City-level greenness exposure is associated with COVID-19 incidence in China

Residential greenness, genetic susceptibility, and asthma risk: Mediating roles of air pollution in UK and Chinese populations

BACKGROUND

The relationship between residential greenness and asthma remains a topic of interest, especially in understanding the pathways involved and how genetic factors might influence this association. This study aimed to explore the association between residential greenness and asthma incidence, while also examining potential mediating pathways and the role of genetic susceptibility.

METHODS

Data were analyzed from two independent cohorts: the UK Biobank and the Chinese Biomarkers for the Prediction of Respiratory Disease Outcomes (C-BIOPRED) study. Greenness was measured by normalized difference vegetation index (NDVI). Polygenic risk scores were constructed from 145 asthma-associated single nucleotide polymorphisms. Cox proportional hazard models and logistics regression models were used to assess the association between residential greenness and asthma incidence, and mediation analysis was conducted to explore potential mediators.

RESULTS

Over a median follow-up of 11.85 years in UK Biobank, higher NDVI exposure was associated with reduced asthma incidence (hazard ratio per IQR increase in NDVI300 m: 0.965, 95 % CI: 0.949-0.982). The association was more pronounced among non-smokers and individuals with highest genetic risk. PM2.5 mediated 40.4 % (95 % CI: 5.1 %-76.4 %) of the protective effect. In the C-BIOPRED study, greenness was inversely associated with severe asthma (odd ratio: 0.645, 95 % CI: 0.441-0.943) and improved clinical outcomes.

CONCLUSION

Residential greenness is associated with a lower risk of asthma, particularly in genetically susceptible and socioeconomically disadvantaged populations, partially through improving air quality. Our findings advocate for integrating green space optimization into urban planning as a precision public health strategy.

The influence of weather and urban environment characteristics on upper respiratory tract infections: a systematic review

Background

Weather can independently affect the occurrence of respiratory tract infections (RTIs) in urban areas. Built environments of cities could further modify exposure to weather and consequently the risk of RTIs, but their combined effects on infections are not known.

Objectives

Our aim was to synthesize evidence of the influence of weather on RTIs in urban areas and to examine whether urban built environments are associated with both weather and RTIs.

Methods

A systematic search of Scopus, PubMed, and Web of Science databases was conducted on 9th of August 2022 following PRISMA guidelines. Studies were included in the review based on predefined criteria by screening 5,789 articles and reviewing reference lists of relevant studies. The quality of the studies was assessed using the AXIS appraisal tool, and the results analyzed by narrative synthesis.

Results

Twenty-one eligible studies focusing on COVID-19 and influenza transmissions, were included in the review. All studies were register based ecological studies by design. Low temperature (11/19 studies) was most often associated with increased risk of RTI. Humidity showed either negative (5/14 studies), positive (3/14 studies) or no (6/14 studies) relation with RTIs. The association between wind and solar radiation on infections was inconclusive. Population density was positively associated with RTIs (14/15 studies).

Conclusions

Our review shows that exposure to low temperature increases the occurrence of RTIs in urban areas, and where also high population density increases the infection risk. The study highlights the need to further assess the relationship between built environment characteristics, weather, and RTIs.

Predicting 30-day readmissions in pneumonia patients using machine learning and residential greenness

Introduction

Identifying factors that increase the risk of hospital readmission will help determine high-risk patients and decrease the socioeconomic burden. Pneumonia is associated with high readmission rates. Although residential greenness has been reported to have beneficial health effects, no studies have investigated its importance in predicting readmission in patients with pneumonia. This study aimed to build prediction models for 30-day readmission in patients with pneumonia and to analyze the importance of risk factors for readmission, mainly residential greenness.

Methods

Data on 47 risk factors were collected from 22,600 patients diagnosed with pneumonia. Residential greenness was quantified as the mean of normalized difference vegetation index of the district in which the patient resides. Prediction models were built using logistic regression, support vector machine, random forest, and extreme gradient boosting.

Results

Residential greenness was selected from the top 21 risk factors after feature selection. The area under the curves of the four models were 0.6919, 0.6931, 0.7117, and 0.7044. Age, red blood cell distribution width, and history of cancer were the top three risk factors affecting readmission prediction. Residential greenness was the 15th important factor.

Discussion

We constructed prediction models for 30-day readmission of patients with pneumonia by incorporating residential greenness as a risk factor. The models demonstrated sufficient performance, and residential greenness was significant in predicting readmission. Incorporating residential greenness into the identification of groups at high risk for readmission can complement the possible loss of information when using data from electronic health records.

