The impacts of the built environment on the incidence rate of COVID-19: A case study of King County, Washington

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

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 ( 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.

Investigating the spatiotemporal characteristics and medical response during the initial COVID-19 epidemic in six Chinese cities

In the future, novel and highly pathogenic viruses may re-emerge, leading to a surge in healthcare demand. It is essential for urban epidemic control to investigate different cities' spatiotemporal spread characteristics and medical carrying capacity during the early stages of COVID-19. This study employed textual analysis, mathematical statistics, and spatial analysis methods to examine the situation in six highly affected Chinese cities. The findings reveal that these cities experienced three phases during the initial outbreak of COVID-19: "unknown-origin incubation", "Wuhan-related outbreak", and "local exposure outbreak". Cities with a high number of confirmed cases exhibited a multicore pattern, while those with fewer cases displayed a single-core pattern. The cores were distributed hierarchically in the central built-up areas of cities' economic, political, or transportation centers. The radii of these cores shrank as the central built-up area's level decreased, indicating a hierarchical decay and a core-edge structure. It suggests that decentralized built environments (non-clustered economies and populations) are less likely to facilitate large-scale epidemic clusters. Additionally, the deployment of designated hospitals in these cities was consistent with the spatial distribution of the epidemic; however, their carrying capacity requires urgent improvement. Ultimately, the essence of prevention and control is the governance of human activities and the efficient management of limited resources about individuals, places, and materials through leveraging IT and GIS technologies to address supply-demand contradictions.

COVID-19, Cities and Inequality

COVID-19 has changed our lives and will likely leave a lasting imprint on our cities. This paper reviews how the pandemic has altered the way people commute, work, collaborate, and consume, especially its reflection on urban space and spatial inequality. We conceptualize these urban changes as structural transformation, accelerated transition, and temporal change. First, we have seen more structural transformation far exceeding scholars' earlier predictions, especially remote working and global supply chain restructuring. Second, COVID-19 has accelerated the processes of digitalization and sustainable transition. While COVID-19 has contributed to suburbanization and urban sprawl, it has also raised the significance of green spaces and the environment. Third, COVID-19 reduced human impact on the environment, which might be temporary. Last, the pandemic has also amplified the pre-existing inequalities in urban areas, created a more fragmented and segregated urban landscape, and expanded the scope of urban inequality research by connecting health inequality with environmental and socio-injustice. We further discuss the emergence of post-pandemic urban theories and identify research questions for future research.

How do density, employment and transit affect the prevalence of COVID-19 pandemic? A study of 3,141 counties across the United States

Previous research has explored the effect of the built environment on the spread of the coronavirus disease (COVID-19) pandemic. This study extends the existing literature by examining the relationship between pandemic prevalence and density, employment, and transit factors at the county level. Using multilinear spatial-lag regressions and time series clustering analyses on the Smart Location Database encompassing 3141 counties in the United States, our findings reveal the following: (1) Density, employment, and transit variables yield heterogeneous effects to infection rate, death rate, and mortality rate. (2) Pedestrian-oriented road density is positively correlated to the prevalence of COVID-19, every 0.011 miles/acre increase is associated with 1% increase in the infection rate. (3) A consistent negative correlation is observed between jobs per household and infection rate, while a decrease in unemployment rate leads to an increase in the death rate. (4) The results from time series analysis suggest that areas characterized by low auto-oriented intersection density but high pedestrian-oriented road density are more susceptible to the impacts of pandemics. This highlights the need to prioritize pandemic prevention efforts in the suburban and rural areas with low population density, as emphasized in existing literature emphasized.

Tokyo's COVID-19: An urban perspective on factors influencing infection rates in a global city

This research investigates the relationship between COVID-19 and urban factors in Tokyo. To understand the spread dynamics of COVID-19, the study examined 53 urban variables (including population density, socio-economic status, housing conditions, transportation, and land use) in 53 municipalities of Tokyo prefecture. Using spatial models, the study analysed the patterns and predictors of COVID-19 infection rates. The findings revealed that COVID-19 cases were concentrated in central Tokyo, with clustering levels decreasing after the outbreaks. COVID-19 infection rates were higher in areas with a greater density of retail stores, restaurants, health facilities, workers in those sectors, public transit use, and telecommuting. However, household crowding was negatively associated. The study also found that telecommuting rate and housing crowding were the strongest predictors of COVID-19 infection rates in Tokyo, according to the regression model with time-fixed effects, which had the best validation and stability. This study's results could be useful for researchers and policymakers, particularly because Japan and Tokyo have unique circumstances, as there was no mandatory lockdown during the pandemic.

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.

Household conditions, COVID-19, and equity: Insight from two nationally representative surveys

Background

With people across the United States spending increased time at home since the emergence of COVID-19, housing characteristics may have an even greater impact on health. Therefore, we assessed associations between household conditions and COVID-19 experiences.

Methods

We used data from two nationally representative surveys: the Tufts Equity Study (TES; n = 1449 in 2021; n = 1831 in 2022) and the Household Pulse Survey (HPS; n = 147,380 in 2021; n = 62,826 in 2022). In the TES, housing conditions were characterized by heating/cooling methods; smoking inside the home; visible water damage/mold; age of housing unit; and self-reported concern about various environmental factors. In TES and HPS, household size was assessed. Accounting for sampling weights, we examined associations between each housing exposure and COVID-19 outcomes (diagnosis, vaccination) using separate logistic regression models with covariates selected based on an evidence-based directed acyclic graph.

