Spatial analysis of the impact of urban geometry and socio-demographic characteristics on COVID-19, a study in Hong Kong

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.

Street-level built environment on SARS-CoV-2 transmission: A study of Hong Kong

Understanding the association between SARS-CoV-2 Spatial Transmission Risk (SSTR) and Built Environments (BE) is crucial for implementing effective pandemic prevention measures. Massive efforts have been made to examine the macro-built environment at the regional level, which has neglected the living service areas at the residential scale. Therefore, this study aims to explore the association between Street-level Built Environments (SLBE) and SSTR in Hong Kong from the 1st to the early 5th waves of the pandemic to address this gap. A total of 3693 visited/resided buildings were collected and clustered by spatial autocorrelation, and then Google Street View (GSV) was employed to obtain SLBE features around the buildings. Eventually, the interpretable machine learning framework based on the random forest algorithm (RFA)-based SHapley Additive exPlanations (SHAP) model was proposed to reveal the hidden non-linear association between SSTR and SLBE. The results indicated that in the high-risk cluster area, street sidewalks, street sanitation facilities, and artificial structures were the primary risk factors positively associated with SSTR, in low-risk cluster areas with a significant positive association with traffic control facilities. Our study elucidates the role of SLBE in COVID-19 transmission, facilitates strategic resource allocation, and guides the optimization of outdoor behavior during pandemics for urban policymakers.

Exploring the Spatial and Temporal Patterns of Children and Adolescents with COVID-19 Infections in Slovakia during March 2020 to July 2022

Background and Objectives: The COVID-19 pandemic has had a significant global impact, necessitating a comprehensive understanding of its spatiotemporal patterns. The objective of this study is to explore the spatial and temporal patterns of COVID-19 infections among five age groups (<1, 1-4, 5-9, 10-14, and 15-19 years) in 72 districts of Slovakia on a quarterly basis from March 2020 to July 2022. Material and Methods: During the study period, a total of 393,429 confirmed PCR cases of COVID-19 or positive antigen tests were recorded across all studied age groups. The analysis examined the spatiotemporal spread of COVID infections per quarter, from September 2021 to May 2022. Additionally, data on hospitalizations, intensive care unit (ICU) admissions, pulmonary ventilation (PV), and death cases were analyzed. Results: The highest number of COVID-19 infections occurred between September 2021 and May 2022, particularly in the 10-14-year-old group (68,695 cases), followed by the 15-19-year-old group (62,232 cases), while the lowest incidence was observed in the <1-year-old group (1235 cases). Out of the total confirmed PCR cases, 18,886 individuals required hospitalization, 456 needed ICU admission, 402 received pulmonary ventilation, and only 16 died. The analysis of total daily confirmed PCR cases for all regions showed two major peaks on 12 December 2021 (6114 cases) and 1 February 2022 (3889 cases). Spatial mapping revealed that during December 2021 to February 2022, the highest number of infections in all age groups were concentrated mainly in Bratislava. Moreover, temporal trends of infections within each age group, considering monthly and yearly variations, exhibited distinct spatial patterns, indicating localized outbreaks in specific regions. Conclusions: The spatial and temporal patterns of COVID-19 infections among different age groups in Slovakia showed a higher number of infections in the 10-14-year-old age group, mainly occurring in urban districts. The temporal pattern of the spread of the virus to neighboring urban and rural districts reflected the movement of infected individuals. Hospitalizations, ICU and PV admissions, and deaths were relatively low. The study highlights the need for more proactive measures to contain outbreaks promptly and ensure the resilience of healthcare systems against future pandemics.

Social and economic variables explain COVID-19 diffusion in European regions

At the beginning of 2020, Italy was the country with the highest number of COVID-19 cases, not only in Europe, but also in the rest of the world, and Lombardy was the most heavily hit region of Italy. The objective of this research is to understand which variables have determined the prevalence of cases in Lombardy and in other highly-affected European regions. We consider the first and second waves of the COVID-19 pandemic, using a set of 22 variables related to economy, population, healthcare and education. Regions with a high prevalence of cases are extracted by means of binary classifiers, then the most relevant variables for the classification are determined, and the robustness of the analysis is assessed. Our results show that the most meaningful features to identify high-prevalence regions include high number of hours spent in work environments, high life expectancy, and low number of people leaving from education and neither employed nor educated or trained.