How did COVID-19 case distribution associate with the urban built environment? A community-level exploration in Shanghai focusing on non-linear relationship

Several associations between the built environment and COVID-19 case distribution have been identified in previous studies. However, few studies have explored the non-linear associations between the built environment and COVID-19 at the community level. This study employed the March 2022 Shanghai COVID-19 pandemic as a case study to examine the association between built-environment characteristics and the incidence of COVID-19. A non-linear modeling approach, namely the boosted regression tree model, was used to investigate this relationship. A multi-scale study was conducted at the community level based on buffers of 5-minute, 10-minute, and 15-minute walking distances. The main findings are as follows: (1) Relationships between built environment variables and COVID-19 case distribution vary across scales of analysis at the neighborhood level. (2) Significant non-linear associations exist between built-environment characteristics and COVID-19 case distribution at different scales. Population, housing price, normalized difference vegetation index, Shannon's diversity index, number of bus stops, floor-area ratio, and distance from the city center played important roles at different scales. These non-linear results provide a more refined reference for pandemic responses at different scales from an urban planning perspective and offer useful recommendations for a sustainable COVID-19 post-pandemic response.

A Global Meta-Analysis of the Effects of Greenspaces on COVID-19 Infection and Mortality Rates

The COVID-19 outbreak in 2020 resulted in rapidly rising infection rates with high associated mortality rates. In response, several epidemiological studies aimed to define ways in which the spread and severity of COVID-19 can be curbed. As a result, there is a steady increase in the evidence linking greenspaces and COVID-19 impact. However, the evidence of the benefits of greenspaces or greenness to human wellbeing in the context of COVID-19 is fragmented and sometimes contradictory. This calls for a meta-analysis of existing studies to clarify the matter. Here, we identified 621 studies across the world on the matter, which were then filtered down to 13 relevant studies for meta-analysis, covering Africa, Asia, Europe, and the USA. These studies were meta-analyzed, with the impacts of greenness on COVID-19 infection rate quantified using regression estimates whereas impacts on mortality rates were measured using mortality rate ratios. We found evidence of significant negative correlations between greenness and both COVID-19 infection and mortality rates. We further found that the impacts on COVID-19 infection and related mortality are moderated by year of publication, greenness metrics, sample size, health and political covariates. This clarification has far-reaching implications for policy development toward the establishment and management of green infrastructure for the benefit of human wellbeing.

Environmental and geographical factors influencing the spread of SARS-CoV-2 over 2 years: a fine-scale spatiotemporal analysis

Introduction

Since its emergence in late 2019, the SARS-CoV-2 virus has led to a global health crisis, affecting millions and reshaping societies and economies worldwide. Investigating the determinants of SARS-CoV-2 diffusion and their spatiotemporal dynamics at high spatial resolution is critical for public health and policymaking.

Methods

This study analyses 194,682 georeferenced SARS-CoV-2 RT-PCR tests from March 2020 and April 2022 in the canton of Vaud, Switzerland. We characterized five distinct pandemic periods using metrics of spatial and temporal clustering like inverse Shannon entropy, the Hoover index, Lloyd's index of mean crowding, and the modified space-time DBSCAN algorithm. We assessed the demographic, socioeconomic, and environmental factors contributing to cluster persistence during each period using eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP), to consider non-linear and spatial effects.

Results

Our findings reveal important variations in the spatial and temporal clustering of cases. Notably, areas with flatter epidemics had higher total attack rate. Air pollution emerged as a factor showing a consistent positive association with higher cluster persistence, substantiated by both immission models and, to a lesser extent, tropospheric NO2 estimations. Factors including population density, testing rates, and geographical coordinates, also showed important positive associations with higher cluster persistence. The socioeconomic index showed no significant contribution to cluster persistence, suggesting its limited role in the observed dynamics, which warrants further research.

Discussion

Overall, the determinants of cluster persistence remained across the study periods. These findings highlight the need for effective air quality management strategies to mitigate air pollution's adverse impacts on public health, particularly in the context of respiratory viral diseases like COVID-19.