Results

Having had COVID-19 was more likely among people who reported poor physical housing condition (odds ratio [OR] = 2.32; 95% confidence interval [CI] = 1.17-4.59; 2021), visible water damage or mold/musty smells (OR = 1.50; 95% CI = 1.10-2.03; 2022), and larger household size (5+ versus 1-2 people; OR = 1.53, 95% CI = 1.34-1.75, HPS 2022). COVID-19 vaccination was less likely among participants who reported smoke exposure inside the home (OR = 0.53; 95% CI = 0.31-0.90; 2022), poor water quality (OR = 0.42; 95% CI = 0.21-0.85; 2021), noise from industrial activity/construction (OR = 0.44; 95% CI = 0.19-0.99; 2022), and larger household size (OR = 0.57; 95% CI = 0.46-0.71; HPS 2022). Vaccination was also positively associated with poor indoor air quality (OR = 1.96; 95% CI = 1.02-3.72; 2022) and poor physical housing condition (OR = 2.27; 95% CI = 1.01-5.13; 2022). Certain heating/cooling sources were associated with COVID-19 outcomes.

Conclusions

Our study found poor housing conditions associated with increased COVID-19 burden, which may be driven by systemic disparities in housing, healthcare, and financial access to resources during the COVID-19 pandemic.

The Role of Greenery in Stress Reduction among City Residents during the COVID-19 Pandemic

Cities, as places of social interactions and human relationships, face new challenges, problems, and threats, which are sources of stress for residents. An additional cause of stress in recent years has been the COVID-19 pandemic; it was urban dwellers who were most exposed to the virus and most affected by it. Chronic stress has led to the serious erosion of physical health and psychophysical well-being among urban dwellers, and so there is a need to seek new solutions in terms of building the resilience of cities and their residents to stress. This study aims to verify the hypothesis that greenery reduced the level of stress among urban dwellers during the pandemic. The verification of this hypothesis was achieved based on a literature analysis and the results of geo-questionnaire studies conducted involving 651 residents of Poznan-among the largest of Polish cities, where the share of green areas in the spatial structure is more than 30%. According to the analysis, the interviewees experienced above-average stress levels that went up during the pandemic, and the source was not so much the virus but the restrictions imposed. Green areas and outdoor activities helped in reducing this stress (being surrounded by and looking at greenery, garden work, or plant cultivation). Residents perceive a post-pandemic city as one that is more green, in which priority is given to unmanaged green areas. It has also been pointed out that a response to the reported need for urban re-construction towards stress resilience may be a biophilic city.

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.

Associations Between Built Environment Factors and SARS-CoV-2 Infections at the Neighbourhood Level in a Metropolitan Area in Germany

COVID-19-related health outcomes displayed distinct geographical patterns within countries. The transmission of SARS-CoV-2 requires close spatial proximity of people, which can be influenced by the built environment. Only few studies have analysed SARS-CoV-2 infections related to the built environment within urban areas at a high spatial resolution. This study examined the association between built environment factors and SARS-CoV-2 infections in a metropolitan area in Germany. Polymerase chain reaction (PCR)-confirmed SARS-CoV-2 infections of 7866 citizens of Essen between March 2020 and May 2021 were analysed, aggregated at the neighbourhood level. We performed spatial regression analyses to investigate associations between the cumulative number of SARS-CoV-2 infections per 1000 inhabitants (cum. SARS-CoV-2 infections) up to 31.05.2021 and built environment factors. The cum. SARS-CoV-2 infections in neighbourhoods (median: 11.5, IQR: 8.1-16.9) followed a marked socially determined north-south gradient. The effect estimates of the adjusted spatial regression models showed negative associations with urban greenness, i.e. normalized difference vegetation index (NDVI) (adjusted β =  - 35.36, 95% CI: - 57.68; - 13.04), rooms per person (- 10.40, - 13.79; - 7.01), living space per person (- 0.51, - 0.66; - 0.36), and residential (- 0.07, 0.16; 0.01) and commercial areas (- 0.15, - 0.25; - 0.05). Residential areas with multi-storey buildings (- 0.03, - 0.12; 0.06) and green space (0.03, - 0.05; 0.11) did not show a substantial association. Our results suggest that the built environment matters for the spread of SARS-CoV-2 infections, such as more spacious apartments or higher levels of urban greenness are associated with lower infection rates at the neighbourhood level. The unequal intra-urban distribution of these factors emphasizes prevailing environmental health inequalities regarding the COVID-19 pandemic.

