Living environment matters: Unravelling the spatial clustering of COVID-19 hotspots in Kolkata megacity, India

How hydrological components of urban blue space influence the thermal milieu?

Present study examines the possible improvement of thermal discomfort mitigation. Unlike prior researches, which focused primarily on cooling effects of urban blue space, this study, instead of physical presence of blue space considers its hydrological components. The aim of the study is to better understand the role hydrological components like water consistency depth etc. In temperature regulation. The work uses field surveys and modeling to demonstrate how these hydrological factors influence the cooling effect of blue space, providing insights on urban thermal management. To fulfill the purpose, spatial association of hydrological components blue space with its thermal environment and cooling effects was assessed. The control of hydrological components on the surrounding air temperature was examined by conducting case studies. RESULTS: reveals greater hydro-duration, deeper water, and higher Water Presence Frequency (WPF) produce greater cooling effects. The study demonstrates a favorable correlation between hydrological richness and temperature reduction. The study also analyzes how land use and wetland size affect temperature, emphasizing the significance of hydrological conservation and restoration for successful temperature mitigation. Due to their hydrology, larger wetlands are able to moderate temperature to some extent, whereas smaller, fragmented wetlands being hydrologically poor are not so influential in this regard. With these results, the present study reaches beyond to the general understanding regarding the cooling effects of the urban blue spaces. While the previous studies primarily focused on estimating the cooling effect of urban blue space, the current one shows its synchronization with the hydrological characteristics. Novelty also entrusts here, through the modeling and field survey current study demonstrates deeper and consistent water coverage in the urban blue space for maximum period of a year pronounces the cooling effect. In addition, in this cooling effect, the role of land use which is a strong determinant of many aspects of the urban environment is also highlighted. Since all these findings define specific hydrological feature, the study has several practical implications. Mare restoration of urban blue space is not enough to mitigate the thermal discomfort. In order to optimize the cooling effect, the conservation of the hydrological richness is essential. The hydrological richness of the smaller wetlands and the edge of the larger wetlands is to be improved. The connection of these wetlands with the adjacent mighty may strengthen the hydrology. The vegetation was found to promote the cooling effect whereas shorter building helped in spreading the cooling effect. Such finding drives to incorporate the blue space with the green infrastructure along with restricting the building height atleast at the edge of the blue space.

Demographic characteristics and mental health condition of Tehran Municipality employees during the COVID-19 pandemic

BACKGROUND

Coronavirus disease 2019 (COVID-19) has become a global health issue with various effects on the physical and mental state of the societies. The aim of this study was to identify the demographic characteristics and mental health condition of Tehran Municipality employees during the COVID-19 pandemic.

METHODS

This cross-sectional study was performed on Tehran Municipality employees in 2020-2021. Participants were selected using stratified random sampling and were divided into COVID-19 and uninfected groups. Demographic characteristics, COVID-19 risk behaviors, General Health Questionnaire-28 (GHQ-28), and Well- Being Social Inventory were filled for all participants.

RESULTS

A total of 510 participants (363 uninfected participants and 147 participants with COVID-19) were evaluated. The prevalence of female gender was significantly higher in COVID-19 group compared to uninfected group (p < 0.001). There was a significant difference between groups in terms of education level (p < 0.001), prevalence of excess weight (p < 0.001), and working sector (p < 0.001). The uninfected group mainly had low contact with clients (p < 0.001) and few underlying diseases (p = 0.004) compared to the COVID-19 group. The mean GHQ-28 and Well- Being Social Inventory were significantly higher in the uninfected group compared to the COVID-19 group (p = 0.002 and p < 0.001, respectively). The prevalence of no and low contact level was significantly higher in the high infection cluster compared to moderate and low infection clusters (p = 0.024).

CONCLUSIONS

The findings of this study indicated that all workers should be educated about the significance of social distancing and follow the recommendations regardless of their level of contact with clients.

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.

Involving trained community health mediators in COVID-19 prevention measures. A process evaluation from Bremen, Germany

Objective

The objective was to assess the feasibility of incorporating trained community health mediators in COVID-19 prevention in a multicultural and disadvantaged setting in Bremen, Germany. Specifically, we aimed to develop and implement measures corresponding to the needs of the residents and to analyse the role of digital communication tools and sustainability factors of the health mediator approach.

Methods

A comprehensive process evaluation using 41 qualitative interviews with residents, mediator short surveys and group discussions, work documentation sheets, and a stakeholder workshop was carried out.

Results

Uncertainties due to changing regulations, a lack of trust and fear of potential side effects were major themes identified in the needs assessment. The eight mediators documented more than 1,600 contacts. Digital communication via Facebook was a useful tool, but personal contacts remained crucial for communicating with residents. The participatory approach, multilingualism and the flexibility to react to dynamic situations were identified as relevant factors for the success and sustainability of the health mediator approach.

Conclusion

Multilingual health mediators can facilitate contact with and dissemination of health information to different communities and also can play an important role in pandemic preparedness.

Study on the spatial decomposition of the infection probability of COVID-19

In the course of our observations of the transmission of COVID-19 around the world, we perceived substantial concern about imported cases versus cases of local transmission. This study, therefore, tries to isolate cases due to local transmission (also called community spread) from those due to externally introduced COVID-19 infection, which can be key to understanding the spread pattern of the pandemic. In particular, we offer a probabilistic perspective to estimate the scale of the outbreak at the epicenter of the COVID-19 epidemic with an environmental focus. First, this study proposes a novel explanation of the probability of COVID-19 cases in the local population of the target city, in which the chain of probability is based on the assumption of independent distribution. Then it conducts a spatial statistical analysis on the spread of COVID-19, using two model specifications to identify the spatial dependence, more commonly known as the spillover effect. The results are found to have strong spatial dependence. Finally, it confirms the significance of residential waste in the transmission of COVID-19, which indicates that the fight against COVID-19 requires us to pay close attention to environmental factors. The method shown in this study is critical and has high practical value, because it can be easily applied elsewhere and to other future pandemics.