The impact of urban spatial environment on COVID-19: a case study in Beijing

Epidemics are dangerous and difficult to prevent and control, especially in urban areas. Clarifying the correlation between the COVID-19 Outbreak Frequency and the urban spatial environment may help improve cities' ability to respond to such public health emergencies. In this study, we firstly analyzed the spatial distribution characteristics of COVID-19 Outbreak Frequency by correlating the geographic locations of COVID-19 epidemic-affected neighborhoods in the city of Beijing with the time point of onset. Secondly, we created a geographically weighted regression model combining the COVID-19 Outbreak Frequency with the external spatial environmental elements of the city. Thirdly, different grades of epidemic-affected neighborhoods in the study area were classified according to the clustering analysis results. Finally, the correlation between the COVID-19 Outbreak Frequency and the internal spatial environmental elements of different grades of neighborhoods was investigated using a binomial logistic regression model. The study yielded the following results. (i) Epidemic outbreak frequency was evidently correlated with the urban external spatial environment, among building density, volume ratio, density of commercial facilities, density of service facilities, and density of transportation facilities were positively correlated with COVID-19 Outbreak Frequency, while water and greenery coverage was negatively correlated with it. (ii) The correlation between COVID-19 Outbreak Frequency and the internal spatial environmental elements of neighborhoods of different grades differed. House price and the number of households were positively correlated with the COVID-19 Outbreak Frequency in low-end neighborhoods, while the number of households was positively correlated with the COVID-19 Outbreak Frequency in mid-end neighborhoods. In order to achieve spatial justice, society should strive to address the inequality phenomena of income gaps and residential differentiation, and promote fair distribution of spatial environments.

Spatial co-location patterns between early COVID-19 risk and urban facilities: a case study of Wuhan, China

Introduction

COVID-19, being a new type of infectious disease, holds significant implications for scientific prevention and control to understand its spatiotemporal transmission process. This study examines the diverse spatial patterns of COVID-19 within Wuhan by analyzing early case data alongside urban infrastructure information.

Methods

Through co-location analysis, we assess both local and global spatial risks linked to the epidemic. In addition, we use the Geodetector, identifying facilities displaying unique spatial risk characteristics, revealing factors contributing to heightened risk.

Results

Our findings unveil a noticeable spatial distribution of COVID-19 in the city, notably influenced by road networks and functional zones. Higher risk levels are observed in the central city compared to its outskirts. Specific facilities such as parking, residence, ATM, bank, entertainment, and hospital consistently exhibit connections with COVID-19 case sites. Conversely, facilities like subway station, dessert restaurant, and movie theater display a stronger association with case sites as distance increases, hinting at their potential as outbreak focal points.

Discussion

Despite our success in containing the recent COVID-19 outbreak, uncertainties persist regarding its origin and initial spread. Some experts caution that with increased human activity, similar outbreaks might become more frequent. This research provides a comprehensive analytical framework centered on urban facilities, contributing quantitatively to understanding their impact on the spatial risks linked with COVID-19 outbreaks. It enriches our understanding of the interconnectedness between urban facility distribution and transportation flow, affirming and refining the distance decay law governing infectious disease risks. Furthermore, the study offers practical guidance for post-epidemic urban planning, promoting the development of safer urban environments resilient to epidemics. It equips government bodies with a reliable quantitative analysis method for more accurately predicting and assessing infectious disease risks. In conclusion, this study furnishes both theoretical and empirical support for tailoring distinct strategies to prevent and control COVID-19 epidemics.

Spatial variations of the third and fourth COVID-19 waves in Hong Kong: A comparative study using built environment and socio-demographic characteristics

Since the first confirmed case was reported in January 2020, Hong Kong has experienced multiple waves of COVID-19 outbreaks. Recent literature has explored the spatial patterns of disease incidence and their relationships with the built environment and demographic characteristics. Nonetheless, few studies aim at the comparative patterns of different epidemic waves occurring in the same spatial context. This study analyses spatial patterns of the third and fourth COVID-19 epidemic waves and then evaluates the spatial relationship between case incidence and built environment and socio-demographic characteristics. By collecting local-related cases, this study incorporates a two-fold analytical strategy: (1) Using rank-size distribution and log-odd ratio to depict the spatial pattern of COVID-19 incidence rates; (2) through global and local regression models, investigating incidence's associations with the urban built environment and socio-demographic characteristics. The results reveal that the two different epidemic waves have far distinct spatial tendencies to their infection risk factors, reflecting location-specific associations with the built environments and socio-demographics. Collectively, we discover that the third and fourth COVID-19 waves are likely associated with residential context and urban activities, respectively. Practical implications are discussed that would be of interest to policymakers and health professionals.

Spatial autocorrelation and heterogenicity of demographic and healthcare factors in the five waves of COVID-19 epidemic in Thailand

A study of 2,569,617 Thailand citizens diagnosed with COVID-19 from January 2020 to March 2022 was conducted with the aim of identifying the spatial distribution pattern of incidence rate of COVID-19 during its five main waves in all 77 provinces of the country. Wave 4 had the highest incidence rate (9,007 cases per 100,000) followed by the Wave 5, with 8,460 cases per 100,000. We also determined the spatial autocorrelation between a set of five demographic and health care factors and the spread of the infection within the provinces using Local Indicators of Spatial Association (LISA) and univariate and bivariate analysis with Moran's I. The spatial autocorrelation between the variables examined and the incidence rates was particularly strong during the waves 3-5. All findings confirmed the existence of spatial autocorrelation and heterogenicity of COVID-19 with the distribution of cases with respect to one or several of the five factors examined. The study identified significant spatial autocorrelation with regard to the COVID-19 incidence rate with these variables in all five waves. Depending on which province that was investigated, strong spatial autocorrelation of the High-High pattern was observed in 3 to 9 clusters and of the Low-Low pattern in 4 to 17 clusters, whereas negative spatial autocorrelation was observed in 1 to 9 clusters of the High-Low pattern and in 1 to 6 clusters of Low-High pattern. These spatial data should support stakeholders and policymakers in their efforts to prevent, control, monitor and evaluate the multidimensional determinants of the COVID-19 pandemic.