Modelling Impact of High-Rise, High-Density Built Environment on COVID-19 Risks: Empirical Results from a Case Study of Two Chinese Cities

Characteristics of the urban environment (e.g., building density and road network) can influence the spread and transmission of coronavirus disease 2019 (COVID-19) within cities, especially in high-density high-rise built environments. Therefore, it is necessary to identify the key attributes of high-density high-rise built environments to enhance modelling of the spread of COVID-19. To this end, case studies for testing attributes for modelling development were performed in two densely populated Chinese cities with high-rise, high-density built environments (Hong Kong and Shanghai).The investigated urban environmental features included 2D and 3D urban morphological indices (e.g., sky view factor, floor area ratio, frontal area density, height to width ratio, and building coverage ratio), socioeconomic and demographic attributes (e.g., population), and public service points-of-interest (e.g., bus stations and clinics). The modelling effects of 3D urban morphological features on the infection rate are notable in urban communities. As the spatial scale becomes larger, the modelling effect of 2D built environment factors (e.g., building coverage ratio) on the infection rate becomes more notable. The influence of several key factors (e.g., the building coverage ratio and population density) at different scales can be considered when modelling the infection risk in urban communities. The findings of this study clarify how attributes of built environments can be applied to predict the spread of infectious diseases. This knowledge can be used to develop effective planning strategies to prevent and control epidemics and ensure healthy cities.

Measuring the effects of Compactness/Sprawl on COVID 19 spread patterns at the neighborhood level

This study analyzes the compactness/sprawl index and its effects on the spread of COVID-19 in the neighborhoods of Ahvaz, Iran. Multiple Criteria Decision Making and GIS techniques were used to develop the index. Also, the effects of compactness/sprawl on COVID-19 were investigated using a regression model. It was found that when considering the number of COVID-19 cases per 1000 people, the compactness/sprawl index did not affect the spread of the disease. However, it had a low but significant effect if the raw number of cases was considered. Results also showed that the compactness index significantly affected the raw number of cases, with a coefficient of 0.291, indicating that more compact neighborhoods had more COVID-19 cases. This is unsurprising as more people live in compact areas and, therefore, the raw number of cases is also likely to be higher. In the absence of proper control measures, this could result in further contact between people, thereby, increasing the risk of virus spread. Overall, we found that compactness had a dual effect on the spread of COVID-19 in Ahvaz. We conclude that proper development and implementation of control measures in well-designed compact neighborhoods are essential for enhancing pandemic resilience.

Mechanisms influencing the factors of urban built environments and coronavirus disease 2019 at macroscopic and microscopic scales: The role of cities

In late 2019, the coronavirus disease 2019 (COVID-19) pandemic soundlessly slinked in and swept the world, exerting a tremendous impact on lifestyles. This study investigated changes in the infection rates of COVID-19 and the urban built environment in 45 areas in Manhattan, New York, and the relationship between the factors of the urban built environment and COVID-19. COVID-19 was used as the outcome variable, which represents the situation under normal conditions vs. non-pharmacological intervention (NPI), to analyze the macroscopic (macro) and microscopic (micro) factors of the urban built environment. Computer vision was introduced to quantify the material space of urban places from street-level panoramic images of the urban streetscape. The study then extracted the microscopic factors of the urban built environment. The micro factors were composed of two parts. The first was the urban level, which was composed of urban buildings, Panoramic View Green View Index, roads, the sky, and buildings (walls). The second was the streets' green structure, which consisted of macrophanerophyte, bush, and grass. The macro factors comprised population density, traffic, and points of interest. This study analyzed correlations from multiple levels using linear regression models. It also effectively explored the relationship between the urban built environment and COVID-19 transmission and the mechanism of its influence from multiple perspectives.

Effects of housing environments on COVID-19 transmission and mental health revealed by COVID-19 Participant Experience data from the All of Us Research Program in the USA: a case-control study

OBJECTIVES

To examine the association between housing types and COVID-19 infection (or mental health) during the early stages of the pandemic by using the large-scale individual-level All of Us Research Program COVID-19 Participant Experience (COPE) survey data. We hypothesise that housing types with a shared component are associated with elevated COVID-19 infection and subsequent mental health conditions.

DESIGN

A retrospective case-control study.

SETTING

Secondary analysis of online surveys conducted in the USA.

PARTICIPANTS

62 664 participant responses to COPE from May to July 2020.

PRIMARY AND SECONDARY OUTCOME MEASURES

Primary outcome measure is the self-reported COVID-19 status, and the secondary outcome measures are anxiety or stress. Both measures were applied for matched cases and controls of the same race, sex, age group and survey version.

RESULTS

A multiple logistic regression analysis revealed that housing types with a shared component are significantly associated with COVID-19 infection (OR=1.19, 95% CI 1.1 to 1.3; p=2×10^-4), anxiety (OR=1.26, 95% CI 1.1 to 1.4; p=1.1×10^-6) and stress (OR=1.29, 95% CI 1.2 to 1.4; p=4.3×10^-10) as compared with free-standing houses, after adjusting for confounding factors. Further, frequent optional shopping or outing trips, another indicator of the built environment, are also associated with COVID-19 infection (OR=1.36, 95% CI 1.1 to 1.8; p=0.02), but not associated with elevated mental health conditions. Confounding factors are controlled in the analysis such as ethnicity, age, social distancing behaviour and house occupancy.

CONCLUSION

Our study demonstrates that houses with a shared component tend to have an increased risk of COVID-19 transmission, which consequently leads to high levels of anxiety and stress for their dwellers. The study also suggests the necessity to improve the quality of the built environment such as residential housing and its surroundings through planning, design and management, ensuring a more resilient society that can cope with future pandemics.

Effects of housing environments on COVID-19 transmission and mental health revealed by COVID-19 Participant Experience data from the All of Us Research Program in the USA: a case-control study

OBJECTIVES

To examine the association between housing types and COVID-19 infection (or mental health) during the early stages of the pandemic by using the large-scale individual-level All of Us Research Program COVID-19 Participant Experience (COPE) survey data. We hypothesise that housing types with a shared component are associated with elevated COVID-19 infection and subsequent mental health conditions.