Associations of residential greenness exposure and ambient air pollutants with newly-diagnosed drug-resistant tuberculosis cases

Growing evidence has found the health protective effects of greenness exposure on tuberculosis (TB) and the impact of ambient air pollutants on TB drug-resistance. However, it remains unclear whether residential greenness is also beneficial to reduce TB drug-resistance, and whether air pollution modify the greenness-TB resistance relationship. We enrolled 5006 newly-diagnosed TB patients from Shandong, China, during 2014 to 2021. Normalized Difference Vegetation Index (NDVI) in 250 m and 500 m buffer around individuals' residential zone was used to assess greenness exposure. All patients were divided by quartiles of NDVI250-m and NDVI500-m (from low to high: Q1, Q2, Q3, Q4) respectively. Six logistic regression models (NDVI, NDVI + PM2.5/PM10/SO2/NO2/O3) were used to estimate the association of NDVI and TB drug-resistance when adjusting different air pollutants or not. All models were adjusted for age, gender, body mass index, complications, smoking, drinking, population density, nighttime light index, road density. Compared with participants in NDVI250-m Q1 and NDVI500-m Q1, other groups had lower rates of MDR-TB, PDR-TB, RFP-resistance, SM-resistance, RFP + SM resistance, INH + RFP + EMB + SM resistance. NDVI500-m reduced the risk of multidrug resistant tuberculosis (MDR-TB) and the adjusted odds ratio (aOR, 95% confidence interval, CI) compared with NDVI500-m Q1 were 0.736 (0.547-0.991) in NDVI + PM10 model, 0.733 (0.544-0.986) in NDVI + PM2.5 model, 0.735(0.546-0.99) in NDVI + SO2 model, 0.736 (0.546-0.991) in NDVI + NO2 model, respectively, P < 0.05. NDVI500-m contributed to a decreased risk of streptomycin (SM)-resistance. The aOR of rifampicin (RFP) + SM resistance were 0.132 (NDVI250-m, Q4 vs Q1, 95% CI: 0.03-0.578), 0.199 (NDVI500-m, Q3 vs. Q1, 95% CI: 0.057-0.688) and 0.264 (NDVI500-m, Q4 vs. Q1, 95% CI: 0.087-0.799). The adjusted ORs (Q2 vs. Q1, 95% CI) of isoniazid (INH) + RFP + ethambutol (EMB) + SM resistance in 500 m buffer were 0.276 (0.119-0.639) in NDVI model, 0.279 (0.11-0.705) in NDVI + PM10 model, 0.281 (0.111-0.713) in NDVI + PM2.5 model, 0.279 (0.11-0.709) in NDVI + SO2 model, 0.296 (0.117-0.754) in NDVI + NO2 model, 0.294 (0.116-0.748) in NDVI + O3 model, respectively. The study showed, for the first time, that residential greenness exposure in 500 m buffer is beneficial for reducing newly-diagnosed DR-TB (including PDR-RB, MDR-TB, MR-TB), and ambient air pollutants may partially mediate this association.

How does multiscale greenspace exposure affect human health? Evidence from urban parks in the central city of Beijing

While the health benefits of exposure to urban greenspace have been widely discussed at different spatial scales, the comprehensive health effects of multiscale greenspace exposure are far from understood. There is a lack of quantified evidence when conducting cost-effective greenspace management practices for promoting human health and well-being. This study proposed a conceptual model that links objective and subjective greenspace exposure metrics at different spatial scales with self-rated health of residents. The model attempted to deconstruct and explore the associations between multiscale greenspace exposure and human health, and a cross-sectional study was conducted to examine the model. Taking urban parks in the central city of Beijing as case study area, the objective greenspace exposure metrics at both the site and subdistrict scales were spatial explicitly assessed, and the subjective exposure metrics and self-rated health status of 1017 respondents were obtained through questionnaire survey and spatial positioning. The results of multiple regression analyses and path analyses suggested that greenspace exposure metrics at both site and subdistrict scales were significantly associated with the respondents' self-rated health status, with the exposure metrics at the site scale being more important than those at the subdistrict scale in affecting human health. The contribution of urban parks to self-rated physical and mental health of respondents varied across spatial scales. Specifically, the aesthetic value of urban parks at site scale contributed the most to mental health by promoting respondents' resting behaviors in urban parks, and the density of urban parks at subdistrict scale had the most significant effects on self-rated physical health by increasing the usage frequency of urban parks. Findings of this study can contribute to understanding the complex associations between urban greenspace and human health from a multiscale perspective, and are also expected to provide quantified information for health-oriented urban greenspace planning and management practices.