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.

COVID-19 transmission risk in Surabaya and Sidoarjo: an inhomogeneous marked Poisson point process approach

Understanding the spatio-temporal dynamics of COVID-19 transmission is necessary to plan better strategies for controlling the spread of the disease. However, only a few studies explore the COVID-19 transmission risk over a fine spatial resolution while considering relevant spatial and temporal factors. To this aim, we consider an inhomogeneous marked Poisson point process model to assess COVID-19 transmission risk using data of home addresses of confirmed cases, in relation to locations of sources of crowd (enterprise, market, and place of worship) and population density in Surabaya and Sidoarjo, Indonesia. Our marked model is able to analyze how the spatial covariates are varying with time, helping authorities to evaluate the information of covariates depending on the period in which restrictions are taking place. Our results show that enterprise, place of worship, and population densities have significant impact to the transmission risk in Surabaya and Sidoarjo. We finally provide predicted risk maps which provide additional information based on the demographic-based risk analysis to help conduct more efficient testing, tracing, and vaccination programs.

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.

The evaluation of urban spatial quality and utility trade-offs for Post-COVID working preferences: a case study of Hong Kong

The formation of urban districts and the appeal of densely populated areas reflect a spatial equilibrium in which workers migrate to locations with greater urban vitality but diminished environmental qualities. However, the pandemic and associated health concerns have accelerated remote and hybrid work modes, altered people's sense of place and appreciation of urban density, and transformed perceptions of desirable places to live and work. This study presents a systematic method for evaluating the trade-offs between perceived urban environmental qualities and urban amenities by analysing post-pandemic urban residence preferences. By evaluating neighbourhood Street View Imagery (SVI) and urban amenity data, such as park sizes, the study collects subjective opinions from surveys on two working conditions (work-from-office or from-home). On this basis, several Machine Learning (ML) models were trained to predict the preference scores for both work modes. In light of the complexity of work-from-home preferences, the results demonstrate that the method predicts work-from-office scores with greater precision. In the post-pandemic era, the research aims to shed light on the development of a valuable instrument for driving and evaluating urban design strategies based on the potential self-organisation of work-life patterns and social profiles in designated neighbourhoods.

Inconsistent Association between Perceived Air Quality and Self-Reported Respiratory Symptoms: A Pilot Study and Implications for Environmental Health Studies

As public awareness of air quality issues becomes heightened, people's perception of air quality is drawing increasing academic interest. However, data about people's perceived environment need scrutiny before being used in environmental health studies. In this research, we examine the associations between people's perceptions of air quality and their self-reported respiratory health symptoms. Spearman rank correlation coefficients were estimated and the associations were tested at the 95% confidence level. Using data collected from participants in two representative communities in Hong Kong, the results indicate a weak but significant association between people's perceived air quality and their self-reported frequency of respiratory symptoms. However, there are disparities in such an association between different genders, age groups, household income levels, education levels, marital statuses, and geographic contexts. The most striking disparities are between genders and geographic contexts. Multiple significant associations were observed for male participants (correlation coefficients: 0.169~0.205, p-values: 0.021~0.049), while none was observed for female participants. Besides, multiple significant associations were observed in the old town (correlation coefficients: 0.164~0.270, p-values: 0.003~0.048), while none was observed in the new town. The results have significant implications for environmental health research using social media data, whose reliability depends on the association between people's perceived or actual environments and their health outcomes. Since inconsistent associations exist between different groups of people, researchers need to scrutinize social media data before using them in health studies.

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.

Changes in mobility amid the COVID-19 pandemic in Sapporo City, Japan: An investigation through the relationship between spatiotemporal population density and urban facilities

By the end of 2021, the Omicron variant of coronavirus disease 2019 had become the dominant cause of a worldwide pandemic crisis. This demands a deeper analysis to support policy makers in creating interventions that not only protect people from the pandemic but also remedy its negative effects on the economy. Thus, this study investigated people's mobility changes through the relationship between spatiotemporal population density and urban facilities. Results showed that places related to daily services, restaurants, commercial areas, and offices experienced decreased visits, with the highest decline belonging to commercial facilities. Visits to health care and production facilities were stable on weekdays but increased on holidays. Educational institutions' visits decreased on weekdays but increased on holidays. People's visits to residential housing and open spaces increased, with the rise in residential housing visits being more substantial. The results also confirmed that policy interventions (e.g., declaration of emergency and upgrade of restriction level) have a great impact on people's mobility in the short term. The findings would seem to indicate that visit patterns at service and restaurant places decreased least during the pandemic. The analysis outcomes suggest that policy makers should pay more attention to risk perception enhancement as a long-term measure. Furthermore, the study clarified the population density of each facility type in a time series. Improving model performance would be promising for tracking and predicting the spread of 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.

