Crowding has consequences: Prevention and management of COVID-19 in informal urban settlements

Age, gender and household infrastructural inequality in COVID-19: Contextual analysis of Mamelodi

BACKGROUND

 Age, gender and household infrastructure are important social determinants affecting health inequalities. This study aims to assess the ways that age and gender of the household head and household infrastructure intersect to create relative advantage and disadvantage in COVID-19 vulnerability.

METHODS

 Using household primary care survey data from Mamelodi, Gauteng, headed households were sorted into three risk categories for each of the relevant infrastructural determinants of COVID-19. Bivariate ordinal logistic regression was used to determine the odds of households falling into each risk category. The proportion of high-risk (HR) categories and dwelling types was also calculated.

RESULTS

 Households headed by someone ≥ 65 years were less likely to be in all HR categories and more frequently had formal houses. Male-head households were more likely to be HR for water, sanitation and hygiene infrastructure and indoor pollution; however, female-headed households (FHHs) were at higher risk for crowding. In Mamelodi, households headed by ≥ 65 years olds were relatively infrastructurally protected, likely because of pro-equity housing policy, as were FHHs, except for crowding. The care load on FHHs results in their infrastructural protection benefiting more community members, while simultaneously incurring risk.

CONCLUSION

 Infrastructural support based on the household head's age and gender could improve targeting and the effectiveness of health interventions. These results demonstrate the importance of a contextual understanding of gender and age inequalities and tailoring public health support based on this understanding.Contribution: This research describes patterns of health-related infrastructural inequality, identifies ways to improve health interventions, and demonstrates the importance of equity-focused policy in an African context.

Assessing the mental health of slum dwellers: an ordinal logistic approach

BACKGROUND

Stress, depression and anxiety are prevalent mental health concerns that are getting worse every day in the context of rapidly expanding megacities, where a rising number of people live in slums. The purpose of this study is to evaluate the state of mental health and related variables underlying mental health issues among the impoverished population.

METHODOLOGY

A total of 384 slum residents from the Khulna division responded to our questionnaire, which included the DASS-21 and other pertinent questions. Using ordinal logistic regression, the significant factors were extracted.

RESULTS

The Cronbach's reliability coefficient for the DASS-21 scale lies between 0.79 and 0.89 which indicates the high reliability of the DASS-21 scales. According to the findings, roughly 72.7%, 84.1%, and 69% of participants slum dwellers experienced anxiety, depression, and anxiety problems respectively. The result of ordinal logistic regression shows, slum dwellers of female gender (B = 0.669*, 95% CI 0.141, 1.197), married (B = 1.506*, 95% CI 0.119, 2.893), having only one earning member in the family (B = 1.224*, 95% CI 0.526, 1.922), day laborers (B = 2.294*, 95% CI 1.115, 3.473), not being able to educate children due to financial problem (B = 0.558*, 95% CI 0.183, 0.932) were more likely to report high levels of anxiety, depression, and stress.

CONCLUSION

The study finds that a significant portion of people who live in slums struggle with mental health issues. It also points to the need for further support, intervention, and study on Khulna's slum inhabitants who are experiencing mental health issues. The authors anticipate that the results will spur policymakers and government representatives to enhance financing for slum dwellers and employ psychological intervention strategies, both of which will aid in achieving the Sustainable Development Goal.

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.

Risk factors for COVID-19 in-hospital mortality in Argentina: A competing risk survival analysis

COVID-19 poses dire threats for low and middle-income countries (LMICs). Yet, there remains limited rigorous evidence describing the characteristics and outcomes of hospitalized patients for LMICs, and often the evidence was based on small samples and/or unicentric. The objective of this study was to examine risk factors of COVID-19 mortality in Argentina, a hard-hit middle-income Latin American country. We analyze data on 5,146 COVID-19 patients from 11 centers across 10 cities in Argentina, making this one of the largest multi-centric retrospective observational descriptive studies in the LMICs. Information on demographics and co-morbidities was extracted from medical records. Outcomes of relevance consisted of whether the patient was discharged or deceased (as established in medical records), along with date of each event. We use survival models that account for competing risks. Median age was 60 years (IQR: 48-72), there were fewer women (40.8%) hospitalized than men (59.2%), and the most prevalent comorbidities were hypertension (40.9%), diabetes (20.0%) and obesity (19.1%). Patients were hospitalized for a median duration of 8 days (IQR: 5-13), and in-hospital mortality was 18.1%, though it varied substantially across health centers (95%CI: 17.1%-19.2%). Baseline characteristics most associated with in-hospital mortality were respiratory rate (adjusted HR = 3.6, 95%CI: 2.5-5.4 for ≥ 26 breathes/min), older age (adjusted HR = 2.5, 95%CI: 2.0-3.3 for the 80+ age group), and chronic kidney disease (adjusted HR = 2.2, 95%CI: 1.8-2.8). Associations were attenuated when survival models did not account for the competing risk of being discharged. We document lower mortality rates than those in prior studies, likely due to a lower prevalence of comorbidities amongst patients in our sample. Compared with standard Cox models, we find that, when using competing risk models, risk factors have a larger role in explaining COVID-19 mortality. Overall, we provide rigorous evidence describing the characteristics and outcomes of hospitalized patients for LMICs. Thus, our findings are useful to conduct a more accurate in-hospital monitoring of patient subgroups who may be at greater risk. They also provide valuable guidance for public health and policy efforts in Argentina and other developing countries.

Multi-scale risk assessment and mitigations comparison for COVID-19 in urban public transport: A combined field measurement and modeling approach

The outbreak of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has caused an unparalleled disruption to daily life. Given that COVID-19 primarily spreads in densely populated indoor areas, urban public transport (UPT) systems pose significant risks. This study presents an analysis of the air change rate in buses, subways, and high speed trains based on measured CO₂ concentrations and passenger behaviors. The resulting values were used as inputs for an infection risk assessment model, which was used to quantitatively evaluate the effects of various factors, including ventilation rates, respiratory activities, and viral variants, on the infection risk. The findings demonstrate that ventilation has a negligible impact on reducing average risks (less than 10.0%) for short-range scales, but can result in a reduction of average risks by 32.1%-57.4% for room scales. When all passengers wear masks, the average risk reduction ranges from 4.5-folds to 7.5-folds. Based on our analysis, the average total reproduction numbers (R) of subways are 1.4-folds higher than buses, and 2-folds higher than high speed trains. Additionally, it is important to note that the Omicron variant may result in a much higher R value, estimated to be approximately 4.9-folds higher than the Delta variant. To reduce disease transmission, it is important to keep the R value below 1. Thus, two indices have been proposed: time-scale based exposure thresholds and spatial-scale based upper limit warnings. Mask wearing provides the greatest protection against infection in the face of long exposure duration to the omicron epidemic.