Debates Paper: COVID-19 and urban informality: Exploring the implications of the pandemic for the politics of planning and inequality

The COVID-19 pandemic has highlighted a major contradiction in contemporary urban planning. This is the relationship between the entrepreneurial modes of urban politics that shape contemporary planning practice and the interrelated dynamics of economic precarity and informalisation of low-income communities that exacerbate contagion, and therefore enable pandemic spread. Through a review of literature on the urban dimensions of COVID-19, and on the historical relationship between pandemics and urban planning, we develop a framework for analysing the debates that are emerging around planning approaches to addressing contemporary pandemic risk in low-income, informalised communities. We argue that post-pandemic debates about urban planning responses are likely to take shape around three discourses that have framed approaches to addressing informalised communities under entrepreneurial urbanism - a revanchist approach based on territorial stigmatisation of spaces of the poor, an incrementalist approach premised on addressing the most immediate drivers of contagion, and a reformist approach that seeks to address the structural conditions that have produced economic precarity and shelter informality. We further argue that any effort to assess the political outfall of the COVID-19 pandemic in a given context needs to take an inter-scalar approach, analysing how debates over informality take shape at the urban and national scales.

Predicting onset risk of COVID-19 symptom to support healthy travel route planning in the new normal of long-term coexistence with SARS-CoV-2

Due to the increased outdoor transmission risk of new SARS-COV-2 variants, the health of urban residents in daily travel is being threatened. In the new normal of long-term coexistence with SARS-CoV-2, how to avoid being infected by SARS-CoV-2 in daily travel has become a key issue. Hence, a spatiotemporal solution has been proposed to assist healthy travel route planning. Firstly, an enhanced urban-community-scale geographic model was proposed to predict daily COVID-19 symptom onset risk by incorporating the real-time effective reproduction numbers, and daily population variation of fully vaccinated. On-road onset risk predictions in the next following days were then extracted for searching healthy routes with the least onset risk values. The healthy route planning was further implemented in a mobile application. Hong Kong, one of the representative highly populated cities, has been chosen as an example to apply the spatiotemporal solution. The application results in the four epidemic waves of Hong Kong show that based on the high accurate prediction of COVID-19 symptom onset risk, the healthy route planning could reduce people's exposure to the COVID-19 symptoms onset risk. To sum, the proposed solution can be applied to support the healthy travel of residents in more cities in the new normalcy.

Associations between institutional-social-ecological factors and COVID -19 case-fatality: Evidence from 134 countries using multiscale geographically weighted regression (MGWR)

During the period in which the Omicron coronavirus variant was rapidly spreading, the impact of the institutional-social-ecological dimensions on the case-fatality rate was rarely afforded attention. By adopting the diagnostic social-ecological system (SES) framework, the present paper aims to identify the impact of institutional-social-ecological factors on the case-fatality rate of COVID-19 in 134 countries and regions and test their spatial heterogeneity. Using statistical data from the Our World In Data website, the present study collected the cumulative case-fatality rate from 9 November 2021 to 23 June 2022, along with 11 country-level institutional-social-ecological factors. By comparing the goodness of fit of the multiple linear regression model and the multiscale geographically weighted regression (MGWR) model, the study demonstrated that the effects of SES factors exhibit significant spatial heterogeneity in relation to the case-fatality rate of COVID-19. After substituting the data into the MGWR model, six SES factors were identified with an R square of 0.470 based on the ascending effect size: COVID-19 vaccination policy, age dependency ratio, press freedom, gross domestic product (GDP), COVID-19 testing policy, and population density. The GWR model was used to test and confirm the robustness of the research results. Based on the analysis results, it is suggested that the world needs to meet four conditions to restore normal economic activity in the wake of the COVID-19 pandemic: (i) Countries should increase their COVID-19 vaccination coverage and maximize COVID-19 testing expansion. (ii) Countries should increase public health facilities available to provide COVID-19 treatment and subsidize the medical costs of COVID-19 patients. (iii) Countries should strictly review COVID-19 news reports and actively publicize COVID-19 pandemic prevention knowledge to the public through a range of media. (iv) Countries should adopt an internationalist spirit of cooperation and help each other to navigate the COVID-19 pandemic. The study further tests the applicability of the SES framework to the field of COVID-19 prevention and control based on the existing research, offering novel policy insights to cope with the COVID-19 pandemic that coexists with long-term human production and life for a long time.

Association of socio-economic deprivation with COVID-19 incidence and fatality during the first wave of the pandemic in Italy: lessons learned from a local register-based study

BACKGROUND

COVID-19 has been characterised by its global and rapid spread, with high infection, hospitalisation, and mortality rates worldwide. However, the course of the pandemic showed differences in chronology and intensity in different geographical areas and countries, probably due to a multitude of factors. Among these, socio-economic deprivation has been supposed to play a substantial role, although available evidence is not fully in agreement. Our study aimed to assess incidence and fatality rates of COVID-19 across the levels of socio-economic deprivation during the first epidemic wave (March-May 2020) in the Italian Province of Foggia, Apulia Region.

METHODS

Based on the data of the regional active surveillance platform, we performed a retrospective epidemiological study among all COVID-19 confirmed cases that occurred in the Apulian District of Foggia, Italy, from March 1st to May 5th, 2020. Geocoded addresses were linked to the individual Census Tract (CT) of residence. Effects of socio-economic condition were calculated by means of the Socio-Economic and Health-related Deprivation Index (SEHDI) on COVID-19 incidence and fatality.

RESULTS

Of the 1054 confirmed COVID-19 cases, 537 (50.9%) were men, 682 (64.7%) were 0-64 years old, and 338 (32.1%) had pre-existing comorbidities. COVID-19 incidence was higher in the less deprived areas (p < 0.05), independently on age. The level of socio-economic deprivation did not show a significant impact on the vital status, while a higher fatality was observed in male cases (p < 0.001), cases > 65 years (p < 0.001), cases having a connection with a nursing home (p < 0.05) or having at least 1 comorbidity (p < 0.001). On the other hand, a significant protection for healthcare workers was apparent (p < 0.001).

CONCLUSIONS

Our findings show that deprivation alone does not affect COVID-19 incidence and fatality burden, suggesting that the burden of disease is driven by a complexity of factors not yet fully understood. Better knowledge is needed to identify subgroups at higher risk and implement effective preventive strategies.

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.