What have we learned about socioeconomic inequalities in the spread of COVID-19? A systematic review

This article aims to provide a better understanding of the associations between groups of socioeconomic variables and confirmed cases of COVID-19. The focus is on cross-continental differences of reported positive, negative, unclear, or no associations. A systematic review of the literature is conducted on the Web of Science and SCOPUS databases. Our search identifies 314 eligible studies published on or before 31 December 2021. We detect nine groups of frequently used socioeconomic variables and results are presented by region of the world (Africa, Asia, Europe, Middle East, North American and South America). The review expands to describe the most used statistical and modelling techniques as well as inclusion of additional dimensions such as demographic, healthcare weather and mobility. Meanwhile findings agree on the generalized positive impact of population density, per capita GDP and urban areas on transmission of infections, contradictory results have been found concerning to educational level and income.

Relationships between building attributes and COVID-19 infection in London

In the UK, all domestic COVID-19 restrictions have been removed since they were introduced in March 2020. After illustrating the spatial-temporal variations in COVID-19 infection rates across London, this study then particularly aimed to examine the relationships of COVID-19 infection rates with building attributes, including building density, type, age, and use, since previous studies have shown that the built environment plays an important role in public health. Multisource data from national health services and the London Geomni map were processed with GIS techniques and statistically analysed. From March 2020 to April 2022, the infection rate of COVID-19 in London was 3,159.28 cases per 10,000 people. The spatial distribution across London was uneven, with a range from 1,837.88 to 4,391.79 per 10,000 people. During this period, it was revealed that building attributes played a significant role in COVID-19 infection. It was noted that higher building density areas had lower COVID-19 infection rates in London. Moreover, a higher percentage of historic or flat buildings tended to lead to a decrease in infection rates. In terms of building use, the rate of COVID-19 infection tended to be lower in public buildings and higher in residential buildings. Variations in the infection rate were more sensitive to building type; in particular, the percentage of residents living in flats contributed the most to variations in COVID-19 infection rates, with a value of 2.3%. This study is expected to provide support for policy and practice towards pandemic-resilient architectural design.

Simulation and forecasting models of COVID-19 taking into account spatio-temporal dynamic characteristics: A review

The COVID-19 epidemic has caused more than 6.4 million deaths to date and has become a hot topic of interest in different disciplines. According to bibliometric analysis, more than 340,000 articles have been published on the COVID-19 epidemic from the beginning of the epidemic until recently. Modeling infectious diseases can provide critical planning and analytical tools for outbreak control and public health research, especially from a spatio-temporal perspective. However, there has not been a comprehensive review of the developing process of spatio-temporal dynamic models. Therefore, the aim of this study is to provide a comprehensive review of these spatio-temporal dynamic models for dealing with COVID-19, focusing on the different model scales. We first summarized several data used in the spatio-temporal modeling of the COVID-19, and then, through literature review and summary, we found that the existing COVID-19 spatio-temporal models can be divided into two categories: macro-dynamic models and micro-dynamic models. Typical representatives of these two types of models are compartmental and metapopulation models, cellular automata (CA), and agent-based models (ABM). Our results show that the modeling results are not accurate enough due to the unavailability of the fine-grained dataset of COVID-19. Furthermore, although many models have been developed, many of them focus on short-term prediction of disease outbreaks and lack medium- and long-term predictions. Therefore, future research needs to integrate macroscopic and microscopic models to build adaptive spatio-temporal dynamic simulation models for the medium and long term (from months to years) and to make sound inferences and recommendations about epidemic development in the context of medical discoveries, which will be the next phase of new challenges and trends to be addressed. In addition, there is still a gap in research on collecting fine-grained spatial-temporal big data based on cloud platforms and crowdsourcing technologies to establishing world model to battle the epidemic.

Examining the Influence of Housing Conditions and Daily Greenspace Exposure on People's Perceived COVID-19 Risk and Distress

Many people have worried about COVID-19 infection, job loss, income reduction, and family conflict during the COVID-19 pandemic. Some social groups may be particularly vulnerable due to their residential neighborhoods and daily activities. On the other hand, people's daily exposure to greenspace offers promising pathways for reducing these worries associated with COVID-19. Using data collected with a questionnaire and a two-day activity diary from two typical neighborhoods in Hong Kong, this study examines how people's housing conditions and daily greenspace exposure affect their perceived COVID-19 risk and distress (i.e., worries about job loss, income reduction, and family conflict) during the pandemic. First, the study compares people's perceived COVID-19 risk and distress based on their residential neighborhoods. Further, it examines the associations between people's perceived COVID-19 risk and distress with their housing conditions and daily greenspace exposure using ordinal logistic regression models. The results indicate that living in a high-risk neighborhood, being married, renting a residential unit, and living in a large household are significantly associated with a higher neighborhood-based perceived COVID-19 risk and distress during the pandemic. In addition, people also reported lower mobility-based perceived COVID-19 risk when compared to their neighborhood-based perceived COVID-19 risk, while they still have a high perceived COVID-19 risk in their occupational venues if they have to work in a high-risk district (e.g., Kowloon). Lastly, daily greenspace exposure (i.e., woodland) could reduce people's perceived COVID-19 risk and distress. These results have important implications for the public health authority when formulating the measures during the COVID-19 pandemic.