Artificial intelligence analysis of over a million Chinese men and women reveals level of dark circle in the facial skin aging process

BACKGROUND

To better compare the progression of dark circles and the aging process in Chinese skin. A total of 100 589 Chinese males and 1 838 997 Chinese females aged 18 to 85, without facial skin conditions, and who had access to a smartphone with a high-resolution camera all took selfies.

METHOD

Using a smartphone application with a built-in artificial intelligence algorithm, facial skin diagnostic evaluated the selfies and score the severity of the dark circles with four other facial indicators (including skin type, Pores, Acne vulgaris, and Blackheads). Basic information was collected with online questionnaire, including their age, gender, skin sensitivity, and dietary habits.

RESULTS

In users between the age of 18 and 59, the prevalence of comprehensive, pigmented, and structural type of dark circles all rose with age. However, between the age of 60 and 85, the intensity of all types of dark circles diminished. Besides, vascular dark circles progressively worsen from the age of 18 to their peak at 39, and then gradually decline with age. Females typically have more pronounced black circles under their eyes than males in China. Bad eating habits, urbanization, regular cosmetics use, and sensitive skin positively correlate with severe dark circles. Vascular, comprehensive dark circles were worse in spring. Both pigmented and structural dark circles were worse in the summer. The results indicated that the intensity of dark circles was influenced by oily skin, wide pores, severe blackheads, and severe acne.

CONCLUSIONS

Chinese men and women differed noticeably in the prevalence of each face aging indicator and the appearance of aging dark circles. Selfies could be automatically graded and examined by artificial intelligence, which is a quick and private method for quantifying signs of facial aging and identifying major problems for different populations. Artificial intelligence would assist in the development of individualized preventive and therapeutic interventions.

Associations of air pollution and greenness with global burden of breast cancer: an ecological study

Despite the significance of the associations of air pollution and greenness with the risk of breast cancer, this topic has not been investigated on a global scale. We conducted an ecological study using 7 years of data from 162 countries. Disability-adjusted life years (DALYs) and incidence data were used to represent the breast cancer disease burden. Particulate matter with a diameter < 2.5 μm (PM2.5), ozone (O3), nitrogen dioxide (NO2), and the normalized difference vegetation index (NDVI) were adopted as our exposures. We employed generalized linear mixed models to explore the relationship between air pollution and greenness on breast cancer disease burden. The rate ratio (RR) and its 95% confidence interval (CI) indicate the effect size. There is a positive association between air pollution and the burden of breast cancer disease. Contrarily, per interquartile range increment in NDVI was negatively associated with DALYs and incidence. In terms of air pollutants and breast cancer, NDVI seems to have a significant influence on the relationship between these two conditions. A higher amount of greenness helps to alleviate the negative association of air pollution on breast cancer. PM2.5 and O3 play a mediating role in the relationship between greenness and breast cancer disease burden. In areas with higher levels of greenness, there is a possibility that the inverse association between air pollutants and the burden of breast cancer may be influenced.

Exploring the spatial pattern of community urban green spaces and COVID-19 risk in Wuhan based on a random forest model

Since 2019, COVID-19 has triggered a renewed investigation of the urban environment and disease outbreak. While the results have been inconsistent, it has been observed that the quantity of urban green spaces (UGS) is correlated with the risk of COVID-19. However, the spatial pattern has largely been ignored, especially on the community scale. In high-density communities where it is difficult to increase UGS quantity, UGS spatial pattern could be a crucial predictive variable. Thus, this study investigated the relative contribution of quantity and spatial patterns of UGS on COVID-19 risk at the community scale using a random forest (RF) regression model based on (n = 44) communities in Wuhan. Findings suggested that 8 UGS indicators can explain 35% of the risk of COVID-19, and the four spatial pattern metrics that contributed most were core, edge, loop, and branch whereas UGS quantity contributed least. The potential mechanisms between UGS and COVID-19 are discussed, including the influence of UGS on residents' social distance and environmental factors in the community. This study offers a new perspective on optimizing UGS for public health and sustainable city design to combat pandemics and inspire future research on the specific relationship between UGS spatial patterns and pandemics and therefore help establish mechanisms of UGS and pandemics.