Biomimetic method of emergency life channel urban planning in Wuhan using slime mold networks

This study investigated a bio-inspired approach to planning optimal routes for urban hospital life channels to enable better responses to urban public security incidents. An experimental slime mold network and an origin-destination (OD) network model in which the nodes were tertiary hospitals in Wuhan were constructed. Correlation metrics of the two network models were used for network analysis and visualization. The experimental results showed that the slime mold network was better than the OD network in terms of global optimization. Furthermore, significant polarization of the influence value of urban hospital nodes resulted in a power-law distribution. This paper presents an urban planning method in which the biological mechanism of slime mold foraging is applied to construct shortest path networks in an emergency life channels. The results can be used to examine the relationship between urban roads and hospital nodes and the rational of global optimization distribution when planning the locations of new hospitals. A set of replicable and sustainable methods for conducting a biomimetic slime mold experiment to model real environments are presented. This approach provides a novel perspective for modeling emergency life channels.

A cross-sectional survey of climate and COVID-19 crises in young people in Indian slums: context, psychological responses, and agency

Background

COVID-19 and the climate crisis have caused unprecedented disruptions across the world. Climate change has affected the mental health and wellbeing of children and adolescent. Young people with a mental illness and without social support are at an increased risk of climate change induced mental ill-health. COVID-19 resulted in a marked increase of psychological distress. Increase in depression, anxiety and insomnia have increased due to the upheavals that people were experiencing including loss of livelihood and breaking of social bonds.

Methods

This exploratory study adopted a cross sectional survey design using quantitative methods to understand the perceptions, thoughts, and feelings of young people on both the climate and COVID-19 crises, their concerns, and desires for the future and to understand their sense of agency to contribute to the changes that they want to see.

Findings

The findings show that most of the respondents from the sample studied reported nearly similar interference of climate change and COVID-19 on their mental wellbeing. Their climate concern and COVID-19 concern scores were comparable. Tangible experiences of extreme weather events, that were personally experienced or that impacted their family members had a negative impact on their lives, while action around improving the environment had a positive impact.Loss of income, loss of mobility and loss of social contact due to COVID-19 had negative impacts on the respondents, while indulging in leisure activities and bonding with the family had positive impacts. Although the majority of the participants reported having both climate and COVID agency, it did not translate into action to improve the environment.

Interpretation

Young people's activism on climate change and COVID-19 has a positive impact on their mental wellbeing hence more opportunities and platforms must be provided to enable young people to take action on both these crises.

Funding

None.

An innovative approach for predicting pandemic hotspots in complex wastewater networks using graph theory coupled with fuzzy logic

Early prediction of COVID-19 infected communities (potential hotspots) is essential to limit the spread of virus. Diagnostic testing has limitations in big populations because it cannot deliver information at a fast enough rate to stop the spread in its early phases. Wastewater based epidemiology (WBE) experiments showed promising results for brisk detection of 'SARS CoV-2' RNA in urban wastewater. However, a systematic and targeted approach to track COVID-19 virus in the complex wastewater networks at a community level is lacking. This research combines graph network (GN) theory with fuzzy logic to determine the chances of a specific community being a COVID-19 hotspot in a wastewater network. To detect 'SARS-CoV-2' RNA, GN divides wastewater network into communities and fuzzy logic-based inference system is used to identify targeted communities. For the propose of tracking, 4000 sample cases from Minnesota (USA) were tested based on various contributing factors. With a probability score of greater than 0.8, 42% of cases were likely to be designated as COVID-19 hotspots based on multiple demographic characteristics. The research enhances the conventional WBE approach through two novel aspects, viz. (1) by integrating graph theory with fuzzy logic for quick prediction of potential hotspot along with its likelihood percentage in a wastewater network, and (2) incorporating the uncertainty associated with COVID-19 contributing factors using fuzzy membership functions. The targeted approach allows for rapid testing and implementation of vaccination campaigns in potential hotspots. Consequently, governmental bodies can be well prepared to check future pandemics and variant spreading in a more planned manner.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00477-023-02468-3.

The impact of COVID-19 on visitors' wayfinding within healthcare centers

The novel COVID-19 pandemic has caused substantial calamities in developing countries such as Iran, which initially suffered from inadequate infrastructure essential for the pandemic control. Due to the ongoing development of this malady, healthcare centers are recognized as one of the most significant hotspots within public settings so they are directly pertinent to the physical and mental health of visitors. The main objective for conducting the present study is to investigate the impact of the COVID-19 pandemic on the visitors' wayfinding procedure within Qa'em hospital, located in Rasht, northern Iran. The adopted methodology in the present study is based on a comparison between the collected data regarding the wayfinding behavior of visitors before and after the outbreak of the COVID-19 pandemic using mixed methods, namely Space Syntax, gate counting, people following, and semi-structured interviews. The obtained empirical results displayed that visitors were significantly confused and hesitant throughout their wayfinding process after the outbreak of the pandemic. Indeed, spatial accessibility and legibility were not found to be adequate for facilitating the wayfinding of the visitors. Moreover, the requirements for the reconfiguration of furniture layout in the waiting areas, according to the underlying notions of social distancing, became conspicuous as the pragmatic implications for the post-pandemic healthcare centers.

COVID-19-related hospital admission in spouses of partners in at-risk occupations

OBJECTIVE

This study aimed to quantify the risk of COVID-19-related hospital admission in spouses living with partners in at-risk occupations in Denmark during 2020-21.

METHODS

Within a registry-based cohort of all Danish employees (N=2 451 542), we identified cohabiting couples, in which at least one member (spouse) held a job that according to a job exposure matrix entailed low risk of occupational exposure to SARS-CoV-2 (N=192 807 employees, 316 COVID-19 hospital admissions). Risk of COVID-19-related hospital admission in such spouses was assessed according to whether their partners were in jobs with low, intermediate or high risk for infection. Overall and sex-specific incidence rate ratios (IRR) of COVID-19-related hospital admission were computed by Poisson regression with adjustment for relevant covariates.

RESULTS

The risk of COVID-19-related hospital admission was increased among spouses with partners in high-risk occupations [adjusted IRR (IRRadj)1.59, 95% confidence interval (CI) 1.1-2.2], but not intermediate-risk occupations (IRRadj 0.97 95% 0.8-1.3). IRR for having a partner in a high-risk job was elevated during the first three pandemic waves but not in the fourth (IRRadj 0.48 95% CI 0.2-1.5). Sex did not modify the risk of hospital admission.