The Role of Functional Urban Areas in the Spread of COVID-19 Omicron (Northern Spain)

This study focuses on the space-time patterns of the COVID-19 Omicron wave at a regional scale, using municipal data. We analyze the Basque Country and Cantabria, two adjacent regions in the north of Spain, which between them numbered 491,816 confirmed cases in their 358 municipalities from 15th November 2021 to 31st March 2022. The study seeks to determine the role of functional urban areas (FUAs) in the spread of the Omicron variant of the virus, using ESRI Technology (ArcGIS Pro) and applying intelligence location methods such as 3D-bins and emerging hot spots. Those methods help identify trends and types of problem area, such as hot spots, at municipal level. The results demonstrate that FUAs do not contain an over-concentration of COVID-19 cases, as their location coefficient is under 1.0 in relation to population. Nevertheless, FUAs do have an important role as drivers of spread in the upward curve of the Omicron wave. Significant hot spot patterns are found in 85.0% of FUA area, where 98.9% of FUA cases occur. The distribution of cases shows a spatially stationary linear correlation linked to demographically progressive areas (densely populated, young profile, and with more children per woman) which are well connected by highways and railroads. Based on this research, the proposed GIS methodology can be adapted to other case studies. Considering geo-prevention and WHO Health in All Policies approaches, the research findings reveal spatial patterns that can help policymakers in tackling the pandemic in future waves as society learns to live with the virus.

Quantitative resilience assessment of the network-level metro rail service's responses to the COVID-19 pandemic

The metro rail system has proven to be the most efficient high-capacity carriers. During the unprecedented coronavirus disease 2019 (COVID-19) challenge, non-pharmaceutical interventions become a widely adopted strategy to limit physical movements and interactions. For situational awareness and decision support, data-driven analytics about serviceability are invaluable to metro agencies and decision-makers of cities. This paper presents a data-driven analytical framework that quantitatively evaluates COVID-19-caused resilience performance of metro rails. Several characteristics (e.g., vulnerability, robustness, rapidity, and degree to return) of the metro system's responses to the disturbance were identified and modeled with multivariate multiple regression. The applicability and rationality of the resilience evaluation model were validated by the metro transit data of the United States. The preliminary results disclosed that metro rail transit encountered more vulnerability (90.6%) in passenger trips than motorbus and light rail (around 70%). A set of statistical models were employed to disentangle the effect of socio-demographic variables and COVID-19-related factors on the metro transit. The disclosed emerging knowledge of resilience provides an in-depth understanding of mobility trends for the public and time-sensitive decision support for the policy effects, to further improve the service and management of the metro system under the spread of the epidemic.

The COVID-19 Mortality Rate Is Associated with Illiteracy, Age, and Air Pollution in Urban Neighborhoods: A Spatiotemporal Cross-Sectional Analysis

There are different area-based factors affecting the COVID-19 mortality rate in urban areas. This research aims to examine COVID-19 mortality rates and their geographical association with various socioeconomic and ecological determinants in 350 of Tehran's neighborhoods as a big city. All deaths related to COVID-19 are included from December 2019 to July 2021. Spatial techniques, such as Kulldorff's SatScan, geographically weighted regression (GWR), and multi-scale GWR (MGWR), were used to investigate the spatially varying correlations between COVID-19 mortality rates and predictors, including air pollutant factors, socioeconomic status, built environment factors, and public transportation infrastructure. The city's downtown and northern areas were found to be significantly clustered in terms of spatial and temporal high-risk areas for COVID-19 mortality. The MGWR regression model outperformed the OLS and GWR regression models with an adjusted R² of 0.67. Furthermore, the mortality rate was found to be associated with air quality (e.g., NO₂, PM₁₀, and O₃); as air pollution increased, so did mortality. Additionally, the aging and illiteracy rates of urban neighborhoods were positively associated with COVID-19 mortality rates. Our approach in this study could be implemented to study potential associations of area-based factors with other emerging infectious diseases worldwide.

Bayesian profile regression to study the ecologic associations of correlated environmental exposures with excess mortality risk during the first year of the Covid-19 epidemic in lombardy, Italy

Many countries, including Italy, have experienced significant social and spatial inequalities in mortality during the Covid-19 pandemic. This study applies a multiple exposures framework to investigate how joint place-based factors influence spatial inequalities of excess mortality during the first year of the Covid -19 pandemic in the Lombardy region of Italy. For the Lombardy region, we integrated municipality-level data on all-cause mortality between 2015 and 2020 with 13 spatial covariates, including 5-year average concentrations of six air pollutants, the average temperature in 2020, and multiple socio-demographic factors, and health facilities per capita. Using the clustering algorithm Bayesian profile regression, we fit spatial covariates jointly to identify clusters of municipalities with similar exposure profiles and estimated associations between clusters and excess mortality in 2020. Cluster analysis resulted in 13 clusters. Controlling for spatial autocorrelation of excess mortality and health-protective agency, two clusters had significantly elevated excess mortality than the rest of Lombardy. Municipalities in these highest-risk clusters are in Bergamo, Brescia, and Cremona provinces. The highest risk cluster (C11) had the highest long-term particulate matter air pollution levels (PM2.5 and PM10) and significantly elevated NO2 and CO air pollutants, temperature, proportion ≤18 years, and male-to-female ratio. This cluster is significantly lower for income and ≥65 years. The other high-risk cluster, Cluster 10 (C10), is elevated significantly for ozone but significantly lower for other air pollutants. Covariates with elevated levels for C10 include proportion 65 years or older and a male-to-female ratio. Cluster 10 is significantly lower for income, temperature, per capita health facilities, ≤18 years, and population density. Our results suggest that joint built, natural, and socio-demographic factors influenced spatial inequalities of excess mortality in Lombardy in 2020. Studies must apply a multiple exposures framework to guide policy decisions addressing the complex and multi-dimensional nature of spatial inequalities of Covid-19-related mortality.

Influencia del índice de privación social en resultados durante la pandemia de COVID-19

Objetivo

Determinar la relación del índice de privación de la población con la utilización del sistema sanitario, la mala evolución y la mortalidad durante la pandemia de COVID-19.

Método

Estudio de cohorte retrospectivo de personas con infección por SARS-CoV-2 del 1 de marzo de 2020 al 9 de enero de 2022. Se recopilaron datos sociodemográficos, comorbilidad y tratamientos basales prescritos, otros datos basales y el índice de privación, estimado por sección censal. Se realizaron modelos multivariable de regresión logística multinivel para cada variable de resultado: fallecimiento, mala evolución (definida como fallecimiento o ingreso en la unidad de cuidados intensivos), ingreso y visitas a urgencias.

Resultados

La cohorte se compone de 371.237 personas con infección por SARS-CoV-2. En los modelos multivariable se observó un mayor riesgo de fallecimiento, de mala evolución, de ingreso hospitalario o de visita a urgencias en los quintiles de mayor privación en comparación con el quintil de menor privación. Para el riesgo de ser hospitalizado o de acudir a urgencias, en términos generales hubo diferencias entre todos los quintiles. También se observó que estas diferencias se daban en el primer y el tercer periodos de la pandemia para la mortalidad y la mala evolución, y en todos para el riesgo de ser ingresado o de acudir a urgencias.