The impact of geo-environmental factors on global COVID-19 transmission: A review of evidence and methodology

Studies on Coronavirus Disease 2019 (COVID-19) transmission indicate that geo-environmental factors have played a significant role in the global pandemic. However, there has not been a systematic review on the impact of geo-environmental factors on global COVID-19 transmission in the context of geography. As such, we reviewed 49 well-chosen studies to reveal the impact of geo-environmental factors (including the natural environment and human activity) on global COVID-19 transmission, and to inform critical intervention strategies that could mitigate the worldwide effects of the pandemic. Existing studies frequently mention the impact of climate factors (e.g., temperature and humidity); in contrast, a more decisive influence can be achieved by human activity, including human mobility, health factors, and non-pharmaceutical interventions (NPIs). The above results exhibit distinct spatiotemporal heterogeneity. The related analytical methodology consists of sensitivity analysis, mathematical modeling, and risk analysis. For future studies, we recommend highlighting geo-environmental interactions, developing geographically statistical models for multiple waves of the pandemic, and investigating NPIs and care patterns. We also propose four implications for practice to combat global COVID-19 transmission.

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.

The fine-scale associations between socioeconomic status, density, functionality, and spread of COVID-19 within a high-density city

BACKGROUND

Motivated by the need for precise epidemic control and epidemic-resilient urban design, this study aims to reveal the joint and interactive associations between urban socioeconomic, density, connectivity, and functionality characteristics and the COVID-19 spread within a high-density city. Many studies have been made on the associations between urban characteristics and the COVID-19 spread, but there is a scarcity of such studies in the intra-city scale and as regards complex joint and interactive associations by using advanced machine learning approaches.

METHODS

Differential-evolution-based association rule mining was used to investigate the joint and interactive associations between the urban characteristics and the spatiotemporal distribution of COVID-19 confirmed cases, at the neighborhood scale in Hong Kong. The associations were comparatively studied for the distribution of the cases in four waves of COVID-19 transmission: before Jun 2020 (wave 1 and 2), Jul-Oct 2020 (wave 3), and Nov 2020-Feb 2021 (wave 4), and for local and imported confirmed cases.

RESULTS

The first two waves of COVID-19 were found mainly characterized by higher-socioeconomic-status (SES) imported cases. The third-wave outbreak concentrated in densely populated and usually lower-SES neighborhoods, showing a high risk of within-neighborhood virus transmissions jointly contributed by high density and unfavorable SES. Starting with a super-spread which considerably involved high-SES population, the fourth-wave outbreak showed a stronger link to cross-neighborhood transmissions driven by urban functionality. Then the outbreak diffused to lower-SES neighborhoods and interactively aggravated the within-neighborhood pandemic transmissions. Association was also found between a higher SES and a slightly longer waiting period (i.e., the period from symptom onset to diagnosis of symptomatic cases), which further indicated the potential contribution of higher-SES population to the pandemic transmission.

CONCLUSIONS

The results of this study may provide references to developing precise anti-pandemic measures for specific neighborhoods and virus transmission routes. The study also highlights the essentiality of reliving co-locating overcrowdedness and unfavorable SES for developing epidemic-resilient compact cities, and the higher obligation of higher-SES population to conform anti-pandemic policies.

The Geographical Distribution and Influencing Factors of COVID-19 in China

The study of the spatial differentiation of COVID-19 in cities and its driving mechanism is helpful to reveal the spatial distribution pattern, transmission mechanism and diffusion model, and evolution mechanism of the epidemic and can lay the foundation for constructing the spatial dynamics model of the epidemic and provide theoretical basis for the policy design, spatial planning and implementation of epidemic prevention and control and social governance. Geodetector (Origin version, Beijing, China) is a great tool for analysis of spatial differentiation and its influencing factors, and it provides decision support for differentiated policy design and its implementation in executing the city-specific policies. Using factor detection and interaction analysis of Geodetector, 15 indicators of economic, social, ecological, and environmental dimensions were integrated, and 143 cities were selected for the empirical research in China. The research shows that, first of all, risks of both infection and death show positive spatial autocorrelation, but the geographical distribution of local spatial autocorrelation differs significantly between the two. Secondly, the inequalities in urban economic, social, and residential environments interact with COVID-19 spatial heterogeneity, with stronger explanatory power especially when multidimensional inequalities are superimposed. Thirdly, the spatial distribution and spread of COVID-19 are highly spatially heterogeneous and correlated due to the complex influence of multiple factors, with factors such as Area of Urban Construction Land, GDP, Industrial Smoke and Dust Emission, and Expenditure having the strongest influence, the factors such as Area of Green, Number of Hospital Beds and Parks, and Industrial NOx Emissions having unignorable influence, while the factors such as Number of Free Parks and Industrial Enterprises, Per-GDP, and Population Density play an indirect role mainly by means of interaction. Fourthly, the factor interaction effect from the infected person's perspective mainly shows a nonlinear enhancement effect, that is, the joint influence of the two factors is greater than the sum of their direct influences; but from the perspective of the dead, it mainly shows a two-factor enhancement effect, that is, the joint influence of the two factors is greater than the maximum of their direct influences but less than their sum. Fifthly, some suggestions are put forward from the perspectives of building a healthy, resilient, safe, and smart city, providing valuable reference and decision basis for city governments to carry out differentiated policy design.