DESIGN

A retrospective case-control study.

SETTING

Secondary analysis of online surveys conducted in the USA.

PARTICIPANTS

62 664 participant responses to COPE from May to July 2020.

PRIMARY AND SECONDARY OUTCOME MEASURES

Primary outcome measure is the self-reported COVID-19 status, and the secondary outcome measures are anxiety or stress. Both measures were applied for matched cases and controls of the same race, sex, age group and survey version.

RESULTS

A multiple logistic regression analysis revealed that housing types with a shared component are significantly associated with COVID-19 infection (OR=1.19, 95% CI 1.1 to 1.3; p=2×10^-4), anxiety (OR=1.26, 95% CI 1.1 to 1.4; p=1.1×10^-6) and stress (OR=1.29, 95% CI 1.2 to 1.4; p=4.3×10^-10) as compared with free-standing houses, after adjusting for confounding factors. Further, frequent optional shopping or outing trips, another indicator of the built environment, are also associated with COVID-19 infection (OR=1.36, 95% CI 1.1 to 1.8; p=0.02), but not associated with elevated mental health conditions. Confounding factors are controlled in the analysis such as ethnicity, age, social distancing behaviour and house occupancy.

CONCLUSION

Our study demonstrates that houses with a shared component tend to have an increased risk of COVID-19 transmission, which consequently leads to high levels of anxiety and stress for their dwellers. The study also suggests the necessity to improve the quality of the built environment such as residential housing and its surroundings through planning, design and management, ensuring a more resilient society that can cope with future pandemics.

Effects of the built environment and human factors on the spread of COVID-19: A systematic literature review

Soon after its emergence, COVID-19 became a global problem. While different types of vaccines and treatments are now available, still non-pharmacological policies play a critical role in managing the pandemic. The literature is enriched enough to provide comprehensive, practical, and scientific insights to better deal with the pandemic. This research aims to find out how the built environment and human factors have affected the transmission of COVID-19 on different scales, including country, state, county, city, and urban district. This is done through a systematic literature review of papers indexed on the Web of Science and Scopus. Initially, these databases returned 4264 papers, and after different stages of screening, we found 166 relevant papers and reviewed them. The empirical papers that had at least one case study and analyzed the effects of at least one built environment factor on the spread of COVID-19 were selected. Results showed that the driving forces can be divided into seven main categories: density, land use, transportation and mobility, housing conditions, demographic factors, socio-economic factors, and health-related factors. We found that among other things, overcrowding, public transport use, proximity to public spaces, the share of health and services workers, levels of poverty, and the share of minorities and vulnerable populations are major predictors of the spread of the pandemic. As the most studied factor, density was associated with mixed results on different scales, but about 58 % of the papers reported that it is linked with a higher number of cases. This study provides insights for policymakers and academics to better understand the dynamic roles of the non-pharmacological driving forces of COVID-19 at different levels.

Lessons learnt for enhancing hospital resilience to pandemics: A qualitative analysis from Italy

The COVID-19 pandemic has outlined the need to strengthen the resilience of healthcare systems. It has cost millions of human lives and has had indirect health impacts too. Hospital buildings have undergone extensive modifications and adaptations to ensure infection control and prevention measures, and, as it is happened following past epidemics, the COVID-19 experience might change the design of hospital buildings in the future. This paper aims to capitalise on the knowledge developed by the stakeholders directly involved with the hospital response during the pandemic to generate new evidence that will enhance resilience of hospital buildings to pandemics. The research adopted qualitative research methods, namely literature review and interviews with Italian experts including doctors and facility managers to collect data which were analysed through a thematic analysis. The findings include the identification of new needs for hospital buildings and the related actions to be taken or already performed at hospital building and service level which are viable for long term implementation and are aimed at improving hospital resilience to pandemics. The results specify how to improve resilience by means of structural modifications (e.g. placing filter zones among different wards, ensuring the presence of airborne infection isolation rooms at least in the emergency departments), technological changes (e.g. oversizing capacity such as medical gases, information technology improvement for delivering healthcare services remotely), and operational measures (e.g. assessing the risk of infection before admission, dividing acute-care from low-care assets). The needs discussed in this paper substantiate the urge to renovate the Italian healthcare infrastructures and they can be considered useful elements of knowledge for enhancing hospital resilience to pandemics in the extended and in the post-COVID-19 era.

An overview and thematic analysis of research on cities and the COVID-19 pandemic: Toward just, resilient, and sustainable urban planning and design

Since early 2020, researchers have made efforts to study various issues related to cities and the pandemic. Despite the wealth of research on this topic, there are only a few review articles that explore multiple issues related to it. This is partly because of the rapid pace of publications that makes systematic literature review challenging. To address this issue, in the present study, we rely on bibliometric analysis techniques to gain an overview of the knowledge structure and map key themes and trends of research on cities and the pandemic. Results of the analysis of 2,799 articles show that research mainly focuses on six broad themes: air quality, meteorological factors, built environment factors, transportation, socio-economic disparities, and smart cities, with the first three being dominant. Based on the findings, we discuss major lessons that can be learned from the pandemic and highlight key areas that need further research.