Conclusiones

Los colectivos con mayor nivel de privación han tenido mayores tasas de mortalidad y de ingreso en comparación con los colectivos con unas tasas de privación más bajas. Es necesario realizar intervenciones que minimicen estas desigualdades.

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.

Designing for the post-pandemic era: Trends, focuses, and strategies learned from architectural competitions based on a text analysis

The COVID-19 pandemic has made the built environment an important source of prevention and control, architects and scholars have thus been seeking countermeasures since the beginning of the outbreak. As design and construction cycles are long, only a few completed cases and evidence-based studies are available for reference. However, massive architectural competition works have emerged, which always been the soil for discussion and practice of cutting-edge design issues. These contain a vast number of ideas for solutions from various design dimensions-including cities, buildings, and facilities-and provide a great deal of materials worth analyzing and summarizing. Therefore, the exploration of competitions will provide us with public health intervention directions, strategies and a rethinking of the built environment. Using a text-mining approach, we analyzed 558 winning entries in architectural competitions related to the pandemic response, exploring specific issues, populations involved, coping strategies, and trends that emerged as the pandemic evolved. Our results show that the strategies proposed can be grouped into 17 keywords, with modularization being the most frequent strategy and related strategies like rapid assembly, flexible space, etc. are also took a significant percentage of the use. Further, we explored the technical orientation, year, territory, target groups, and target problems of the works which lead to a series of cross-comparison relationships. The results indicate that indirect impacts caused by the pandemic gained more attention and flexible Solutions were used more often highlighted the consensus when adapting to the uncertainties. The focus on the spiritual dimension is increasing year by year reflected the spiritual influences were gaining traction and the indirect impacts gradually showed up over time. The research will provide a strategy reference for the design response to the pandemic, as well as help understand the influence and significance of social factors behind the divergence of issue focuses and strategic tendency in different regions and times.

COVID-19 mortality and deprivation: pandemic, syndemic, and endemic health inequalities

COVID-19 has exacerbated endemic health inequalities resulting in a syndemic pandemic of higher mortality and morbidity rates among the most socially disadvantaged. We did a scoping review to identify and synthesise published evidence on geographical inequalities in COVID-19 mortality rates globally. We included peer-reviewed studies, from any country, written in English that showed any area-level (eg, neighbourhood, town, city, municipality, or region) inequalities in mortality by socioeconomic deprivation (ie, measured via indices of multiple deprivation: the percentage of people living in poverty or proxy factors including the Gini coefficient, employment rates, or housing tenure). 95 papers from five WHO global regions were included in the final synthesis. A large majority of the studies (n=86) found that COVID-19 mortality rates were higher in areas of socioeconomic disadvantage than in affluent areas. The subsequent discussion reflects on how the unequal nature of the pandemic has resulted from a syndemic of COVID-19 and endemic inequalities in chronic disease burden.

Spatiotemporal clusters and the socioeconomic determinants of COVID-19 in Toronto neighbourhoods, Canada

The aim of this study is to identify spatiotemporal clusters and the socioeconomic drivers of COVID-19 in Toronto. Geographical, epidemiological, and socioeconomic data from the 140 neighbourhoods in Toronto were used in this study. We used local and global Moran's I, and space-time scan statistic to identify spatial and spatiotemporal clusters of COVID-19. We also used global (spatial regression models), and local geographically weighted regression (GWR) and Multiscale Geographically weighted regression (MGWR) models to identify the globally and locally varying socioeconomic drivers of COVID-19. The global regression model identified a lower percentage of educated people and a higher percentage of immigrants in the neighbourhoods as significant predictors of COVID-19. MGWR shows the best fit model to explain the variables affecting COVID-19. The findings imply that a single intervention package for the entire area would not be an effective strategy for controlling COVID-19; a locally adaptable intervention package would be beneficial.

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.

A review of strategies and their effectiveness in reducing indoor airborne transmission and improving indoor air quality

Airborne transmission arises through the inhalation of aerosol droplets exhaled by an infected person and is now thought to be the primary transmission route of COVID-19. Thus, maintaining adequate indoor air quality levels is vital in mitigating the spread of the airborne virus. The cause-and-effect flow of various agents involved in airborne transmission of viruses has been investigated through a systematic literature review. It has been identified that the airborne virus can stay infectious in the air for hours, and pollutants such as particulate matter (PM10, PM2.5), Nitrogen dioxide (NO2), Sulphur dioxide (SO2), Carbon monoxide (CO), Ozone (O3), Carbon dioxide (CO2), and Total Volatile Organic Compounds (TVOCs) and other air pollutants can enhance the incidence, spread and mortality rates of viral disease. Also, environmental quality parameters such as humidity and temperature have shown considerable influence in virus transmission in indoor spaces. The measures adopted in different research studies that can curb airborne transmission of viruses for an improved Indoor Air Quality (IAQ) have been collated for their effectiveness and limitations. A diverse set of building strategies, components, and operation techniques from the recent literature pertaining to the ongoing spread of COVID-19 disease has been systematically presented to understand the current state of techniques and building systems that can minimize the viral spread in built spaces This comprehensive review will help architects, builders, realtors, and other organizations improve or design a resilient building system to deal with COVID-19 or any such pandemic in the future.

Sustainable solutions for indoor pollution abatement during COVID phase: A critical study on current technologies & challenges

The appearance of the contagious virus COVID-19, several revelations and environmental health experts punctually predicted the possibly disastrous public health complications of coexisting catching and airborne contamination-arbitrated disease. But much attention has been given on the outdoor-mediated interactions. Almost 3.8 million premature deaths occur every year globally due to the illness from indoor air pollution. Considering the human staying longer span indoors due to restricted human activities or work from home, the indoor air quality (IAQ) might show prominent role for individual health life. Currently, the Environmental Protection Agency (EPA) ensures no regulation of indoor airborne pollution. Herein, the paper underlines the common bases of indoor air pollution, poor IAQ, and impacts of the aerosolized airborne particles on the human health. In order to address these challenges and collective contagion events in indoor environment, several emerging control techniques and preventive sustainable solutions are suggested. By this, more innovations need to be investigated in future to measure the impact of indoor air pollution on individual health.

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.

