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

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

Wastewater-based prediction of COVID-19 cases using a random forest algorithm with strain prevalence data: A case study of five municipalities in Latvia.

Wastewater-based epidemiology (WBE) is a rapid and cost-effective method that can detect SARS-CoV-2 genomic components in wastewater and can provide an early warning for possible COVID-19 outbreaks up to one or two weeks in advance. However, the quantitative relationship between the intensity of the epidemic and the possible progression of the pandemic is still unclear, necessitating further research. This study investigates the use of WBE to rapidly monitor the SARS-CoV-2 virus from five municipal wastewater treatment plants in Latvia and forecast cumulative COVID-19 cases two weeks in advance. For this purpose, a real-time quantitative PCR approach was used to monitor the SARS-CoV-2 nucleocapsid 1 (N1), nucleocapsid 2 (N2), and E genes in municipal wastewater. The RNA signals in the wastewater were compared to the reported COVID-19 cases, and the strain prevalence data of the SARS-CoV-2 virus were identified by targeted sequencing of receptor binding domain (RBD) and furin cleavage site (FCS) regions employing next-generation sequencing technology. The model methodology for a linear model and a random forest was designed and carried out to ascertain the correlation between the cumulative cases, strain prevalence data, and RNA concentration in the wastewater to predict the COVID-19 outbreak and its scale. Additionally, the factors that impact the model prediction accuracy for COVID-19 were investigated and compared between linear and random forest models. The results of cross-validated model metrics showed that the random forest model is more effective in predicting the cumulative COVID-19 cases two weeks in advance when strain prevalence data are included. The results from this research help inform WBE and public health recommendations by providing valuable insights into the impact of environmental exposures on health outcomes.

Impact of COVID-19 Pandemic on Daily Mobility of the Elderly Living in Small Cities in Lodz Province.

The article presents a study into the impact that the COVID-19 pandemic had on the daily mobility of those over 60 residing in small towns in the Lodz Province. The study determines the impact on the trip destination, trip frequency, preferred means of transport, distance and duration of trips, and length of the target activity. To achieve these objectives, a survey was conducted using the CATI technique (Computer Assisted Telephone Interviewing), which comprised 500 residents of small towns in the Lodz Province aged 60+, who were divided into three classes of small towns (by population size). In order to determine the impact of the COVID-19 pandemic on the daily mobility of those over 60, the tools the authors decided to use descriptive statistics and hypothesis testing. Overall, the pandemic was found to have had only a minor impact on the changes in transport behavior of those over 60 in small towns. Only 9% of respondents declared any effect on their daily mobility. The impact mainly involved a reduction in travel time and frequency, primarily among the oldest residents. Since a low level of daily mobility leads to low social activity, especially for the elderly-with a consequent sense of loneliness or even depression-towns should take measures to improve the already poor situation, one that has been further exacerbated by the pandemic.

The COVID-19 pandemic and its impacts on mass shootings in six major US cities.

BACKGROUND: The COVID-19 pandemic has significant impacts on the US socioeconomic structure. Gun violence is a major public health issue and the effects on this area have not been well-elucidated. The objective of this study was to determine the impacts of the pandemic on mass shootings in six major United States cities with historically high rates of gun violence. METHODS: Mass shooting data were extracted from an open-source database, Gun Violence Archive. Mass shooting was defined as four or more people shot at a single event. Data from six cities with the highest incidence of mass shootings were analyzed in 2019 versus 2020 (Baltimore, Chicago, Detroit, New Orleans, Philadelphia, and St. Louis). Geographic data were examined to assess changes in each city's mass shooting geographic distribution over time. Quantitative changes were assessed using the Area Deprivation Index (ADI), and qualitative data were assessed using ArcGIS. RESULTS: In 2020, the overall percentage of mass shootings increased by 46.7% though there was no change in the distribution of these events when assessed quantitatively (no change in average ADI) nor qualitatively (using ArcGIS). In the six cities analyzed, the total proportion of mass shooting events was unchanged during the pandemic (21.8% vs 20.6%, p = 0.64). Chicago, the US city with the highest incidence of mass shootings, did not experience a significant change in 2020 (n = 34/91, 37.3% vs. n = 53/126, 42.1%, p = 0.57). Baltimore had a significant decrease in mass shooting events (n = 18/91, 19.8% vs. 10/126, 7.9%, p = 0.01). The other four cities had no significant change in the number of mass shootings (p>0.05). CONCLUSION: This study is the first to use ArcGIS technology to describe the patterns of mass shooting in six major US cities during the COVID-19 pandemic. The number of mass shootings in six US cities remained largely unchanged which suggests that changes in mass shootings is likely occurring in smaller cities. Future studies should focus on the changing patterns of homicides in at-risk communities and other possible social influences.