CONCLUSIONS

SARS-CoV-2 transmission at the workplace may pose an increased risk of severe COVID-19 among spouses in low-risk jobs living with partners in high-risk jobs, which emphasizes the need for preventive measures at the workplace in future outbreaks of epidemic contagious disease. When available, effective vaccines seem essential.

Epidemic dynamics in census-calibrated modular contact network

Network-based models are apt for understanding epidemic dynamics due to their inherent ability to model the heterogeneity of interactions in the contemporary world of intense human connectivity. We propose a framework to create a wire-frame that mimics the social contact network of the population in a geography by lacing it with demographic information. The framework results in a modular network with small-world topology that accommodates density variations and emulates human interactions in family, social, and work spaces. When loaded with suitable economic, social, and urban data shaping patterns of human connectance, the network emerges as a potent decision-making instrument for urban planners, demographers, and social scientists. We employ synthetic networks to experiment in a controlled environment and study the impact of zoning, density variations, and population mobility on the epidemic variables using a variant of the SEIR model. Our results reveal that these demographic factors have a characteristic influence on social contact patterns, manifesting as distinct epidemic dynamics. Subsequently, we present a real-world COVID-19 case study for three Indian states by creating corresponding surrogate social contact networks using available census data. The case study validates that the demography-laced modular contact network reduces errors in the estimates of epidemic variables.

Do the determinants of COVID-19 transmission differ by epidemic wave? Evidence from U.S. counties

This paper uses data from the United States to examine determinants of the spread of COVID-19 during three different epidemic waves. We address how sociodemographic and economic attributes, industry composition, density, crowding in housing, and COVID-19-related variables are associated with the transmission of COVID-19. After controlling for spatial autocorrelation, our findings indicate that the percentage of people in poverty, number of restaurants, and percentage of workers teleworking were associated with the COVID-19 incidence rate during all three waves. Our results also show that dense areas were more vulnerable to the transmission of COVID-19 after the first epidemic wave. Regarding the density of supermarkets, our study elaborates the negative aspects of wholesale retail stores, which likely provide a vulnerable place for virus transmission. Our results suggest that sociodemographic and economic attributes were the determinants of the early phase of the pandemic, while density showed positive association with the transmission during subsequent waves. We provide implications for regions serving as gateway cities with high density and number of population. To add, we further provide evidence that non-pharmaceutical interventions in the early stage may mitigate the virus transmission.

Squatting behavior during the COVID-19 pandemic: The case of the informal settlement "Los Hornos" in Buenos Aires

The COVID-19 outbreak magnified territorial inequalities and increased vulnerability among low-income groups. Inhabitants in informal settlements are structurally disadvantaged in coping with communicative diseases such as the COVID-19 pandemic. Despite that, the pandemic has been accompanied by the proliferation of informal settlements. This study explores how the pandemic caused the squatting on new land with the case of "Los Hornos" in suburban Buenos Aires. We used a random forest algorithm and Google Earth Engine to estimate the rapid growth of a new informal settlement from a series of satellite images from early 2020. We also conducted semi-structured interviews with inhabitants to investigate the link between squatting and COVID-19. The study revealed that squatting on new land during the pandemic was mainly due to economic difficulties, overcrowding in existing informal settlements in the metropolitan center, and speculation in the informal housing market. This case is an example of how the most vulnerable groups bore the brunt of the pandemic, how the households in the existing informal settlement were behaving similar to those in the formal housing market (i.e., away from the urban centers), and how the outbreak had also been an opportunity for collective action of squatting a new land to materialize.

High-resolution estimates of social distancing feasibility, mapped for urban areas in sub-Saharan Africa

Social distancing has been widely-implemented as a public health measure during the COVID-19 pandemic. Despite widespread application of social distancing guidance, the feasibility of people adhering to such guidance varies in different settings, influenced by population density, the built environment and a range of socio-economic factors. Social distancing constraints however have only been identified and mapped for limited areas. Here, we present an ease of social distancing index, integrating metrics on urban form and population density derived from new multi-country building footprint datasets and gridded population estimates. The index dataset provides estimates of social distancing feasibility, mapped at high-resolution for urban areas across 50 countries in sub-Saharan Africa.

Community resilience to pandemics: An assessment framework developed based on the review of COVID-19 literature

The COVID-19 outbreak in 2019 and the challenges it posed to communities around the world, demonstrated the necessity of enhancing the resilience of communities to pandemics. In this regard, assessment frameworks can play an essential role and guide resilience-building efforts. However, the lack of a comprehensive assessment framework has led to a focus on sectoral evaluation. This study aims to propose an integrated framework for assessing the pandemic resilience of communities. For this purpose, we rely on a systematic review of literature indexed in major academic databases. We have thoroughly analyzed a total number of 115 related documents to extract relevant criteria. Findings show that many criteria and factors affect community resilience to pandemics. By inductive content coding in MAXQDA software, we have categorized these criteria into five dimensions of Institutional, Social, Economic, Infrastructural, and Demographic. Good leadership and management, insurance and governmental support, planning and preparation, expertise and labor, and available equipment and technologies are the most important institutional criteria. Communication and collective identity, mutual support, public safety and protection, public awareness, and social justice are the influential social criteria. Economic sustainability and resource availability are criteria of economic resilience. Sufficiency of services, public spaces, housing tenure, and transportation system are the main criteria related to the built environment and infrastructural dimension. Finally, demographic resilience includes physical health, psychological well-being, life quality, and hygiene. Based on these criteria, this study develops an integrated evaluation framework that researchers can implement along with conventional assessment and ranking methods to determine the level of community resilience to pandemics.

Vulnerable or resilient? The response of informal settlements to COVID-19: The case of urban village communities in Beijing

The COVID-19 pandemic has brought topics of the impact, response and adaptation of cities in emergencies to the forefront. When compared with formal settlements, the problems faced by informal settlements are more prominent. We propose the framework of an actor-network theory, substantiated by an empirical study of three typical informal settlements in Haidian District, Beijing, in which the process, characteristics and internal mechanism of the spatial reconstruction of the informal settlements in response to COVID-19 are closely scrutinised. Human actors such as local governments, community volunteers, landlords, tenants and non-human actors all participated in the response to COVID-19 according to their goal vision and political logic, with the local government as the core driving force, forming an integrated actor network. Rooted in the special locality of informal settlements, the actor network was both hierarchical and flexible, and its inherent dynamism has proven to be efficient during COVID-19, resulting in social adaptation and spatial reconstruction. This study contributes to the cautiously optimistic estimate of similar urban community resilience in terms of global epidemics and enriches the understanding of their interlacing dynamics from the perspective of spatial reconstruction.