Application of Data Science for Cluster Analysis of COVID-19 Mortality According to Sociodemographic Factors at Municipal Level in Mexico

Mexico is among the five countries with the largest number of reported deaths from COVID-19 disease, and the mortality rates associated to infections are heterogeneous in the country due to structural factors concerning population. This study aims at the analysis of clusters related to mortality rate from COVID-19 at the municipal level in Mexico from the perspective of Data Science. In this sense, a new application is presented that uses a machine learning hybrid algorithm for generating clusters of municipalities with similar values of sociodemographic indicators and mortality rates. To provide a systematic framework, we applied an extension of the International Business Machines Corporation (IBM) methodology called Batch Foundation Methodology for Data Science (FMDS). For the study, 1,086,743 death certificates corresponding to the year 2020 were used, among other official data. As a result of the analysis, two key indicators related to mortality from COVID-19 at the municipal level were identified: one is population density and the other is percentage of population in poverty. Based on these indicators, 16 municipality clusters were determined. Among the main results of this research, it was found that clusters with high values of mortality rate had high values of population density and low poverty levels. In contrast, clusters with low density values and high poverty levels had low mortality rates. Finally, we think that the patterns found, expressed as municipality clusters with similar characteristics, can be useful for decision making by health authorities regarding disease prevention and control for reinforcing public health measures and optimizing resource distribution for reducing hospitalizations and mortality.

Spatiotemporal pattern of Covid-19 outbreak in Turkey

The earliest case of Covid-19 was documented in Wuhan city of China and since then the virus has been spreading throughout the globe. The aim of this study is to evaluate the clusters of Covid-19 among the provinces in Turkey and to examine whether the clustering pattern has changed after the country's lockdown strategy. The spatial dependence of Covid-19 in 81 provinces of Turkey was examined by spatial analysis between February 8 and June 28, 2021. Global and Local Moran's I and Gi* were employed to measure the global and local spatial autocorrelation degrees. The geographical distribution of Covid-19 in the provinces of Turkey showed a strong spatial autocorrelation while the spatial structure of the clusters varied by weeks. The findings of the study show that the complete lockdown carried out in Turkey has been quite effective in mitigating Covid-19. The importance of spatial relations in preventing the spread of the disease in Turkey has also been demonstrated in this context.

Identifying spatiotemporal patterns of COVID-19 transmissions and the drivers of the patterns in Toronto: a Bayesian hierarchical spatiotemporal modelling

Spatiotemporal patterns and trends of COVID-19 at a local spatial scale using Bayesian approaches are hardly observed in literature. Also, studies rarely use satellite-derived long time-series data on the environment to predict COVID-19 risk at a spatial scale. In this study, we modelled the COVID-19 pandemic risk using a Bayesian hierarchical spatiotemporal model that incorporates satellite-derived remote sensing data on land surface temperature (LST) from January 2020 to October 2021 (89 weeks) and several socioeconomic covariates of the 140 neighbourhoods in Toronto. The spatial patterns of risk were heterogeneous in space with multiple high-risk neighbourhoods in Western and Southern Toronto. Higher risk was observed during Spring 2021. The spatiotemporal risk patterns identified 60% of neighbourhoods had a stable, 37% had an increasing, and 2% had a decreasing trend over the study period. LST was positively, and higher education was negatively associated with the COVID-19 incidence. We believe the use of Bayesian spatial modelling and the remote sensing technologies in this study provided a strong versatility and strengthened our analysis in identifying the spatial risk of COVID-19. The findings would help in prevention planning, and the framework of this study may be replicated in other highly transmissible infectious diseases.

Data-driven multiscale modelling and analysis of COVID-19 spatiotemporal evolution using explainable AI

To quantificationally identify the optimal control measures for regulators to best minimize COVID-19′s growth (G-rate) and death (D-rate) rates in today's context, this paper develops a top-down multiscale engineering approach which encompasses a series of systematic analyses, namely: (global scale) predictive modelling of G-rate and D-rate due to COVID-19 globally, followed by determining the most effective control factors which can best minimize both parameters over time via explainable Artificial Intelligence (AI) with SHAP (SHapley Additive exPlanations) method; (continental scale) same predictive forecasting of G-rate and D-rate in all continents, followed by performing explainable SHAP analysis to determine the most effective control factors for the respective continents; and (country scale) clustering the different countries (> 150 in total) into 3 main clusters to identify the universal set of effective control measures. By using the historical period between 2 May 2020 and 1 Oct 2021, the average MAPE scores for forecasting G-rate and D-rate are within 10%, or less on average, at the global and continental scales. Systematically, we have quantificationally demonstrated that the top 3 most effective control measures for regulators to best minimize G-rate universally are COVID-CONTACT-TRACING, PUBLIC-GATHERING-RULES, and COVID-STRINGENCY-INDEX, while the control factors relating to D-rate depend on the modelling scenario.

Environmental Factors Influencing COVID-19 Incidence and Severity

Emerging evidence supports a link between environmental factors-including air pollution and chemical exposures, climate, and the built environment-and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission and coronavirus disease 2019 (COVID-19) susceptibility and severity. Climate, air pollution, and the built environment have long been recognized to influence viral respiratory infections, and studies have established similar associations with COVID-19 outcomes. More limited evidence links chemical exposures to COVID-19. Environmental factors were found to influence COVID-19 through four major interlinking mechanisms: increased risk of preexisting conditions associated with disease severity; immune system impairment; viral survival and transport; and behaviors that increase viral exposure. Both data and methodologic issues complicate the investigation of these relationships, including reliance on coarse COVID-19 surveillance data; gaps in mechanistic studies; and the predominance of ecological designs. We evaluate the strength of evidence for environment-COVID-19 relationships and discuss environmental actions that might simultaneously address the COVID-19 pandemic, environmental determinants of health, and health disparities.

The association between area deprivation and COVID-19 incidence: a municipality-level spatio-temporal study in Belgium, 2020–2021

Background

In Belgium, current research on socio-economic inequalities in the coronavirus disease 2019 (COVID-19) crisis has mainly focused on excess mortality and data from the first epidemiological wave. The current study adds onto this by examining the association between COVID-19 incidence and area deprivation during the first five wave and interwave periods, thus adding a temporal gradient to the analyses.

Methods

We use all confirmed COVID-19 cases between March 2020 and June 2021 in Belgium, aggregated at the municipality-level. These data were collected by the national laboratory-based COVID-19 surveillance system. A level of area deprivation was assigned to each Belgian municipality using data of three socio-economic variables: the share of unemployed persons in the active population, the share of households without a car and the share of low-educated persons. The spatio-temporal association between COVID-19 incidence and area deprivation was assessed by performing multivariate negative-binomial regression analyses and computing population attributable fractions.