Management of and Revitalization Strategy for Megacities Under Major Public Health Emergencies: A Case Study of Wuhan

The outbreak of the COVID-19 pandemic in late 2019 has meant an uphill battle for city management. However, due to deficiencies in facilities and management experience, many megacities are less resilient when faced with such major public health events. Therefore, we chose Wuhan for a case study to examine five essential modules of urban management relevant to addressing the pandemic: (1) the medical and health system, (2) lifeline engineering and infrastructure, (3) community and urban management, (4) urban ecology and (5) economic development. The experience and deficiencies of each module in fighting the pandemic are analyzed, and strategies for revitalization and sustainable development in the future are proposed. The results show that in response to large-scale public health events, a comprehensive and coordinated medical system and good urban ecology can prevent the rapid spread of the epidemic. Additionally, good infrastructure and community management can maintain the operation of the city under the pandemic, and appropriate support policies are conducive to the recovery and development of the urban economy. These precedents provide insights and can serve as a reference for how to change the course of the pandemic in megacities that are still at risk, and they provide experience for responding to other pandemics.

Using an Eigenvector Spatial Filtering-Based Spatially Varying Coefficient Model to Analyze the Spatial Heterogeneity of COVID-19 and Its Influencing Factors in Mainland China

The COVID-19 pandemic has led to many deaths and economic disruptions across the world. Several studies have examined the effect of corresponding health risk factors in different places, but the problem of spatial heterogeneity has not been adequately addressed. The purpose of this paper was to explore how selected health risk factors are related to the pandemic infection rate within different study extents and to reveal the spatial varying characteristics of certain health risk factors. An eigenvector spatial filtering-based spatially varying coefficient model (ESF-SVC) was developed to find out how the influence of selected health risk factors varies across space and time. The ESF-SVC was able to take good control of over-fitting problems compared with ordinary least square (OLS), eigenvector spatial filtering (ESF) and geographically weighted regression (GWR) models, with a higher adjusted R2 and lower cross validation RMSE. The impact of health risk factors varied as the study extent changed: In Hubei province, only population density and wind speed showed significant spatially constant impact; while in mainland China, other factors including migration score, building density, temperature and altitude showed significant spatially varying impact. The influence of migration score was less contributive and less significant in cities around Wuhan than cities further away, while altitude showed a stronger contribution to the decrease of infection rates in high altitude cities. The temperature showed mixed correlation as time passed, with positive and negative coefficients at 2.42 °C and 8.17 °C, respectively. This study could provide a feasible path to improve the model fit by considering the problem of spatial autocorrelation and heterogeneity that exists in COVID-19 modeling. The yielding ESF-SVC coefficients could also provide an intuitive method for discovering the different impacts of influencing factors across space in large study areas. It is hoped that these findings improve public and governmental awareness of potential health risks and therefore influence epidemic control strategies.

Comparing the space-time patterns of high-risk areas in different waves of COVID-19 in Hong Kong

This study compares the space-time patterns and characteristics of high-risk areas of COVID-19 transmission in Hong Kong between January 23 and April 14 (the first and second waves) and between July 6 and August 29 (the third wave). Using space-time scan statistics and the contact tracing data of individual confirmed cases, we detect the clusters of residences of, and places visited by, both imported and local cases. We also identify the built-environment and demographic characteristics of the high-risk areas during different waves of COVID-19. We find considerable differences in the space-time patterns and characteristics of high-risk residential areas between waves. However, venues and buildings visited by the confirmed cases in different waves have similar characteristics. The results can inform policymakers to target mitigation measures in high-risk areas and at vulnerable groups, and provide guidance to the public to avoid visiting and conducting activities at high-risk places.

Local Characteristics Related to SARS-CoV-2 Transmissions in the Seoul Metropolitan Area, South Korea

The Seoul metropolitan area is one of the most populated metropolitan areas in the world; hence, Seoul's COVID-19 cases are highly concentrated. This study identified local demographic and socio-economic characteristics that affected SARS-CoV-2 transmission to provide locally targeted intervention policies. For the effective control of outbreaks, locally targeted intervention policies are required since the SARS-CoV-2 transmission process is heterogeneous over space. To identify the local COVID-19 characteristics, this study applied the geographically weighted lasso (GWL). GWL provides local regression coefficients, which were used to account for the spatial heterogeneity of SARS-CoV-2 outbreaks. In particular, the GWL pinpoints statistically significant regions with specific local characteristics. The applied explanatory variables involving demographic and socio-economic characteristics that were associated with higher SARS-CoV-2 transmission in the Seoul metropolitan area were as follows: young adults (19~34 years), older population, Christian population, foreign-born population, low-income households, and subway commuters. The COVID-19 case data were classified into three periods: the first period (from January 2020 to July 2021), the second period (from August to November 2020), and the third period (from December 2020 to February 2021), and the GWL was fitted for the entire period (from January 2020 to February 2021). The result showed that young adults, the Christian population, and subway commuters were the most significant local characteristics that influenced SARS-CoV-2 transmissions in the Seoul metropolitan area.

