A Bayesian SIR modeling approach
Halvor Kjellesvig[SUP] 1 [/SUP], Suleman Atique[SUP] 2 [/SUP], Lars Böcker[SUP] 3 [/SUP], Geir Aamodt[SUP] 2 [/SUP]
Affiliations
- PMID: 39955122
- DOI: 10.1016/j.sste.2024.100699
Background: Access to green spaces can provide opportunities for physical activities and social interactions in urban areas during times with strict social distancing. In particular COVID-19 transmission is reduced in ventilated areas. During several waves of the pandemic, this study explores the association between access to urban green spaces and COVID-19 transmission at the district level in Norway's capital, Oslo.
Methods: We used daily numbers of confirmed laboratory PCR tests on district levels reported from the second to the fifth wave of the COVID-19 pandemic, from October 15, 2020 to April 15, 2022 in Oslo. We included the population's access to urban green spaces using two objective measurements: percentage of green area (%Ga) and vegetation cover (NDVI) using 300 and 1000 m buffers. The socio-demographic variables percentage of low-income population, average life expectancy and population density were also included. A Bayesian Susceptible-Infected-Removed (SIR) model was used to take advantage of the daily updated data on COVID-19 incidence and account for spatial and temporal dependencies in the statistical analysis.
Results: We found that low income as well as population density were significantly associated with incidence of COVID-19, but for the second and third waves only. For the second wave, a one percent increase in the proportion with low income at district level increased the risk of COVID-19 by 7 % (95 % CI: 3 % - 11 %) We did not find associations between access to green space and incidence rate for any of the buffer sizes. The second and third waves were more governed by socio-demographic factors than the fourth and fifth wave.
Conclusions: Incidence rate of COVID-19 was not associated with access to green space, but to the socio-demographic variables; income, population density, and life expectancy. Access to green space is equally distributed among districts in Oslo which may explain our findings.
Keywords: Bayesian; COVID-19; Green space; NIMBLE; Natural environment; SARS-CoV-2.