Mapping urban physical distancing constraints, sub-Saharan Africa: a case study from Kenya

With the onset of the coronavirus disease 2019 (COVID-19) pandemic, public health measures such as physical distancing were recommended to reduce transmission of the virus causing the disease. However, the same approach in all areas, regardless of context, may lead to measures being of limited effectiveness and having unforeseen negative consequences, such as loss of livelihoods and food insecurity. A prerequisite to planning and implementing effective, context-appropriate measures to slow community transmission is an understanding of any constraints, such as the locations where physical distancing would not be possible. Focusing on sub-Saharan Africa, we outline and discuss challenges that are faced by residents of urban informal settlements in the ongoing COVID-19 pandemic. We describe how new geospatial data sets can be integrated to provide more detailed information about local constraints on physical distancing and can inform planning of alternative ways to reduce transmission of COVID-19 between people. We include a case study for Nairobi County, Kenya, with mapped outputs which illustrate the intra-urban variation in the feasibility of physical distancing and the expected difficulty for residents of many informal settlement areas. Our examples demonstrate the potential of new geospatial data sets to provide insights and support to policy-making for public health measures, including COVID-19.

Comparing COVID-19 in the antipodes: Insights from pandemic containment strategies on both sides of the Pacific

That the COVID-19 pandemic is unprecedented in terms of its scale, spread and shocks can be evinced by the myriad of ever-changing responses cities all around the world have rolled out throughout the different waves of outbreaks. Although the threat is similar across the world, it took some time before its reach became global and the waves of outbreak are experienced by cities at different times. While this staggered spread imply that some cities might manage the virus better as they learn from the experiences of cities which had been amongst the earliest to face the virus, the reality is more complicated. In the early stages of the pandemic, the global consensus on the best way to contain the virus swiftly converged in the interlinked strategies of restricting the movement of people and minimizing their social contact. However, the effectiveness of these strategies differ greatly between cities. To that end, this study focuses on COVID-19 responses in two regions (Latin America and Southeast Asia) and examines the evolution of the first wave of COVID-19 outbreaks during 2020 in Singapore, Jakarta (Indonesia), Bogotá (Colombia) and Santiago (Chile). The study is based on a comparative approach and uses a variety of data sources, namely morphology, density, housing concentration, mobility, and governance in the four analyzed cities. The goal is to shed light on the response of city governments in these two different regions in terms of mobility restrictions in order to reduce the cases of new infections. The results show the relevance of urban policies and their territorial approaches, particularly in terms of mobility and public transport networks in the four cities.

Social Determinants of Health in the COVID-19 Pandemic Context of the Migrant Population Living in Settlements in Spain

Objectives: The aim of this study was to describe and evaluate the sociodemographic profile and assess the levels of anxiety and fear, work engagement, and psychological distress on a sample of migrants living in settlements in the province of Huelva (Spain) during the COVID-19 pandemic. Methods: A descriptive cross-sectional study was conducted on a sample of 623 migrants during the months of April to June 2021, based on the Anxiety and Fear of COVID-19 (AMICO) assessment scale, the 9-item Utrecht Work Engagement Scale to assess work engagement, and the General Health Questionnaire (GHQ-12) to analyse psychological distress. Results: A low level of education, dwelling of less than 3 m2 and the desire to return to the country of origin may be related to the presence of anxiety and fear of COVID-19 and lower levels of work engagement. Conclusion: There is a need to improve the study of the concept of health of the migrant population residing in the settlements of Huelva (Spain) and the assessment of their physical and mental health, in an official way.

Mapping local variation in household overcrowding across Africa from 2000 to 2018: a modelling study

BACKGROUND

Household overcrowding is a serious public health threat associated with high morbidity and mortality. Rapid population growth and urbanisation contribute to overcrowding and poor sanitation in low-income and middle- income countries, and are risk factors for the spread of infectious diseases, including COVID-19, and antimicrobial resistance. Many countries do not have adequate surveillance capacity to monitor household overcrowding. Geostatistical models are therefore useful tools for estimating household overcrowding. In this study, we aimed to estimate household overcrowding in Africa between 2000 and 2018 by combining available household survey data, population censuses, and other country-specific household surveys within a geostatistical framework.

METHODS

We used data from household surveys and population censuses to generate a Bayesian geostatistical model of household overcrowding in Africa for the 19-year period between 2000 and 2018. Additional sociodemographic and health-related covariates informed the model, which covered 54 African countries.

FINDINGS

We analysed 287 surveys and population censuses, covering 78 695 991 households. Spatial and temporal variability arose in household overcrowding estimates over time. In 2018, the highest overcrowding estimates were observed in the Horn of Africa region (median proportion 62% [IQR 57-63]); the lowest regional median proportion was estimated for the north of Africa region (16% [14-19]). Overall, 474·4 million (95% uncertainty interval [UI] 250·1 million-740·7 million) people were estimated to be living in overcrowded conditions in Africa in 2018, a 62·7% increase from the estimated 291·5 million (180·8 million-417·3 million) people who lived in overcrowded conditions in the year 2000. 48·5% (229·9 million) of people living in overcrowded conditions came from six African countries (Nigeria, Ethiopia, Democratic Republic of the Congo, Sudan, Uganda, and Kenya), with a combined population of 538·3 million people.

INTERPRETATION

This study incorporated survey and population censuses data and used geostatistical modelling to estimate continent-wide overcrowding over a 19-year period. Our analysis identified countries and areas with high numbers of people living in overcrowded conditions, thereby providing a benchmark for policy planning and the implementation of interventions such as in infectious disease control.

FUNDING

UK Department of Health and Social Care, Wellcome Trust, Bill & Melinda Gates Foundation.

Community- and Data-Driven Services for Multi-Policy Pedestrian Routing

Pedestrian routing is important in a multitude of public spaces, especially those characterized by a large number of newcomers. Their needs may be diverse, with priority for the shortest path, the less crowded or the less polluted one, the accessibility for reduced mobility, or the sheltering from unfavorable weather conditions. Hence, typical graph-based routing must be enriched to support multiple policies, at the choice of each person. The paper proposes a systemic approach and a set of services for orientation and accessibility, which are both community-driven and data-driven, for correctly perceiving the routing necessities and the surrounding situation. The response time to a pathfinding query depends on the types of policies applied and not only on their number, because each of them contributes to the customization of the weighted graph, although it refers to the same physical space traversed by pedestrians. The paper also presents results of loading tests for up to 5000 Virtual Users, inspired from real-life requirements and executed on a graph that models a real building in our university; different policies are applied to assess performance metrics, with simulated community feedback and sensor data.