Peculiar weather patterns effects on air pollution and COVID-19 spread in Tokyo metropolis

As a pandemic hotspot in Japan, between March 1, 2020-October 1, 2022, Tokyo metropolis experienced seven COVID-19 waves. Motivated by the high rate of COVID-19 incidence and mortality during the seventh wave, and environmental/health challenges we conducted a time-series analysis to investigate the long-term interaction of air quality and climate variability with viral pandemic in Tokyo. Through daily time series geospatial and observational air pollution/climate data, and COVID-19 incidence and death cases, this study compared the environmental conditions during COVID-19 multiwaves. In spite of five State of Emergency (SOEs) restrictions associated with COVID-19 pandemic, during (2020-2022) period air quality recorded low improvements relative to (2015-2019) average annual values, namely: Aerosol Optical Depth increased by 9.13% in 2020 year, and declined by 6.64% in 2021, and 12.03% in 2022; particulate matter PM2.5 and PM10 decreased during 2020, 2021, and 2022 years by 10.22%, 62.26%, 0.39%, and respectively by 4.42%, 3.95%, 5.76%. For (2021-2022) period the average ratio of PM2.5/PM10 was (0.319 ± 0.1640), showing a higher contribution to aerosol loading of traffic-related coarse particles in comparison with fine particles. The highest rates of the daily recorded COVID-19 incidence and death cases in Tokyo during the seventh COVID-19 wave (1 July 2022-1 October 2022) may be attributed to accumulation near the ground of high levels of air pollutants and viral pathogens due to: 1) peculiar persistent atmospheric anticyclonic circulation with strong positive anomalies of geopotential height at 500 hPa; 2) lower levels of Planetary Boundary Layer (PBL) heights; 3) high daily maximum air temperature and land surface temperature due to the prolonged heat waves (HWs) in summer 2022; 4) no imposed restrictions. Such findings can guide public decision-makers to design proper strategies to curb pandemics under persistent stable anticyclonic weather conditions and summer HWs in large metropolitan areas.

Associations between COVID-19 risk, multiple environmental exposures, and housing conditions: A study using individual-level GPS-based real-time sensing data

Few studies have used individual-level data to explore the association between COVID-19 risk with multiple environmental exposures and housing conditions. Using individual-level data collected with GPS-tracking smartphones, mobile air-pollutant and noise sensors, an activity-travel diary, and a questionnaire from two typical neighborhoods in a dense and well-developed city (i.e., Hong Kong), this study seeks to examine 1) the associations between multiple environmental exposures (i.e., different types of greenspace, PM_2.5, and noise) and housing conditions (i.e., housing types, ownership, and overcrowding) with individuals' COVID-19 risk both in residential neighborhoods and along daily mobility trajectories; 2) which social groups are disadvantaged in COVID-19 risk through the perspective of the neighborhood effect averaging problem (NEAP). Using separate multiple linear regression and logistical regression models, we found a significant negative association between COVID-19 risk with greenspace (i.e., NDVI) both in residential areas and along people's daily mobility trajectories. Meanwhile, we also found that high open space and recreational land exposure and poor housing conditions were positively associated with COVID-19 risk in high-risk neighborhoods, and noise exposure was positively associated with COVID-19 risk in low-risk neighborhoods. Further, people with work places in high-risk areas and poor housing conditions were disadvantaged in COVID-19 risk.

Effects of High Temperature on COVID-19 Deaths in U.S. Counties

The United States of America (USA) was afflicted by extreme heat in the summer of 2021 and some states experienced a record-hot or top-10 hottest summer. Meanwhile, the United States was also one of the countries impacted most by the coronavirus disease 2019 (COVID-19) pandemic. Growing numbers of studies have revealed that meteorological factors such as temperature may influence the number of confirmed COVID-19 cases and deaths. However, the associations between temperature and COVID-19 severity differ in various study areas and periods, especially in periods of high temperatures. Here we choose 119 US counties with large counts of COVID-19 deaths during the summer of 2021 to examine the relationship between COVID-19 deaths and temperature by applying a two-stage epidemiological analytical approach. We also calculate the years of life lost (YLL) owing to COVID-19 and the corresponding values attributable to high temperature exposure. The daily mean temperature is approximately positively correlated with COVID-19 deaths nationwide, with a relative risk of 1.108 (95% confidence interval: 1.046, 1.173) in the 90th percentile of the mean temperature distribution compared with the median temperature. In addition, 0.02 YLL per COVID-19 death attributable to high temperature are estimated at the national level, and distinct spatial variability from -0.10 to 0.08 years is observed in different states. Our results provide new evidence on the relationship between high temperature and COVID-19 deaths, which might help us to understand the underlying modulation of the COVID-19 pandemic by meteorological variables and to develop epidemic policy response strategies.