Correlations between the urban built environmental factors and the spatial distribution at the community level in the reported COVID-19 samples: A case study of Wuhan

COVID-19 has dramatically changed the lifestyle of people, especially in urban environments. This paper investigated the variations of built environments that were measurably associated with the spread of COVID-19 in 150 Wuhan communities. The incidence rate in each community before and after the lockdown (January 23, 2020), as respective dependent variables, represented the situation under normal circumstances and non-pharmaceutical interventions (NPI). After controlling the population density, floor area ratio (FAR), property age and sociodemographic factors, the built environmental factors in two spatial dimensions, the 15-minute walking life circle and the 10-minute cycling life circle, were brought into the Hierarchical Linear Regression Model and the Ridge Regression Model. The results indicated that before lockdown, the number of markets and schools were positively associated with the incidence rate, while community population density and FAR were negatively associated with COVID-19 transmission. After lockdown, FAR, GDP, the number of hospitals (in the 15-minute walking life circle) and the bus stations (in the 10-minute cycling life circle) became negatively correlated with the incidence rate, while markets remained positive. This study effectively extends the discussions on the association between the urban built environment and the spread of COVID-19. Meanwhile, given the limitations of sociodemographic data sources, the conclusions of this study should be interpreted and applied with caution.

Methods Used in the Spatial and Spatiotemporal Analysis of COVID-19 Epidemiology: A Systematic Review

The spread of the COVID-19 pandemic was spatially heterogeneous around the world; the transmission of the disease is driven by complex spatial and temporal variations in socioenvironmental factors. Spatial tools are useful in supporting COVID-19 control programs. A substantive review of the merits of the methodological approaches used to understand the spatial epidemiology of the disease is hardly undertaken. In this study, we reviewed the methodological approaches used to identify the spatial and spatiotemporal variations of COVID-19 and the socioeconomic, demographic and climatic drivers of such variations. We conducted a systematic literature search of spatial studies of COVID-19 published in English from Embase, Scopus, Medline, and Web of Science databases from 1 January 2019 to 7 September 2021. Methodological quality assessments were also performed using the Joanna Briggs Institute (JBI) risk of bias tool. A total of 154 studies met the inclusion criteria that used frequentist (85%) and Bayesian (15%) modelling approaches to identify spatial clusters and the associated risk factors. Bayesian models in the studies incorporated various spatial, temporal and spatiotemporal effects into the modelling schemes. This review highlighted the need for more local-level advanced Bayesian spatiotemporal modelling through the multi-level framework for COVID-19 prevention and control strategies.

How residential energy consumption has changed due to COVID-19 pandemic? An agent-based model

Integrating occupant behavior with residential energy use for detailed energy quantification has attracted research attention. However, many of the available models fail to capture unseen behavior, especially in unprecedented situations such as COVID-19 lockdowns. In this study, we adopted a hybrid approach consisting of agent-based simulation, machine learning and energy simulation techniques to simulate the urban energy consumption considering the occupants' behavior. An agent-based model is developed to simulate the in-home and out-of-home activities of individuals. Separate models were developed to recognize physical characteristics of residential dwellings, including heating equipment, source of energy, and thermostat setpoints. The developed modeling framework was implemented as a case study for the Central Okanagan region of British Columbia, where alternative COVID-19 scenarios were tested. The results suggested that during the pandemic, the daily average in-home-activity duration (IHD) increased by approximately 80%, causing the energy consumption to increase by around 29%. After the pandemic, the average daily IHD is expected to be higher by approximately 32% compared with the pre-pandemic situation, which translates to an approximately 12% increase in energy consumption. The results of this study can help us understand the implications of the imposed COVID-19 lockdown with respect to energy usage in residential locations.

Dynamics of SARS-CoV-2 spreading under the influence of environmental factors and strategies to tackle the pandemic: A systematic review

COVID-19 is deemed as the most critical world health calamity of the 21st century, leading to dramatic life loss. There is a pressing need to understand the multi-stage dynamics, including transmission routes of the virus and environmental conditions due to the possibility of multiple waves of COVID-19 in the future. In this paper, a systematic examination of the literature is conducted associating the virus-laden-aerosol and transmission of these microparticles into the multimedia environment, including built environments. Particularly, this paper provides a critical review of state-of-the-art modelling tools apt for COVID-19 spread and transmission pathways. GIS-based, risk-based, and artificial intelligence-based tools are discussed for their application in the surveillance and forecasting of COVID-19. Primary environmental factors that act as simulators for the spread of the virus include meteorological variation, low air quality, pollen abundance, and spatial-temporal variation. However, the influence of these environmental factors on COVID-19 spread is still equivocal because of other non-pharmaceutical factors. The limitations of different modelling methods suggest the need for a multidisciplinary approach, including the 'One-Health' concept. Extended One-Health-based decision tools would assist policymakers in making informed decisions such as social gatherings, indoor environment improvement, and COVID-19 risk mitigation by adapting the control measurements.

Neighborhood Characteristics and Racial Disparities in Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Seropositivity in Pregnancy

OBJECTIVE

To quantify the extent to which neighborhood characteristics contribute to racial and ethnic disparities in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seropositivity in pregnancy.