Results

A significant association between COVID-19 incidence and area deprivation was found over the entire study period, with the incidence in the most deprived areas predicted to be 24% higher than in the least deprived areas. This effect was dependent on the period during the COVID-19 crisis. The largest socio-economic inequalities in COVID-19 infections could be observed during wave 2 and wave 3, with a clear disadvantage for deprived areas.

Conclusion

Our results provide new insights into spatio-temporal patterns of socio-economic inequalities in COVID-19 incidence in Belgium. They reveal the existence of inequalities and a shift of these patterns over time.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13690-022-00856-9.

A geographical information system model to define COVID-19 problem areas with an analysis in the socio-economic context at the regional scale in the North of Spain

The work presented concerns the spatial behaviour of coronavirus disease 2019 (COVID-19) at the regional scale and the socio-economic context of problem areas over the 2020-2021 period. We propose a replicable geographical information systems (GIS) methodology based on geocodification and analysis of COVID-19 microdata registered by health authorities of the Government of Cantabria, Spain from the beginning of the pandemic register (29th February 2020) to 2nd December 2021. The spatial behaviour of the virus was studied using ArcGIS Pro and a 1x1 km vector grid as the homogeneous reference layer. The GIS analysis of 45,392 geocoded cases revealed a clear process of spatial contraction of the virus after the spread in 2020 with 432 km2 of problem areas reduced to 126.72 km2 in 2021. The socio-economic framework showed complex relationships between COVID-19 cases and the explanatory variables related to household characteristics, socio-economic conditions and demographic structure. Local bivariate analysis showed fuzzier results in persistent hotspots in urban and peri-urban areas. Questions about ‘where, when and how’ contribute to learning from experience as we must draw inspiration from, and explore connections to, those confronting the issues related to the current pandemic.

A comparative study for determining Covid-19 risk levels by unsupervised machine learning methods

The restrictions have been preferred by governments to reduce the spread of Covid-19 and to protect people's health according to regional risk levels. The risk levels of locations are determined due to threshold values ​​based on the number of cases per 100,000 people without environmental variables. The purpose of our study is to apply unsupervised machine learning techniques to determine the cities with similar risk levels by using the number of cases and environmental parameters. Hierarchical, partitional, soft, and gray relational clustering algorithms were applied to different datasets created with weekly the number of cases, population densities, average ages, and air pollution levels. Comparisons of the clustering algorithms were performed by using internal validation indexes, and the most successful method was identified. In the study, it was revealed that the most successful method in clustering based on the number of cases is Gray Relational Clustering. The results show that using the environmental variables for restrictions requires more clusters than 4 for healthier decisions and Gray Relational Clustering gives stable results, unlike other algorithms.

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.

Spatiotemporal clustering patterns and sociodemographic determinants of COVID-19 (SARS-CoV-2) infections in Helsinki, Finland

This study aims to elucidate the variations in spatiotemporal patterns and sociodemographic determinants of SARS-CoV-2 infections in Helsinki, Finland. Global and local spatial autocorrelation were inspected with Moran's I and LISA statistics, and Getis-Ord Gi* statistics was used to identify the hot spot areas. Space-time statistics were used to detect clusters of high relative risk and regression models were implemented to explain sociodemographic determinants for the clusters. The findings revealed the presence of spatial autocorrelation and clustering of COVID-19 cases. High–high clusters and high relative risk areas emerged primarily in Helsinki's eastern neighborhoods, which are socioeconomically vulnerable, with a few exceptions revealing local outbreaks in other areas. The variation in COVID-19 rates was largely explained by median income and the number of foreign citizens in the population. Furthermore, the use of multiple spatiotemporal analysis methods are recommended to gain deeper insights into the complex spatiotemporal clustering patterns and sociodemographic determinants of the COVID-19 cases.

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.

Spatiotemporal Analysis of Covid-19 in Turkey

The Covid-19 pandemic continues to threaten public health around the world. Understanding the spatial dimension of this impact is very important in terms of controlling and reducing the spread of the pandemic. This study measures the spatial association of the Covid-19 outbreak in Turkey between February 8 and May 28, 2021 and reveals its spatiotemporal pattern. In this context, global and local spatial autocorrelation was used to determine whether there is a spatial association of Covid-19 infections, while the spatial regression model was employed to reveal the geographical relationship of the potential factors affecting the number of Covid-19 cases. As a result of the analyzes made in this context, it has been observed that there are spatial associations and distinct spatial clusters in Covid-19 cases at the provincial level in Turkey. The results of the spatial regression model showed that population density and elderly dependency ratio are very important in explaining the model of Covid-19 case numbers. Additionally, it has been revealed that Covid-19 is affected by the Covid-19 numbers of neighboring provinces, apart from the said explanatory variables. The findings of the study revealed that spatial analysis is helpful in understanding the spread of the pandemic in Turkey. It has been determined that geographical location is an important factor to be considered in the investigation of the factors affecting Covid-19.

From federal transfers and local investments to a potential convergence of COVID-19 and climate change: The case study of São Paulo city

This paper is divided into two parts to explore some aspects of municipal development related to national and subnational investments in disaster risk reduction and urban sustainability related to Covid-19 and climate change response. In Part I, a survey on disasters and national transfers to 45 Brazilian municipalities is presented. In Part II, the local-scale approach enabled to compare the areas most affected by COVID-19 with those impacted by climate change. There are large uncertainties around financial support from the federal government and their impact at local scale. São Paulo city was chosen because it reveals some important aspects of spatial structure carried out through local investments. In this sense, updated information on floods and warmer surfaces were updated to provoke a discussion about a potential confluence with the effects of pandemic. The results highlighted the effects of scarce federal transfers and the maps help us to identify the spatial distribution of people at risk, which can be beneficial for municipal decisions as they highlight a significative relationship between pandemic effects and an uneven social structure. In conclusion, the trade-off between this unequal structure and a necessary and effectively sustainable change leads us to reflect on local investment trends.