The mental health of children and young people living in big cities in a revolving postpandemic world

PURPOSE OF REVIEW

The world's population is increasingly urban, with most children and young people growing up and living in cities. Evidence suggests that urbanicity is linked to an increased risk for the development of mental health disorders. Rather than an accumulation of risk factors, urbanization is a complex process that profoundly structures living conditions. In this sense, it is timely to discuss what are the social and structural determinants of mental health of children and young people in such settings.

RECENT FINDINGS

Three domains of determinants of mental health were selected for discussion: economics and living conditions, crime and violence, and urban layouts. For each, we debated realities faced by urban children and young people, providing an overview of recent evidence on implications for mental disorders and well being. We also discuss the potential impacts of the covid-19 pandemic on each domain, as well as recommendations for future action.

SUMMARY

Structural factors are of major relevance for the mental health of children and young people living in cities. The agenda of mental health promotion and prevention must include whole-of-society interventions aimed at improving living conditions, including economic and social capital, violence prevention and urbanistic planning.

Teaching during COVID-19 pandemic in practical laboratory classes of applied biochemistry and pharmacology: A validated fast and simple protocol for detection of SARS-CoV-2 Spike sequences

The pandemic caused by the SARS-CoV-2 virus (COVID-19) is still a major health issue. The COVID-19 pandemic has forced the university teaching to consider in high priority the switch from in-presence teaching to remote teaching, including laboratory teaching. While excellent virtual-laboratory teaching has been proposed and turned out to be very useful, the need of a real-laboratory in-presence teaching is still a major need. This study was aimed at presenting a laboratory exercise focusing (a) on a very challenging therapeutic strategy, i.e. SARS-CoV-2 diagnostics, and (b) on technologies that are playing a central role in applied biochemistry and molecular biology, i.e. PCR and RT-PCR. The aims of the practical laboratory were to determine: (a) the possibility to identify SARS-CoV-2 sequences starting from a recombinant plasmid and (b) the possibility to discriminate cells with respect to the expression of SARS-CoV-2 Spike protein. This activity is simple (cell culture, RNA extraction, RT-qPCR are all well-established technologies), fast (starting from isolated and characterized RNA, few hours are just necessary), highly reproducible (therefore easily employed by even untrained students). We suggest that this laboratory practical exercises should be considered for face-to-face teaching especially if the emergency related to the COVID-19 pandemic is maintained. The teaching protocol here described might be considered in order to perform fast but meaningful in-presence teaching, making feasible the division of crowded classes in low-number cohorts of students, allowing the maintenance of the required social distance.

Rethinking the distribution of urban green spaces in Mexico City: Lessons from the COVID-19 outbreak

The COVID-19 pandemic has exposed inequalities that are expected to widen if no action is taken to support the most marginalized populations. One such inequality is the distribution of urban green spaces (UGS), which are essential to pandemic recovery. Cities that aim to be inclusive and resilient should assess whether access to their UGS is equitably distributed among the population and identify the areas where these spaces are most needed. This study therefore examines the equity of access to UGS in Mexico City at the neighborhood level using network analysis. First, access to UGS was identified at a threshold of 300 m, regardless of UGS size. Second, access was differentiated by the functional level of the UGS, which primarily depends on their size, with larger UGS having more extensive catchment areas. The results of this study suggest a deficit of access to small green spaces in most of the neighborhoods of Mexico City, with the neighborhoods with higher rates of poverty showing an even lower average of UGS access. The results further highlight which neighborhoods in Mexico City should receive priority attention and funding for UGS to mitigate the disproportionate effects of public health crises. This is critical for future city planning and may be used as a roadmap for identifying priority neighborhoods in other cities with similar segregation patterns.

Spatial congruency or mismatch? Analyzing the COVID-19 potential infection risk and urban density as businesses reopen

Countries worldwide are reopening their businesses despite the continuing COVID-19 crisis and the emergence of new variants. In this context, knowing whether the reopening of businesses at various locations exposes higher risk to the public is essential. Whether urban density correlates with the potential infection risk as concluded by previous studies of the COVID-19 pandemic remains unknown. In this study, taking the Macau Peninsula as a testbed, we first identified business locations for daily activities according to the latest point of interest (POI) data and generated the potential risk surface for COVID-19 infection. Then, using the cellular phone network and urban footprint data, we further analyzed the spatial relationship between COVID-19 potential risk and urban density of population and morphology through visual analytics. Results show that while some degree of spatial congruency exists between medium-risk peaks and urban density hotspots, apparent spatial mismatch exists for high-risk peaks, indicating that the traditional planning control based on urban density is inadequate for mitigating public health risks. POI-based spatial layout and configuration better reflecting business services and associated human activities are recommended in future planning and policy-making for more resilient cities in the post-pandemic era.

Investigating the current environmental situation in the Middle East and North Africa (MENA) region during the third wave of COVID-19 pandemic: urban vs. rural context

BACKGROUND

Coronavirus 2019 (COVID-19) pandemic led to a massive global socio-economic tragedy that has impacted the ecosystem. This paper aims to contextualize urban and rural environmental situations during the COVID-19 pandemic in the Middle East and North Africa (MENA) Region.

RESULTS

An online survey was conducted, 6770 participants were included in the final analysis, and 64% were females. The majority of the participants were urban citizens (74%). Over 50% of the urban residents significantly (p < 0.001) reported a reduction in noise, gathering in tourist areas, and gathering in malls and restaurants. Concerning the pollutants, most urban and rural areas have reported an increase in masks thrown in streets (69.49% vs. 73.22%, resp.; p = 0.003). Plastic bags and hospital waste also increased significantly with the same p-value of < 0.001 in urban areas compared with rural ones. The multifactorial logistic model for urban resident predictors achieved acceptable discrimination (AUROC = 0.633) according to age, crowdedness, noise and few pollutants.

CONCLUSION

The COVID-19 pandemic had a beneficial impact on the environment and at the same time, various challenges regarding plastic and medical wastes are rising which requires environmental interventions.

Clinical Severity on Hospital Admission for COVID-19: An Analysis of Social Determinants of Health From an Early Hot Spot in the Southeastern U.S

INTRODUCTION

Disparities in COVID-19 infection, illness severity, hospitalization, and death are often attributed to age and comorbidities, which fails to recognize the contribution of social, environmental, and financial factors on health. The purpose of this study was to examine relationships between social determinants of health (SDOH) and COVID-19 severity.