METHODS

This cohort study included pregnant patients who presented for childbirth at two hospitals in Philadelphia, Pennsylvania from April 13 to December 31, 2020. Seropositivity for SARS-CoV-2 was determined by measuring immunoglobulin G and immunoglobulin M antibodies by enzyme-linked immunosorbent assay in discarded maternal serum samples obtained for clinical purposes. Race and ethnicity were self-reported and abstracted from medical records. Patients' residential addresses were geocoded to obtain three Census tract variables: community deprivation, racial segregation (Index of Concentration at the Extremes), and crowding. Multivariable mixed effects logistic regression models and causal mediation analyses were used to quantify the extent to which neighborhood variables may explain racial and ethnic disparities in seropositivity.

RESULTS

Among 5,991 pregnant patients, 562 (9.4%) were seropositive for SARS-CoV-2. Higher seropositivity rates were observed among Hispanic (19.3%, 104/538) and Black (14.0%, 373/2,658) patients, compared with Asian (3.2%, 13/406) patients, White (2.7%, 57/2,133) patients, and patients of another race or ethnicity (5.9%, 15/256) (P<.001). In adjusted models, per SD increase, deprivation (adjusted odds ratio [aOR] 1.16, 95% CI 1.02-1.32) and crowding (aOR 1.15, 95% CI 1.05-1.26) were associated with seropositivity, but segregation was not (aOR 0.90, 95% CI 0.78-1.04). Mediation analyses revealed that crowded housing may explain 6.7% (95% CI 2.0-14.7%) of the Hispanic-White disparity and that neighborhood deprivation may explain 10.2% (95% CI 0.5-21.1%) of the Black-White disparity.

CONCLUSION

Neighborhood deprivation and crowding were associated with SARS-CoV-2 seropositivity in pregnancy in the prevaccination era and may partially explain high rates of SARS-CoV-2 seropositivity among Black and Hispanic patients. Investing in structural neighborhood improvements may reduce inequities in viral transmission.

Geographies of infections: built environment and COVID-19 pandemic in metropolitan Melbourne

This paper uses spatial statistical techniques to reflect on geographies of COVID-19 infections in metropolitan Melbourne. We argue that the evolution of the COVID-19 pandemic, which has become widespread since early 2020 in Melbourne, typically proceeds through multiple built environment attributes - diversity, destination accessibility, distance to transit, design, and density. The spread of the contagion is institutionalised within local communities and postcodes, and reshapes movement practices, discourses, and structures of administrative politics. We demonstrate how a focus on spatial patterns of the built environment can inform scholarship on the spread of infections associated with COVID-19 pandemic and geographies of infections more broadly, by highlighting the consistency of built environment influences on COVID-19 infections across three waves of outbreaks. A focus on the built environment influence seeks to enact visions of the future as new variants emerge, illustrating the importance of understanding geographies of infections as global cities adapt to 'COVID-normal' living. We argue that understanding geographies of infections within cities could be a springboard for pursuing sustainable urban development via inclusive compact, mixed-use development and safe public transport.

Dynamic topic modeling of twitter data during the COVID-19 pandemic

In an effort to gauge the global pandemic’s impact on social thoughts and behavior, it is important to answer the following questions: (1) What kinds of topics are individuals and groups vocalizing in relation to the pandemic? (2) Are there any noticeable topic trends and if so how do these topics change over time and in response to major events? In this paper, through the advanced Sequential Latent Dirichlet Allocation model, we identified twelve of the most popular topics present in a Twitter dataset collected over the period spanning April 3^rd to April 13^th, 2020 in the United States and discussed their growth and changes over time. These topics were both robust, in that they covered specific domains, not simply events, and dynamic, in that they were able to change over time in response to rising trends in our dataset. They spanned politics, healthcare, community, and the economy, and experienced macro-level growth over time, while also exhibiting micro-level changes in topic composition. Our approach differentiated itself in both scale and scope to study the emerging topics concerning COVID-19 at a scale that few works have been able to achieve. We contributed to the cross-sectional field of urban studies and big data. Whereas we are optimistic towards the future, we also understand that this is an unprecedented time that will have lasting impacts on individuals and society at large, impacting not only the economy or geo-politics, but human behavior and psychology. Therefore, in more ways than one, this research is just beginning to scratch the surface of what will be a concerted research effort into studying the history and repercussions of COVID-19.

A spatial-temporal analysis at the early stages of the COVID-19 pandemic and its determinants: The case of Recife neighborhoods, Brazil

The outbreak of COVID-19 has led to there being a worldwide socio-economic crisis, with major impacts on developing countries. Understanding the dynamics of the disease and its driving factors, on a small spatial scale, might support strategies to control infections. This paper explores the impact of the COVID-19 on neighborhoods of Recife, Brazil, for which we examine a set of drivers that combines socio-economic factors and the presence of non-stop services. A three-stage methodology was conducted by conducting a statistical and spatial analysis, including clusters and regression models. COVID-19 data were investigated concerning ten dates between April and July 2020. Hotspots of the most affected regions and their determinant effects were highlighted. We have identified that clusters of confirmed cases were carried from a well-developed neighborhood to socially deprived areas, along with the emergence of hotspots of the case-fatality rate. The influence of age-groups, income, level of education, and the access to essential services on the spread of COVID-19 was also verified. The recognition of variables that influence the spatial spread of the disease becomes vital for pinpointing the most vulnerable areas. Consequently, specific prevention actions can be developed for these places, especially in heterogeneous cities.