Factors associated with vaccine intention in adults living in England who either did not want or had not yet decided to be vaccinated against COVID-19

Early studies showed that 28-36% of UK adults were unsure or unwilling to be vaccinated against COVID-19. We wanted to identify which socio-demographic, socio-economic, personal health and psychological factors were associated with COVID-19 vaccine intentions (CVI) in adults living in England who did not want, yet to consider, or not sure whether to vaccinate. In October/November 2020, prior to vaccine availability, we surveyed adults stratified by gender, region, and deprivation, with additional purposive sampling of those aged 50 and over and those from an ethnic minority. Two hundred and ten did not want; 407 had yet to consider; and 1,043 were not sure whether to be vaccinated. Factors positively associated with CVI were: favorable vaccine views, trust in institutions associated with vaccine approval, vaccine subjective norms, anticipated regret of not having a vaccine, perceived vaccine benefits, perceived safety knowledge sufficiency, and a history of having an influenza vaccine. Factors negatively associated were: anti-lockdown views, and being a health or social care worker. Whilst showing significant relationships with CVI when analyzed in isolation, neighborhood deprivation and ethnicity did show an independent relationship to intention when all study measures were controlled for. Our findings suggest vaccine promotion focusing on the anticipated regret of not having a vaccine, the benefits of a mass COVID-19 immunization program, and the safety of a vaccine whilst ensuring or engendering trust in those bodies that brand a campaign may be most supportive of COVID-19 vaccine uptake.

Exploring the nexus between social vulnerability, built environment, and the prevalence of COVID-19: A case study of Chicago

COVID-19 has significantly and unevenly impacted the United States, disproportionately affecting socially vulnerable communities. While epidemiologists and public health officials have suggested social distancing and shelter-in-place orders to halt the spread of this virus, the ability to comply with these guidelines is dependent on neighborhood, household, and individual characteristics related to social vulnerability. We use structural equation modeling and multiple data sources, including anonymized mobile phone location data from SafeGraph, to examine the effects of different social vulnerability and built environment factors on COVID-19 prevalence over two overlapping time periods (March to May and March to November of 2020). We use Chicago, Illinois as a case study and find that zip codes with low educational attainment consistently experienced higher case rates over both periods. Though population density was not significantly related to the prevalence in any period, movement of people made a significant contribution only during the longer time period. This finding highlights the significance of analyzing different timeframes for understanding social vulnerability. Our results suggest social vulnerability played an influential role in COVID-19 prevalence, highlighting the needs to address socioeconomic barriers to pandemic recovery and future pandemic response.

An evaluative model for assessing pandemic resilience at the neighborhood level: The case of Tehran

The spread of the COVID-19 virus, which has caused abundant mortalities in human settlements, has drawn the attention of urban planners and policy-makers to the necessity of improving resilience to future pandemics. In this study, a set of indicators related to pandemic resilience were identified and used to develop a composite multi-dimensional pandemic resilience index for Tehran's neighborhoods. The physical, infrastructural, socio-economic, and environmental dimensions of pandemic resilience were defined considering the conditions of 351 neighborhoods through the exploratory factor analysis method. Accordingly, the pandemic resilience (PR) score of the neighborhoods was calculated. Furthermore, the Pearson correlation analysis was used to validate the PR scores by examining the correlation between the neighborhood PR scores and the number of confirmed cases. For this purpose, we used a sample consisting of 43,000 confirmed COVID-19 patients in the first five months of its spread. The test shows a statistically significant negative correlation between neighborhoods' resilience score and the cumulative number of confirmed patients in the neighborhoods (r= -.456, P<0.001). This study also tries to develop a new model to better understand health determinants of pandemic resilience. The proposed model can inform planners and policymakers to take appropriate measures to create more pandemic-resilient urban neighborhoods.

Correlating dynamic climate conditions and socioeconomic-governmental factors to spatiotemporal spread of COVID-19 via semantic segmentation deep learning analysis

In this study, we develop a deep learning model to forecast the transmission rate of COVID-19 globally, via a proposed G parameter, as a function of fused data features which encompass selected climate conditions, socioeconomic and restrictive governmental factors. A 2-step optimization process is adopted for the model's data fusion component which systematically performs the following: (Step I) determining the optimal climate feature which can achieve good precision score (> 70%) when predicting the spatial classes distribution of the G parameter on a global scale consisting of 251 countries, followed by (Step II) fusing the optimal climate feature with 11 selected socioeconomic-governmental factors to further improve the model's predictive capability. By far, the obtained results from the model's testing step indicate that land surface temperature day (LSTD) has the strongest correlation with the global G parameter over time by achieving an average precision score of 72%. When coupled with relevant socioeconomic-governmental factors, the model's average precision score improves to 77%. At the local scale analysis for selected countries, our proposed model can provide insights into the relationship between the fused data features and the respective local G parameter by achieving an average accuracy score of 79%.

Discovering optimal strategies for mitigating COVID-19 spread using machine learning: Experience from Asia

To inform data-driven decisions in fighting the global pandemic caused by COVID-19, this research develops a spatiotemporal analysis framework under the combination of an ensemble model (random forest regression) and a multi-objective optimization algorithm (NSGA-II). It has been verified for four Asian countries, including Japan, South Korea, Pakistan, and Nepal. Accordingly, we can gain some valuable experience to better understand the disease evolution, forecast the prevalence of the disease, which can provide sustainable evidence to guide further intervention and management. Random forest with a proper rolling time-window can learn the combined effects of environmental and social factors to accurately predict the daily growth of confirmed cases and daily death rate on a national scale, which is followed by NSGA-II to find a range of Pareto optimal solutions for ensuring the minimization of the infection rate and mortality at the same time. Experimental results demonstrate that the predictive model can alert the local government in advance, allowing the accused time to put forward relevant measures. The temperature in the category of environment and the stringency index belonging to the social factor are identified as the top 2 important features to exert a greater impact on the virus transmission. Moreover, optimal solutions provide references to design the best control strategies towards pandemic containment and prevention that can accommodate the country-specific circumstance, which are possible to decrease the two objectives by more than 95%. In particular, appropriate adjustment of social-related features needs to take priority over others, since it can bring about at least 1.47% average improvement of two objectives compared to environmental factors.

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.

Habitat vulnerability in slum areas of India - What we learnt from COVID-19?

UN-Habitat identified the present COVID-19 pandemic as 'city-centric'. In India, more than 50% of the total cases were documented in megacities and million-plus cities. The slums of cities are the most vulnerable due to its unhygienic environment and high population density that requires an urgent implementation of public healthcare measures. This study aims to examine habitat vulnerability in slum areas to COVID-19 in India using principal component analysis and Fuzzy AHP based technique to develop slum vulnerability index to COVID-19 (SVI_covid-19). Four slum vulnerability groups (i.e. principal components) were retained with eigen-values greater than 1 based on Kaiser criterion - poor slum household status; lack of social distance maintenance; high concentrations of slum population and towns and mobility of the households. This study also mapped composite SVI_covid-19 on the basis of PCA and Fuzzy AHP method at the state level for a better understanding of spatial variations. The result shows that slums located in the eastern and central parts of India (particularly Uttar Pradesh, Bihar, Jharkhand, Odisha, West Bengal) were more vulnerable to COVID-19 transmission due to lack of availability as well as accessibility to the basic services and amenities to slum dwellers. Thus, the findings of the study may not only help to understand the habitat vulnerability in slum areas to COVID-19 but it will also teach a lesson to implement effective policies for enhancing the quality of slum households (HHs) and to reduce the health risk from any infectious disease in future.