"What should be computed" for supporting post-pandemic recovery policymaking? A life-oriented perspective

The COVID-19 pandemic has caused various impacts on people's lives, while changes in people's lives have shown mixed effects on mitigating the spread of the SARS-CoV-2 virus. Understanding how to capture such two-way interactions is crucial, not only to control the pandemic but also to support post-pandemic urban recovery policies. As suggested by the life-oriented approach, the above interactions exist with respect to a variety of life domains, which form a complex behavior system. Through a review of the literature, this paper first points out inconsistent evidence about behavioral factors affecting the spread of COVID-19, and then argues that existing studies on the impacts of COVID-19 on people's lives have ignored behavioral co-changes in multiple life domains. Furthermore, selected uncertain trends of people's lives for the post-pandemic recovery are described. Finally, this paper concludes with a summary about "what should be computed?" in Computational Urban Science with respect to how to catch up with delays in the SDGs caused by the COVID-19 pandemic, how to address digital divides and dilemmas of e-society, how to capture behavioral co-changes during the post-pandemic recovery process, and how to better manage post-pandemic recovery policymaking processes.

The superspreading places of COVID-19 and the associated built-environment and socio-demographic features: A study using a spatial network framework and individual-level activity data

Previous studies observed that most COVID-19 infections were transmitted by a few individuals at a few high-risk places (e.g., bars or social gathering venues). These individuals, often called superspreaders, transmit the virus to an unexpectedly large number of people. Further, a small number of superspreading places (SSPs) where this occurred account for a large number of COVID-19 transmissions. In this study, we propose a spatial network framework for identifying the SSPs that disproportionately spread COVID-19. Using individual-level activity data of the confirmed cases in Hong Kong, we first identify the high-risk places in the first four COVID-19 waves using the space-time kernel density method (STKDE). Then, we identify the SSPs among these high-risk places by constructing spatial networks that integrate the flow intensity of the confirmed cases. We also examine what built-environment and socio-demographic features would make a high-risk place to more likely become an SSP in different waves of COVID-19 by using regression models. The results indicate that some places had very high transmission risk and suffered from repeated COVID-19 outbreaks over the four waves, and some of these high-risk places were SSPs where most (about 80%) of the COVID-19 transmission occurred due to their intense spatial interactions with other places. Further, we find that high-risk places with dense urban renewal buildings and high median monthly household rent-to-income ratio have higher odds of being SSPs. The results also imply that the associations between built-environment and socio-demographic features with the high-risk places and SSPs are dynamic over time. The implications for better policymaking during the COVID-19 pandemic are discussed.

Study of SARS-CoV-2 transmission in urban environment by questionnaire and modeling for sustainable risk control

Caused by SARS-CoV-2, COVID-19 has become a severe threaten to society and human health, its epidemic control emerges as long-term issue. A sustainable epidemic and environmental transmission risk control (SEERC) in urban area is urgently needed. This work aims to conduct a new investigation on the transmission risk of SARS-COV-2 as virus/hazardous material through various environmental medias, routes and regions in the entirely urban area for guiding the SEERC. Specifically, 5 routes in 28 regions (totally 140 scenarios) are considered. For a new perspective, the risk evaluation is conducted by the quantification of frontline medicals staffs' valuable experience in this work. 207 specialists responsible for the treatment of over 9000 infected patients are involved. The result showed that degree of risk was in the order of breath>contact-to-object>contact-to-human>intake>unknown. The modeling suggested source control as the prior measure for epidemic control. The combination of source control & mask wearing showed high efficiency in SEERC. The homeworking policy needed to cooperate with activity limitation to perform its efficiency. Subsequently, a new plan for SEERC was discussed. This work delivered significant information to researchers and decision makers for the further development of sustainable control for SARS-COV-2 spreading and COVID-19 epidemic.

Geo-clusters and socio-demographic profiles at village-level associated with COVID-19 incidence in the metropolitan city of Jakarta: An ecological study

The Special Capital Region of Jakarta is the epicentre of the transmission of COVID‐19 in Indonesia. However, much remains unknown about the spatial and temporal patterns of COVID‐19 incidence and related socio‐demographic factors explaining the variations of COVID‐19 incidence at local level. COVID‐19 cases at the village level of Jakarta from March 2020 to June 2021 were analyzed from the local public COVID‐19 dashboard. Global and local spatial clustering of COVID‐19 incidence was examined using the Moran's I and local Moran analysis. Socio‐demographic profiles of identified hotspots were elaborated. The association between village characteristics and COVID‐19 incidence was evaluated. The COVID‐19 incidence was significantly clustered based on the geographical village level (Moran's I = 0.174; p = .002). Seventeen COVID‐19 high‐risk clusters were found and dynamically shifted over the study period. The proportion of people aged 20–49 (incidence rate ratio [IRR] = 1.016; 95% confidence interval [CI]: 1.012–1.019), proportion of elderly (≥50 years) (IRR = 1.045; 95% CI = 1.041–1.050), number of households (IRR = 1.196; 95% CI = 1.193–1.200), access to metered water for washing, and the main occupation of the residents were village level socio‐demographic factors associated with the risk of COVID‐19. Targeted public health responses such as restriction, improved testing and contact tracing, and improved access to health services for those vulnerable populations are essential in areas with high‐risk COVID‐19.