COVID-19 and transportation of India: influence on infection risk and greenhouse gas emissions

COVID-19 have significant impact on travel behaviour and greenhouse gases (GHG), especially for the most affected city in India, Mumbai metropolitan region (MMR). The present study attempts to explore the risk on different modes of transportation and GHG emissions (based on change in travel behavior) during peak/non-peak hours in a day by an online/offline survey for commuters in Indian metropolitan cities like MMR, Delhi and Bengaluru. In MMR, the probability of infection in car estimated to be 0.88 and 0.29 during peak and non-peak hour, respectively, considering all windows open. The risk of infection in public transportation system such as in bus (0.307), train (0.521), and metro (0.26) observed to be lower than in private vehicles. Furthermore, impact of COVID-19 on GHG emissions have also been explored considering three scenarios. The GHG emissions have been estimated for base (3.83-16.87 tonne), lockdown (0.22-0.48 tonne) and unlocking (2.13-9.30 tonne) scenarios. It has been observed that emissions are highest during base scenario and lowest during lockdown situation. This study will be a breakthrough in understanding the impact of pandemic on environment and transportation. The study shall help transport planners and decision makers to operate public transport during pandemic like situation such that the modal share of public transportation is always highest. It shall also help in regulating the GHG emissions causing climate change.

The Effect of the Built Environment on the COVID-19 Pandemic at the Initial Stage: A County-Level Study of the USA

The COVID-19 pandemic affected how people interact with the built environment and ways of human habitation are facing significant challenges. However, the existing literature has not adequately addressed how the built environment affected the early prevalence of the pandemic. This research aims to extend the existing literature by relating the initial stage pandemic conditions with more comprehensive measures of the built environment including density, diversity, road network, and accessibility at the county level across the United States and conducting bi-weekly comparisons. We collected infection, death, and mortality data in 3141 counties between 1 March to 8 June 2020 and collected seventeen built environment attributes. Our results show that: (1) Road density and street intersection density were significantly associated with the infection rate; (2) Population density only maintained a positive correlation to the prevalence of COVID-19 during the first two weeks, after which the relationship became negative; and (3) Transit accessibility also contributed significantly to the pandemic and the accessibility of transit-oriented jobs was highly correlated to the infection rate in the first two weeks. The study provides valuable insights for policymakers and stakeholders to adopt resource allocation strategies for context-specific conditions.

Are leading urban centers predisposed to global risks- A analysis of the global south from COVID-19 perspective

COVID-19 initially spread among prominent global cities and soon to the urban centers of countries across the globe. While cities are the hotbeds of activities, they also seem highly exposed to global risks including the pandemic. Using the case of COVID-19 and the World Risk Index framework, this paper examines if the leading cities from the global south are inherently vulnerable and exposed to global risks and can they exacerbate the overall risk of their respective nations. Compared against their respective national averages, most of the 20 cities from 10 countries analyzed in this paper, have higher exposure, lower adaptive capacity, higher coping capacity and varied susceptibility. As this relative understanding is based on respective national averages which are often lower than the global standards, even high performance on certain indicators may still result in elevated predisposition. This paper concludes that the leading urban centers from the global south are highly likely to be predisposed to global risks due to their inherent vulnerability and exposure, and many of the drivers of this predisposition are related to the process of urbanization itself. This predisposition can enhance the overall exposure and vulnerability of the nation in which they are located.

Spatial disparities of self-reported COVID-19 cases and influencing factors in Wuhan, China

The lack of detailed COVID-19 cases at a fine spatial resolution restricts the investigation of spatial disparities of its attack rate. Here, we collected nearly one thousand self-reported cases from a social media platform during the early stage of COVID-19 epidemic in Wuhan, China. We used kernel density estimation (KDE) to explore spatial disparities of epidemic intensity and adopted geographically weighted regression (GWR) model to quantify influences of population dynamics, transportation, and social interactions on COVID-19 epidemic. Results show that self-reported COVID-19 cases concentrated in commercial centers and populous residential areas. Blocks with higher population density, higher aging rate, more metro stations, more main roads, and more commercial point-of-interests (POIs) have a higher density of COVID-19 cases. These five explanatory variables explain 76% variance of self-reported cases using an OLS model. Commercial POIs have the strongest influence, which increase COVID-19 cases by 28% with one standard deviation increase. The GWR model performs better than OLS model with the adjusted R ² of 0.96. Spatial heterogeneities of coefficients in the GWR model show that influencing factors play different roles in diverse communities. We further discussed potential implications for the healthy city and urban planning for the sustainable development of cities.

Examining spatiotemporal evolution of racial/ethnic disparities in human mobility and COVID-19 health outcomes: Evidence from the contiguous United States

Social distancing has become a key countermeasure to contain the dissemination of COVID-19. This study examined county-level racial/ethnic disparities in human mobility and COVID-19 health outcomes during the year 2020 by leveraging geo-tracking data across the contiguous US. Sets of generalized additive models were fitted under cross-sectional and time-varying settings, with percentage of mobility change, percentage of staying home, COVID-19 infection rate, and case-fatality ratio as dependent variables, respectively. After adjusting for spatial effects, built environment, socioeconomics, demographics, and partisanship, we found counties with higher Asian populations decreased most in travel, counties with higher White and Asian populations experienced the least infection rate, and counties with higher African American populations presented the highest case-fatality ratio. Control variables, particularly partisanship and education attainment, significantly influenced modeling results. Time-varying analyses further suggested racial differences in human mobility varied dramatically at the beginning but remained stable during the pandemic, while racial differences in COVID-19 outcomes broadly decreased over time. All conclusions hold robust with different aggregation units or model specifications. Altogether, our analyses shine a spotlight on the entrenched racial segregation in the US as well as how it may influence the mobility patterns, urban forms, and health disparities during the COVID-19.