Self-correcting error-based prediction model for the COVID-19 pandemic and analysis of economic impacts

In order to improve the prediction accuracy of COVID-19 and strengthen the economic management and control, a self-correcting intelligent pandemic prediction model is proposed. The research shows that: (1) The pandemic, as a major social factor, has a great impact on the consumption expenditure level of various industries, and directly affects the public consumption expenditure level in different periods for example the spend_all in California decreased by 37.7%; (2) The economic losses caused by the increasingly serious pandemic period far less than the economic losses caused by the panic in the early stage of the pandemic, and the reason is the government's strong guarantee policies stimulate economic recovery. For example, the spend_all in California has increased from -37.7% to about -18%; (3) The proposed model improves the prediction accuracy of economic trend, and the government can make prediction according to the early warning economic prediction, which provides reference for the economic management control at the micro level of enterprises and the macro level of the nation; (4) The dual strategies of self correcting prediction and pandemic control realize the overall design of real-time control and performance optimization of economic process, and provide reference for the overall recovery of the economy.

Is compulsory home quarantine less effective than centralized quarantine in controlling the COVID-19 outbreak? Evidence from Hong Kong

Faced with the global spread of COVID-19, the Hong Kong government imposed compulsory home quarantine on all overseas arrivals, while cities in mainland China and Macau adopted a more stringent centralized quarantine approach. This study evaluates the effectiveness of compulsory home quarantine as a means of pandemic control. Combining epidemiological data with traditional socioeconomic and meteorological data from over 250 cities, we employ the Synthetic Control Method (SCM) to construct a counterfactual "synthetic Hong Kong". This model simulates the infection trends for a hypothetical situation in which HK adopts centralized quarantine measures, and compares them to actual infection numbers. Results suggest that home quarantine would have been less effective than centralized quarantine initially. However, the infection rate under home quarantine later converges with the counterfactual estimate under centralized quarantine (0.136% vs. 0.174%), suggesting similar efficacy in the later phase of implementation. Considering its minimal reliance on public resources, home quarantine with heightened enforcement may therefore be preferable to centralized quarantine in countries with limited public health resources. Home quarantine as a quarantine alternative balances public protection and individual freedom, while conserving resources, making it a more sustainable option for many cities.

Mapping the Impact of COVID-19 Lockdown on Urban Surface Ecological Status (USES): A Case Study of Kolkata Metropolitan Area (KMA), India

An urban ecosystem’s ecological structure and functions can be assessed through Urban Surface Ecological Status (USES). USES are affected by human activities and environmental processes. The mapping of USESs are crucial for urban environmental sustainability, particularly in developing countries such as India. The COVID-19 pandemic caused unprecedented negative impacts on socio-economic domains; however, there was a reduction in human pressures on the environment. This study aims to assess the effects of lockdown on the USES in the Kolkata Metropolitan Area (KMA), India, during different lockdown phases (phases I, II and III). The land surface temperature (LST), normalized difference vegetation index (NDVI), and wetness and normalized difference soil index (NDSI) were assessed. The USES was developed by combining all of the biophysical parameters using Principal Component Analysis (PCA). The results showed that there was a substantial USES spatial variability in KMA. During lockdown phase III, the USES in fair and poor sustainability areas decreased from 29% (2019) to 24% (2020), and from 33% (2019) to 25% (2020), respectively. Overall, the areas under poor USES decreased from 30% to 25% during lockdown periods. Our results also showed that the USES mean value was 0.49 in 2019but reached 0.34 during the lockdown period (a decrease of more than 30%). The poor USES area was mainly concentrated in built-up areas (with high LST and NDSI), compared to the rural fringe areas of KMA (high NDVI and wetness). The mapping of USES are crucial in different biophysical environmental conditions, and they can be very helpful for the assessment of urban sustainability.

COVID-19 and urban spaces: A new integrated CFD approach for public health opportunities

Safe urban public spaces are vital owing to their impacts on public health, especially during pandemics such as the ongoing COVID-19 pandemic. Urban public spaces and urbanscape elements must be designed with the risk of viral transmission in mind. This work therefore examines how the design of urbanscape elements can be revisited to control COVID-19 transmission dynamics. Nine proposed models of urban public seating were thus presented and assessed using a transient three-dimensional computational fluid dynamics (CFD) model, with the Eulerian-Lagrangian method and discrete phase model (DPM). The proposed seating models were evaluated by their impact on the normalized air velocity, the diameter of coughing droplets, and deposition fraction. Each of the proposed models demonstrated an increase in the normalized velocity, and a decrease in the deposition fraction by >29%. Diagonal cross linear and curved triangle configurations demonstrated an improved airflow momentum and turbulent flow, which decreased the droplets deposition fraction by 68%, thus providing an improved, healthier urban public seating option.

A review of GIS methodologies to analyze the dynamics of COVID-19 in the second half of 2020

COVID-19 has infected over 163 million people and has resulted in over 3.9 million deaths. Regarding the tools and strategies to research the ongoing pandemic, spatial analysis has been increasingly utilized to study the impacts of COVID-19. This article provides a review of 221 scientific articles that used spatial science to study the pandemic published from June 2020 to December 2020. The main objectives are: to identify the tools and techniques used by the authors; to review the subjects addressed and their disciplines; and to classify the studies based on their applications. This contribution will facilitate comparisons with the body of work published during the first half of 2020, revealing the evolution of the COVID-19 phenomenon through the lens of spatial analysis. Our results show that there was an increase in the use of both spatial statistical tools (e.g., geographically weighted regression, Bayesian models, spatial regression) applied to socioeconomic variables and analysis at finer spatial and temporal scales. We found an increase in remote sensing approaches, which are now widely applied in studies around the world. Lockdowns and associated changes in human mobility have been extensively examined using spatiotemporal techniques. Another dominant topic studied has been the relationship between pollution and COVID-19 dynamics, which enhance the impact of human activities on the pandemic's evolution. This represents a shift from the first half of 2020, when the research focused on climatic and weather factors. Overall, we have seen a vast increase in spatial tools and techniques to study COVID-19 transmission and the associated risk factors.