METHODS

This multicenter retrospective study included adult patients hospitalized with COVID-19 in Southwest Georgia, U.S. The primary outcome was the severity of illness among patients on hospital admission for COVID-19. To characterize the effect of biological and genetic factors combined with SDOH on COVID-19, we used a multilevel analysis to examine patient-level and ZIP code-level data to determine the risk of COVID-19 illness severity at admission.

RESULTS

Of 392 patients included, 65% presented with moderate or severe COVID-19 compared to 35% with critical disease. Compared to moderate or severe COVID-19, increasing levels of Charlson Comorbidity Index (OR 1.15, 95% CI 1.07-1.24), tobacco use (OR 1.85, 95% CI 1.10-3.11), and unemployment or retired versus employed (OR 1.91, 95% CI 1.04-3.50 and OR 2.17, 95% CI 1.17-4.02, respectively) were associated with increased odds of critical COVID-19 in bivariate models. In the multi-level model, ZIP codes with a higher percentage of Black or African American residents (OR 0.94, 95% CI 0.91-0.97) were associated with decreased odds of critical COVID-19.

CONCLUSION

Differences in SDOH did not lead to significantly higher odds of presenting with severe COVID-19 when accounting for patient-level and ZIP code-level variables.

Anxiety and Insomnia Among Urban Slum Dwellers in Bangladesh: The Role of COVID-19 and Its Associated Factors

Background: Although mental health is an important part of health and wellbeing, very little is known about the impact of the COVID-19 pandemic on the mental health of marginalized communities like urban slum dwellers. Our study estimated the prevalence of generalized anxiety disorder and insomnia among the residents of the informal settlements of Dhaka, Bangladesh, during the COVID-19 pandemic. Methods: A cross-sectional phone-based survey was conducted from October to November 2020 among adult residents of five informal settlements of Dhaka city randomly chosen from an existing Urban Health and Demographic Surveillance Systems (UHDSS) run by icddr,b. Data on Generalized Anxiety Disorder-7 (GAD-7) and Insomnia Severity Index (ISI) were collected. A multinomial logistic regression was performed to assess the associated factors of anxiety and insomnia. Results: Of the total 586 participants, the prevalence of mild to severe anxiety and insomnia were 53% and 43%, respectively. As per the multinomial regression analysis, participants with mild anxiety were significantly more likely to be older (>50 years) and afraid of COVID-19 infection. Likewise, participants with moderate/severe anxiety were significantly more likely to share less household facilities (e.g., toilet, kitchen, water) (OR: 2.23; 95% CI: 1.31-3.79), to have difficulties in food availability (OR: 2.76; 95% CI: 1.10-6.93), to be afraid of self (OR: 5.27; 95% CI: 2.82-9.88), and to worry about the family members (OR: 2.26; 95% CI: 1.23-4.17) getting infected. Participants with mild insomnia were significantly more likely to share fewer household facilities and be afraid of being infected with COVID-19 infection. Moreover, participants with moderate/severe insomnia were significantly more likely to be female (OR: 1.90; 95% CI: 1.02-3.56), to receive food aid (OR: 0.50; 95% CI: 0.29-0.88), to be afraid of self (OR: 3.85; 95% CI: 1.81-8.19), and to worry about someone like friends or neighbors (OR: 2.45; 95% CI: 1.07-5.58) getting infected with COVID-19. Conclusions: We found elevated prevalence of both anxiety and insomnia among the urban poor of Bangladesh in the context of COVID-19. This indicates the importance of integrating mental health in the mitigation and recovery efforts related to similar crises for the urban poor in the future.

Ensuring Rights while Protecting Health: The Importance of Using a Human Rights Approach in Implementing Public Health Responses to COVID-19

In response to the COVID-19 pandemic, governments around the world have implemented public health policies that limit individual freedoms in order to control disease transmission. While such limitations on liberties are sometimes necessary for pandemic control, many of these policies have been overly broad or have neglected to consider the costs for populations already susceptible to human rights violations. Furthermore, the pandemic has exacerbated preexisting inequities based on health care access, poverty, racial injustice, refugee crises, and lack of education. The worsening of such human rights violations increases the need to utilize a human rights approach in the response to COVID-19. This paper provides a global overview of COVID-19 public health policy interventions implemented from January 1 to June 30, 2020, and identifies their impacts on the human rights of marginalized populations. We find that over 70% of these public health policies negatively affect human rights in at least one way or for at least one population. We recommend that policy makers take a human rights approach to COVID-19 pandemic control by designing public health policies focused on the most marginalized groups in society. Doing so would allow for a more equitable, realistic, and sustainable pandemic response that is centered on the needs of those at highest risk of COVID-19 and human rights violations.

From pandemic response to portable population health: A formative evaluation of the Detroit mobile health unit program

This article describes our experience developing a novel mobile health unit (MHU) program in the Detroit, Michigan, metropolitan area. Our main objectives were to improve healthcare accessibility, quality and equity in our community during the novel coronavirus pandemic. While initially focused on SARS-CoV-2 testing, our program quickly evolved to include preventive health services. The MHU program began as a location-based SARS-CoV-2 testing strategy coordinated with local and state public health agencies. Community needs motivated further program expansion to include additional preventive healthcare and social services. MHU deployment was targeted to disease "hotspots" based on publicly available SARS-CoV-2 testing data and community-level information about social vulnerability. This formative evaluation explores whether our MHU deployment strategy enabled us to reach patients from communities with heightened social vulnerability as intended. From 3/20/20-3/24/21, the Detroit MHU program reached a total of 32,523 people. The proportion of patients who resided in communities with top quartile Centers for Disease Control and Prevention Social Vulnerability Index rankings increased from 25% during location-based "drive-through" SARS-CoV-2 testing (3/20/20-4/13/20) to 27% after pivoting to a mobile platform (4/13/20-to-8/31/20; p = 0.01). The adoption of a data-driven deployment strategy resulted in further improvement; 41% of the patients who sought MHU services from 9/1/20-to-3/24/21 lived in vulnerable communities (Cochrane Armitage test for trend, p<0.001). Since 10/1/21, 1,837 people received social service referrals and, as of 3/15/21, 4,603 were administered at least one dose of COVID-19 vaccine. Our MHU program demonstrates the capacity to provide needed healthcare and social services to difficult-to-reach populations from areas with heightened social vulnerability. This model can be expanded to meet emerging pandemic needs, but it is also uniquely capable of improving health equity by addressing longstanding gaps in primary care and social services in vulnerable communities.