Identification of sampling points for the detection of SARS-CoV-2 in the sewage system

A suitable tool for monitoring the spread of SARS-CoV-2 is to identify potential sampling points in the wastewater collection system that can be used to monitor the distribution of COVID-19 disease affected clusters within a city. The applicability of the developed methodology is presented through the description of the 72,837 population equivalent wastewater collection system of the city of Nagykanizsa, Hungary and the results of the analytical and epidemiological measurements of the wastewater samples. The wastewater sampling was conducted during the 3rd wave of the COVID-19 epidemic. It was found that the overlap between the road system and the wastewater network is high, it is 82 %. It was showed that the proposed methodological approach, using the tools of network science, determines confidently the zones of the wastewater collection system and provides the ideal monitoring points in order to provide the best sampling resolution in urban areas. The strength of the presented approach is that it estimates the network based on publicly available information. It was concluded that the number of zones or sampling points can be chosen based on relevant epidemiological intervention and mitigation strategies. The algorithm allows for continuous effective monitoring of the population infected by SARS-CoV-2 in small-sized cities.

Comparison of COVID-19 infection risks through aerosol transmission in supermarkets and small shops

Aerosol transmission is academically recognized as possible transmission route of Coronavirus disease 2019 (COVID-19). We established an approach to assess the airborne-disease infection risks through aerosol transmission based on the dose-response model and aerosol transport model. The accuracy of evaluation was guaranteed with on-site surveyed ventilation rate and occupant behavior. With the proposed approach, COVID-19 infection risks in 5 typical supermarkets and 21 small shops were evaluated. With one original infected early-shift staff, the average aerosols concentrations at steady-state are 1.06 × 10^-3 RNA copies/m³ in the supermarkets and 4.73 × 10^-2 RNA copies/m³ in the small shops. With the assumption of 5% original infected staff in the retail buildings, the infection probability of one customer is 1.40 × 10^-6 for visiting one small shop and 6.22 × 10^-6 for visiting one supermarket. The averaged infection risk in the supermarkets is higher than the small shops (p-value<0.001). On the other hand, the infection risks are higher for the staff working with the infected staff compared with the customers. The proposed approach can be applied to other occupied buildings and assist the pandemic control policy making for sustainable cities and society.

Spatiotemporal patterns of the COVID-19 control measures impact on industrial production in Wuhan using time-series earth observation data

Understanding the spatiotemporal patterns of the COVID-19 impact on industrial production could improve the estimation of the economic loss and sustainable work resumption policies in cities. In this study, assuming and checking a correlation between the land surface temperature (LST) and industrial production, we applied the BFAST algorithm and linear regression models on multi-temporal MODIS data to derive monthly time-series deviation of LST with a spatial resolution of 1 × 1 km, to quantificationally explore the fine-scale spatiotemporal patterns of the COVID-19 control measures impact on industrial production, within Wuhan city. The results demonstrate that (1) the trend of time-series LST could partly reflect the impact of the COVID-19 pandemic on industrial production, and the year-around industrial production was less than expectations, with a fall of 14.30%; (2) the most serious COVID-19 impact on industrial production appeared in Mar. and Apr., then, after the lifting of lockdown, some regions (approximate 4.90%) firstly returned to expected levels in Jun, and almost all regions (98.49%) have completed the resumption of work and production before Nov.; (3) the southwest and south-central had more serious impact of the COVID-19 pandemic, approximate twice as much as that in the north and suburban, in Wuhan. The results and findings elaborated the spatiotemporal distribution and their changes during 2020 within Wuhan, which could provide a beneficial support for assessment of the COVID-19 pandemic and implementation of resumption plans for sustainable development.

On the Critical Role of Human Feces and Public Toilets in the Transmission of COVID-19: Evidence from China

The surprising spread speed of the COVID-19 pandemic creates an urgent need for investigating the transmission chain or transmission pattern of COVID-19 beyond the traditional respiratory channels. This study therefore examines whether human feces and public toilets play a critical role in the transmission of COVID-19. First, it develops a theoretical model that simulates the transmission chain of COVID-19 through public restrooms. Second, it uses stabilized epidemic data from China to empirically examine this theory, conducting an empirical estimation using a two-stage least squares (2SLS) model with appropriate instrumental variables (IVs). This study confirms that the wastewater directly promotes the transmission of COVID-19 within a city. However, the role of garbage in this transmission chain is more indirect in the sense that garbage has a complex relationship with public toilets, and it promotes the transmission of COVID-19 within a city through interaction with public toilets and, hence, human feces. These findings have very strong policy implications in the sense that if we can somehow use the ratio of public toilets as a policy instrument, then we can find a way to minimize the total number of infections in a region. As shown in this study, pushing the ratio of public toilets (against open defecation) to the local population in a city to its optimal level would help to reduce the total infection in a region.