Associations between greenness and predicted COVID-19-like illness incidence in the United States and the United Kingdom

Green spaces may be protective against COVID-19 incidence. They may provide outdoor, ventilated, settings for physical and social activities and therefore decrease transmission risk. We examined the association between neighborhood greenness and COVID-19-like illness incidence using individual-level data.

Methods

The study population includes participants enrolled in the COVID Symptom Study smartphone application in the United Kingdom and the United States (March-November 2020). All participants were encouraged to report their current health condition and suspected risk factors for COVID-19. We used a validated symptom-based classifier that predicts COVID-19-like illness. We estimated the Normalized Difference Vegetation Index (NDVI), for each participant's reported neighborhood of residence for each month, using images from Landsat 8 (30 m2). We used time-varying Cox proportional hazards models stratified by age, country, and calendar month at study entry and adjusted for the individual- and neighborhood-level risk factors.

Results

We observed 143,340 cases of predicted COVID-19-like illness among 2,794,029 participants. Neighborhood NDVI was associated with a decreased risk of predicted COVID-19-like illness incidence in the fully adjusted model (hazard ratio = 0.965, 95% confidence interval = 0.960, 0.970, per 0.1 NDVI increase). Stratified analyses showed protective associations among U.K. participants but not among U.S. participants. Associations were slightly stronger for White individuals, for individuals living in rural neighborhoods, and for individuals living in high-income neighborhoods compared to individuals living in low-income neighborhoods.

Conclusions

Higher levels of greenness may reduce the risk of predicted COVID-19-like illness incidence, but these associations were not observed in all populations.

Association of greenness with COVID-19 deaths in India: An ecological study at district level

BACKGROUND

The world has witnessed a colossal death toll due to the novel coronavirus disease-2019 (COVID-19). A few environmental epidemiology studies have identified association of environmental factors (air pollution, greenness, temperature, etc.) with COVID-19 incidence and mortality, particularly in developed countries. India, being one of the most severely affected countries by the pandemic, still has a dearth of research exploring the linkages of environment and COVID-19 pandemic.

OBJECTIVES

We evaluate whether district-level greenness exposure is associated with a reduced risk of COVID-19 deaths in India.

METHODS

We used average normalized difference vegetation index (NDVI) from January to March 2019, derived by Oceansat-2 satellite, to represent district-level greenness exposure. COVID-19 death counts were obtained through May 1, 2021 (around the peak of the second wave) from an open portal: covid19india.org. We used hierarchical generalized negative binomial regressions to check the associations of greenness with COVID-19 death counts. Analyses were adjusted for air pollution (PM2.5), temperature, rainfall, population density, proportion of older adults (50 years and above), sex ratio over age 50, proportions of rural population, household overcrowding, materially deprived households, health facilities, and secondary school education.

RESULTS

Our analyses found a significant association between greenness and reduced risk of COVID-19 deaths. Compared to the districts with the lowest NDVI (quintile 1), districts within quintiles 3, 4, and 5 have respectively, around 32% [MRR = 0.68 (95% CI: 0.51, 0.88)], 39% [MRR = 0.61 (95% CI: 0.46, 0.80)], and 47% [MRR = 0.53 (95% CI: 0.40, 0.71)] reduced risk of COVID-19 deaths. The association remains consistent for analyses restricted to districts with a rather good overall death registration (>80%).

CONCLUSION

Though cause-of-death statistics are limited, we confirm that exposure to greenness was associated with reduced district-level COVID-19 deaths in India. However, material deprivation and air pollution modify this association.

Five waves of the COVID-19 pandemic and green-blue spaces in urban and rural areas in Poland

Several waves of COVID-19 caused by different SARS-CoV-2 variants have been recorded worldwide. During this period, many publications were released describing the influence of various factors, such as environmental, social and economic factors, on the spread of COVID-19. This paper presents the results of a detailed spatiotemporal analysis of the course of COVID-19 cases and deaths in five waves in Poland in relation to green‒blue spaces. The results, based on 380 counties, reveal that the negative correlation between the indicator of green‒blue space per inhabitant and the average daily number of COVID-19 cases and deaths was clearly visible during all waves. These relationships were described by a power equation (coefficient of determination ranging from 0.83 to 0.88) with a high level of significance. The second important discovery was the fact that the rates of COVID-19 cases and deaths were significantly higher in urban counties (low values of the green-blue space indicator in m²/people) than in rural areas. The developed models can be used in decision-making by local government authorities to organize anti-COVID-19 prevention measures, including local lockdowns, especially in urban areas.