Quantitative analysis of droplet deposition produced by an electrostatic sprayer on a classroom table by using fluorescent tracer

Due to the ongoing COVID-19 pandemic situation, measures to mitigate the risk of transmission of the SARS-CoV-2 virus in an indoor setting are urgently needed. Among the various types of disinfectant methods, electrostatic spraying is often applied to decontamination in public places. For quantitatively characterizing electrostatic spraying, we developed the novel evaluation method by using a fluorescent tracer. By applying this method, we performed three different experiment cases (static test on a table, static test on a cylinder, and dynamic test on a table) to figure out its unique characteristics (Coulombic fission and wraparound effect) and measure its performance in various aspects. To be specific, bimodal distribution with peak sizes of ~10 and ~100 μm was found due to Coulombic fission. Otherwise, a unimodal distribution with a peak size of ~100 μm occurred for the uncharged droplets. As a result, the effective contact area increased by 40-80 % due to small progeny droplets. The wraparound effect was examined on two different cylinders: copper (Cu) and polyvinyl chloride (PVC) pipe. When the target surface was not charged (Cu 0 kV and PVC 0 kV), the average normalized concentrations on the backside of the cylinder (θ = 180°) increased by around 67 % for charged droplets. Meanwhile, when the target surface was highly charged (PVC -19 kV), the average normalized concentrations at θ = 180° were increased more than two times for charged droplets.

COVID-19 spread in a classroom equipped with partition - A CFD approach

In this study, the motion and distribution of droplets containing coronaviruses emitted by coughing of an infected person in front of a classroom (e.g., a teacher) were investigated using CFD. A 3D turbulence model was used to simulate the airflow in the classroom, and a Lagrangian particle trajectory analysis method was used to track the droplets. The numerical model was validated and was used to study the effects of ventilation airflow speeds of 3, 5, and 7 m/s on the dispersion of droplets of different sizes. In particular, the effect of installing transparent barriers in front of the seats on reducing the average droplet concentration was examined. The results showed that using the seat partitions for individuals can prevent the infection to a certain extent. An increase in the ventilation air velocity increased the droplets' velocities in the airflow direction, simultaneously reducing the trapping time of the droplets by solid barriers. As expected, in the absence of partitions, the closest seats to the infected person had the highest average droplet concentration (3.80 × 10^-8 kg/m³ for the case of 3 m/s).

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.

Exploring the Ethics of Space in Slum Research during COVID-19 through the Lens of Merleau-Ponty

COVID-19 modifies a number of social behaviors and standards that we have been following. In slum research, the multifarious issues posed by COVID-19 are not limited to the increased disadvantages of slum inhabitants, but also to the closure of slums as a physical space conducive to understanding the slum dwellers’ plight. Their voices are silenced at a time when their narratives are critical for developing policies and initiatives to address their predicament. In this regard, the article will examine Merleau-Ponty’s concept of space and then utilize it to create an ethics of space, which is crucial in establishing the viability of virtual space as a creative space that may be used in lieu of traditional slum research. Finally, I discuss the advantages of using virtual space as an alternate space in slum research and how the study might be expanded in the post-COVID era.

Stigmatization, psychological and emotional trauma among frontline health care workers treated for COVID-19 in Lagos State, Nigeria: a qualitative study

BACKGROUND

COVID-19 pandemic has resulted in global health and economic crisis. We investigated the experiences of frontline health care workers recovering from COVID-19 in Lagos State Nigeria.

METHODS

We conducted a qualitative study among frontline health workers recovering from COVID-19 in Lagos State, Nigeria. We interviewed 12 respondents before achieving data saturation. We used a checklist to guide the interview according to the phenomenon under study. Data obtained were analyzed using Colaizzi's phenomenological method.

RESULTS

The study was summarized under five themes: knowledge of COVID-19, exposure, reactions, challenges and recommendations. The respondents were quite knowledgeable on COVID-19, their reactions when informed of their status were denial, anxiety, distress, disorientation, crying for fear of stigmatization, while some were psychologically prepared. Reactions from colleagues, family and friends were encouraging and provided solace for them with a few colleagues and families that had negative reactions. Challenges include anosmia, movement restriction, loneliness, worries about the state of their families, nondisclosure of status to family members, non-conducive isolation centre with limited space, insomnia, stigmatization by health workers at the isolation centre, extended duration of stay, delay in the release of test results and use of ambulance for evacuation to the isolation centres. Coping strategies were watching movies, phone calls, use of social media, listening to music, attending webinars, working on projects and reading spiritual books. Recommendations were early laboratory testing of samples and conveying of results, increase testing capacity, the need of health care workers to be more compassionate, better method of evacuation of people that tested positive to COVID-19, aside the use of ambulance that increases the likelihood of stigmatization and standard guideline for the case management of people recovering from COVID-19 in Lagos state.

CONCLUSIONS

Respondents felt stigmatized and psychologically and morally traumatized. Isolation is a difficult experience and some negative emotions as expressed by previous studies were experienced by the respondents. There is need for increased testing capacity, timely results dissemination, early evacuation and creation of more isolation centres in Lagos State due to the rising number of cases and shortage of bed space.

Resilience and Equity in a Time of Crises: Investing in Public Urban Greenspace Is Now More Essential Than Ever in the US and Beyond

The intersecting negative effects of structural racism, COVID-19, climate change, and chronic diseases disproportionately affect racial and ethnic minorities in the US and around the world. Urban populations of color are concentrated in historically redlined, segregated, disinvested, and marginalized neighborhoods with inadequate quality housing and limited access to resources, including quality greenspaces designed to support natural ecosystems and healthy outdoor activities while mitigating urban environmental challenges such as air pollution, heat island effects, combined sewer overflows and poor water quality. Disinvested urban environments thus contribute to health inequity via physical and social environmental exposures, resulting in disparities across numerous health outcomes, including COVID-19 and chronic diseases such as cancer and cardiovascular diseases (CVD). In this paper, we build off an existing conceptual framework and propose another conceptual framework for the role of greenspace in contributing to resilience and health equity in the US and beyond. We argue that strategic investments in public greenspaces in urban neighborhoods impacted by long term economic disinvestment are critically needed to adapt and build resilience in communities of color, with urgency due to immediate health threats of climate change, COVID-19, and endemic disparities in chronic diseases. We suggest that equity-focused investments in public urban greenspaces are needed to reduce social inequalities, expand economic opportunities with diversity in workforce initiatives, build resilient urban ecosystems, and improve health equity. We recommend key strategies and considerations to guide this investment, drawing upon a robust compilation of scientific literature along with decades of community-based work, using strategic partnerships from multiple efforts in Milwaukee Wisconsin as examples of success.

