National lockdowns in England: The same restrictions for all, but do the impacts on COVID-19 mortality risks vary geographically?

Muegge, Robin; Dean, Nema; Jack, Eilidh; Lee, Duncan

doi:10.1016/j.sste.2022.100559

Cited by 6 publications

(1 citation statement)

References 38 publications

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“…During the COVID-19 pandemic, the UK government introduced various measures, such as the tiered system, the localise and nationwide lockdowns to limit the spread of COVID-19. Our observation window encompasses three separate periods of national lockdown in England, which were officially announced and took place from 26 March to 12 May 2020, 5 November to 2 December 2020, and 5 January to 28 March 2021, respectively 37 . To aid in visualising these periods, they have been shaded in pink in the figure.…”

Section: Methodsmentioning

confidence: 99%

A Bayesian spatio-temporal model of COVID-19 spread in England

Yin,

Aiken,

Harris

et al. 2024

Sci Rep

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Exploring the spatio-temporal variations of COVID-19 transmission and its potential determinants could provide a deeper understanding of the dynamics of disease spread. This study aimed to investigate the spatio-temporal spread of COVID-19 infections in England, and examine its associations with socioeconomic, demographic and environmental risk factors. We obtained weekly reported COVID-19 cases from 7 March 2020 to 26 March 2022 at Middle Layer Super Output Area (MSOA) level in mainland England from publicly available datasets. With these data, we conducted an ecological study to predict the COVID-19 infection risk and identify its associations with socioeconomic, demographic and environmental risk factors using a Bayesian hierarchical spatio-temporal model. The Bayesian model outperformed the ordinary least squares model and geographically weighted regression model in terms of prediction accuracy. The spread of COVID-19 infections over space and time was heterogeneous. Hotspots of infection risk exhibited inconsistent clustering patterns over time. Risk factors found to be positively associated with COVID-19 infection risk were: annual household income [relative risk (RR) = 1.0008, 95% Credible Interval (CI) 1.0005–1.0012], unemployment rate [RR = 1.0027, 95% CI 1.0024–1.0030], population density on the log scale [RR = 1.0146, 95% CI 1.0129–1.0164], percentage of Caribbean population [RR = 1.0022, 95% CI 1.0009–1.0036], percentage of adults aged 45–64 years old [RR = 1.0031, 95% CI 1.0024–1.0039], and particulate matter ($$\text {PM}_{2.5}$$ PM 2.5 ) concentrations [RR = 1.0126, 95% CI 1.0083–1.0167]. The study highlights the importance of considering socioeconomic, demographic, and environmental factors in analysing the spatio-temporal variations of COVID-19 infections in England. The findings could assist policymakers in developing tailored public health interventions at a localised level.

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Section: Methodsmentioning

confidence: 99%

A Bayesian spatio-temporal model of COVID-19 spread in England

Yin,

Aiken,

Harris

et al. 2024

Sci Rep

View full text Add to dashboard Cite

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How do temperature and precipitation drive dengue transmission in nine cities, in Guangdong Province, China: a Bayesian spatio-temporal model analysis

Quan

Zhang

Deng

et al. 2023

Air Qual Atmos Health

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Some lessons learned and still to be learned about the role of forests and urban green areas during the COVID-19 pandemic

Bonanno,

Veneziano

2023

Discov Sustain

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Forests and green areas proved to be crucial and unexpected allies in the struggle against the COVID-19 pandemic. Indeed, urban and suburban green areas acted as propelling centers of public health during the most severe periods of lockdown. The exploitation of forested areas also contributed to mitigating the economic impact of job losses during the pandemic. This study specifically focused on the lessons that still need to be learned and suggested possible strategies for strengthening the lessons learned from COVID-19. Based on our investigation, we found that the use of urban green areas is significantly dependent on cultural and socioeconomic factors, and the increased interest in suburban green areas suggests a generally insufficient level of urban green spaces. Moreover, the exploitation of forests favored greater illegal deforestation in economically depressed countries, which led to biodiversity disruption and the further impoverishment of natural wood resources. First, as many citizens as possible, across different age, gender, cultural and socioeconomic conditions, should be encouraged to maintain the healthy habits that they developed during the pandemic. This may be accomplished by making cities more inclusive through the promotion of societal values, social cohesion and awareness campaigns. People should be encouraged to benefit from suburban green areas through increased connectivity between cities and their surrounding natural zones. Moreover, the promotion of the sustainable use of forested lands will contribute to not only ecological but also economic recovery through the creation of green jobs. Therefore, the greatest lesson of the COVID-19 pandemic is something that we have always known: equity is the guiding principle for a more resilient world.

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National lockdowns in England: The same restrictions for all, but do the impacts on COVID-19 mortality risks vary geographically?

Cited by 6 publications

References 38 publications

A Bayesian spatio-temporal model of COVID-19 spread in England

A Bayesian spatio-temporal model of COVID-19 spread in England

How do temperature and precipitation drive dengue transmission in nine cities, in Guangdong Province, China: a Bayesian spatio-temporal model analysis

Some lessons learned and still to be learned about the role of forests and urban green areas during the COVID-19 pandemic

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