Comparison of Public Health Containment Measures of COVID-19 in China and India

Objective

This study aimed to make a comparative analysis of the public health containment measures between China and India, explore the causes of the serious COVID-19 epidemic in India, and eventually to improve global infectious disease control.

Methods

We extracted publicly available data from official websites, summarized the containment measures implemented in China and India, and assessed their effectiveness.

Results

China has responded to the COVID-19 outbreak with strict public health containment measures, including lockdown of Wuhan city, active case tracing, and large-scale testing, ultimately preventing a large increase in daily new cases and maintaining a low mortality rate per million population (as of May 5, 2021, daily new cases were 11 and mortality rate per million population was 3.37). India, although imposing a national lockdown to control the pandemic, has not implemented strict testing, tracking, and quarantine measures due to the overburdened healthcare system. Combined with massive lockdown, it has accelerated human mobility and exacerbated the epidemic, resulting in a rapid increase in daily new cases and a high mortality rate per million population (as of May 5, 2021, daily new cases were 412,431 and mortality rate per million population was 166.79).

Conclusion

China and India implemented public health containment measures to contain the spread of the COVID-19 pandemic based on their national situations. Meanwhile, daily new cases and mortality of COVID-19 also were affected by environmental and socioeconomic. Countries make a comprehensive strategy not only in terms of the biological, pharmaceutical, health, and sanitation sectors but also based on sustainability science and environmental science.

Assessing the Country-Level Excess All-Cause Mortality and the Impacts of Air Pollution and Human Activity during the COVID-19 Epidemic

The impact of Coronavirus Disease 2019 (COVID-19) on cause-specific mortality has been investigated on a global scale. However, less is known about the excess all-cause mortality and air pollution-human activity responses. This study estimated the weekly excess all-cause mortality during COVID-19 and evaluated the impacts of air pollution and human activities on mortality variations during the 10th to 52nd weeks of 2020 among sixteen countries. A SARIMA model was adopted to estimate the mortality benchmark based on short-term mortality during 2015-2019 and calculate excess mortality. A quasi-likelihood Poisson-based GAM model was further applied for air pollution/human activity response evaluation, namely ground-level NO2 and PM2.5 and the visit frequencies of parks and workplaces. The findings showed that, compared with COVID-19 mortality (i.e., cause-specific mortality), excess all-cause mortality changed from -26.52% to 373.60% during the 10th to 52nd weeks across the sixteen countries examined, revealing higher excess all-cause mortality than COVID-19 mortality in most countries. For the impact of air pollution and human activities, the average country-level relative risk showed that one unit increase in weekly NO2, PM2.5, park visits and workplace visits was associated with approximately 1.54% increase and 0.19%, 0.23%, and 0.23% decrease in excess all-cause mortality, respectively. Moreover, compared with the impact on COVID-19 mortality, the relative risks of weekly NO2 and PM2.5 were lower, and the relative risks of weekly park and workplace visits were higher for excess all-cause mortality. These results suggest that the estimation based on excess all-cause mortality reduced the potential impact of air pollution and enhanced the influence of human activities compared with the estimation based on COVID-19 mortality.

Demographic and socioeconomic determinants of COVID-19 across Oman - A geospatial modelling approach

Local, bivariate relationships between coronavirus 2019 (COVID-19) infection rates and a set of demographic and socioeconomic variables were explored at the district level in Oman. To limit multicollinearity a principal component analysis was conducted, the results of which showed that three components together could explain 65% of the total variance that were therefore subjected to further study. Comparison of a generalized linear model (GLM) and geographically weighted regression (GWR) indicated an improvement in model performance using GWR (goodness of fit=93%) compared to GLM (goodness of fit=86%). The local coefficient of determination (R2) showed a significant influence of specific demographic and socioeconomic factors on COVID-19, including percentages of Omani and non-Omani population at various age levels; spatial interaction; population density; number of hospital beds; total number of households; purchasing power; and purchasing power per km2. No direct correlation between COVID- 19 rates and health facilities distribution or tobacco usage. This study suggests that Poisson regression using GWR and GLM can address unobserved spatial non-stationary relationships. Findings of this study can promote current understanding of the demographic and socioeconomic variables impacting the spatial patterns of COVID-19 in Oman, allowing local and national authorities to adopt more appropriate strategies to cope with this pandemic in the future and also to allocate more effective prevention resources.