Clinical subphenotypes in COVID-19: derivation, validation, prediction, temporal patterns, and interaction with social determinants of health

The coronavirus disease 2019 (COVID-19) is heterogeneous and our understanding of the biological mechanisms of host response to the viral infection remains limited. Identification of meaningful clinical subphenotypes may benefit pathophysiological study, clinical practice, and clinical trials. Here, our aim was to derive and validate COVID-19 subphenotypes using machine learning and routinely collected clinical data, assess temporal patterns of these subphenotypes during the pandemic course, and examine their interaction with social determinants of health (SDoH). We retrospectively analyzed 14418 COVID-19 patients in five major medical centers in New York City (NYC), between March 1 and June 12, 2020. Using clustering analysis, 4 biologically distinct subphenotypes were derived in the development cohort (N = 8199). Importantly, the identified subphenotypes were highly predictive of clinical outcomes (especially 60-day mortality). Sensitivity analyses in the development cohort, and rederivation and prediction in the internal (N = 3519) and external (N = 3519) validation cohorts confirmed the reproducibility and usability of the subphenotypes. Further analyses showed varying subphenotype prevalence across the peak of the outbreak in NYC. We also found that SDoH specifically influenced mortality outcome in Subphenotype IV, which is associated with older age, worse clinical manifestation, and high comorbidity burden. Our findings may lead to a better understanding of how COVID-19 causes disease in different populations and potentially benefit clinical trial development. The temporal patterns and SDoH implications of the subphenotypes may add insights to health policy to reduce social disparity in the pandemic.

COVID-19, the Built Environment, and Health

BACKGROUND

Since the dawn of cities, the built environment has both affected infectious disease transmission and evolved in response to infectious diseases. COVID-19 illustrates both dynamics. The pandemic presented an opportunity to implement health promotion and disease prevention strategies in numerous elements of the built environment.

OBJECTIVES

This commentary aims to identify features of the built environment that affect the risk of COVID-19 as well as to identify elements of the pandemic response with implications for the built environment (and, therefore, for long-term public health).

DISCUSSION

Built environment risk factors for COVID-19 transmission include crowding, poverty, and racism (as they manifest in housing and neighborhood features), poor indoor air circulation, and ambient air pollution. Potential long-term implications of COVID-19 for the built environment include changes in building design, increased teleworking, reconfigured streets, changing modes of travel, provision of parks and greenspace, and population shifts out of urban centers. Although it is too early to predict with confidence which of these responses may persist, identifying and monitoring them can help health professionals, architects, urban planners, and decision makers, as well as members of the public, optimize healthy built environments during and after recovery from the pandemic. https://doi.org/10.1289/EHP8888.

Field study to characterize customer flow and ventilation rates in retail buildings in Shenzhen, China

Reduction of the customers' exposure risk of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the retail buildings, i.e., supermarkets and small shops where residents purchase daily necessities is of prime importance during pandemic. In this study, the main influencing factors of the exposure risk of SARS-CoV-2, namely the occupant density, dwell time, and fresh air volume per person, were on-sited measured in 5 supermarkets and 21 small shops in Shenzhen, China. The small shops with an occupant area per person of 4.7 m²/per presented a more crowded environment than the supermarkets with an occupant area per person of 18.8 m²/per. The average dwell time of customers in the supermarkets linearly increased with the floor area and its probability distribution was fitted well by the Gamma distribution with a shape parameter of 3.0. The average dwell time of customers in the supermarkets was relatively longer than the combination of five types of small shops. In addition, the measured average outdoor air change rate of the small shops by natural ventilation was 10.7 h^-1, while that of the supermarkets by mechanical ventilation was only 0.7 h^-1. Correspondingly, the CO₂ concentration in the small shops was 100-150 ppm lower than the supermarkets. The small shops provided an average fresh air volume per person of 216 m³/(h·per), far exceeding the supermarkets with a value of 95 m³/(h·per).

One-Year Lesson: Machine Learning Prediction of COVID-19 Positive Cases with Meteorological Data and Mobility Estimate in Japan

With the wide spread of COVID-19 and the corresponding negative impact on different life aspects, it becomes important to understand ways to deal with the pandemic as a part of daily routine. After a year of the COVID-19 pandemic, it has become obvious that different factors, including meteorological factors, influence the speed at which the disease is spread and the potential fatalities. However, the impact of each factor on the speed at which COVID-19 is spreading remains controversial. Accurate forecasting of potential positive cases may lead to better management of healthcare resources and provide guidelines for government policies in terms of the action required within an effective timeframe. Recently, Google Cloud has provided online COVID-19 forecasting data for the United States and Japan, which would help in predicting future situations on a state/prefecture scale and are updated on a day-by-day basis. In this study, we propose a deep learning architecture to predict the spread of COVID-19 considering various factors, such as meteorological data and public mobility estimates, and applied it to data collected in Japan to demonstrate its effectiveness. The proposed model was constructed using a neural network architecture based on a long short-term memory (LSTM) network. The model consists of multi-path LSTM layers that are trained using time-series meteorological data and public mobility data obtained from open-source data. The model was tested using different time frames, and the results were compared to Google Cloud forecasts. Public mobility is a dominant factor in estimating new positive cases, whereas meteorological data improve their accuracy. The average relative error of the proposed model ranged from 16.1% to 22.6% in major regions, which is a significant improvement compared with Google Cloud forecasting. This model can be used to provide public awareness regarding the morbidity risk of the COVID-19 pandemic in a feasible manner.

COVID-19 in Germany and China: mitigation versus elimination strategy

Background: The COVID-19 pandemic shows variable dynamics in WHO Regions, with lowest disease burden in the Western-Pacific Region. While China has been able to rapidly eliminate transmission of SARS-CoV-2, Germany - as well as most of Europe and the Americas - is struggling with high numbers of cases and deaths. Objective: We analyse COVID-19 epidemiology and control strategies in China and in Germany, two countries which have chosen profoundly different approaches to deal with the epidemic. Methods: In this narrative review, we searched the literature from 1 December 2019, to 4 December 2020. Results: China and several neighbours (e.g. Australia, Japan, South Korea, New Zealand, Thailand) have achieved COVID-19 elimination or sustained low case numbers. This can be attributed to: (1) experience with previous coronavirus outbreaks; (2) classification of SARS-CoV-2 in the highest risk category and consequent early employment of aggressive control measures; (3) mandatory isolation of cases and contacts in institutions; (4) broad employment of modern contact tracking technology; (5) travel restrictions to prevent SARS-CoV-2 re-importation; (6) cohesive communities with varying levels of social control. Conclusions: Early implementation of intense and sustained control measures is key to achieving a near normal social and economic life.