Spread of SARS-CoV-2 Infection in Adult Populations in Cameroon: A Repeated Cross-Sectional Study Among Blood Donors in the Cities of Yaoundé and Douala.

Over a period of about 9 months, we conducted three serosurveys in the two major cities of Cameroon to determine the prevalence of SARS-COV-2 antibodies and to identify factors associated with seropositivity in each survey. We conducted three independent cross-sectional serosurveys of adult blood donors at the Central Hospital in Yaoundé (CHY), the Jamot Hospital in Yaoundé (JHY) and at the Laquintinie Hospital in Douala (LHD) who consented in writing to participate. Before blood sampling, a short questionnaire was administered to participants to collect their sociodemographic and clinical characteristics. We included a total of 743, 1202, and 1501 participants in the first (January 25-February 15, 2021), second (May 03-28, 2021), and third (November 29-December 31, 2021) surveys, respectively. The adjusted seroprevalence increased from 66.3% (95% CrI 61.1-71.3) in the first survey to 87.2% (95% CrI 84.0-90.0) in the second survey, and 98.4% (95% CrI 96.8-99.7) in the third survey. In the first survey, study site, participant occupation, and comorbid conditions were associated with SARS-CoV-2 seropositivity, whereas only study site remained associated in the second survey. None of the factors studied was significantly associated with seropositivity in the third survey. Together, the data suggest a rapid initial spread of SARS-CoV-2 in the study population, independent of the sociodemographic parameters assessed.

Temporal relation between human mobility, climate, and COVID-19 disease.

Using the example of the city of São Paulo (Brazil), in this paper, we analyze the temporal relation between human mobility and meteorological variables with the number of infected individuals by the COVID-19 disease. For the temporal relation, we use the significant values of distance correlation t0(DC), which is a recently proposed quantity capable of detecting nonlinear correlations between time series. The analyzed period was from February 26, 2020 to June 28, 2020. Fewer movements in recreation and transit stations and the increase in the maximal temperature have strong correlations with the number of newly infected cases occurring 17 days after. Furthermore, more significant changes in grocery and pharmacy, parks, and recreation and sudden changes in the maximal pressure occurring 10 and 11 days before the disease begins are also correlated with it. Scanning the whole period of the data, not only the early stage of the disease, we observe that changes in human mobility also primarily affect the disease for 0-19 days after. In other words, our results demonstrate the crucial role of the municipal decree declaring an emergency in the city to influence the number of infected individuals.

Unveiling the paths of COVID-19 in a large city based on public transportation data.

Human mobility plays a key role in the dissemination of infectious diseases around the world. However, the complexity introduced by commuting patterns in the daily life of cities makes such a role unclear, especially at the intracity scale. Here, we propose a multiplex network fed with 9 months of mobility data with more than 107 million public bus validations in order to understand the relation between urban mobility and the spreading of COVID-19 within a large city, namely, Fortaleza in the northeast of Brazil. Our results suggest that the shortest bus rides in Fortaleza, measured in the number of daily rides among all neighborhoods, decreased [Formula: see text]% more than the longest ones after an epidemic wave. Such a result is the opposite of what has been observed at the intercity scale. We also find that mobility changes among the neighborhoods are synchronous and geographically homogeneous. Furthermore, we find that the most central neighborhoods in mobility are the first targets for infectious disease outbreaks, which is quantified here in terms of the positive linear relation between the disease arrival time and the average of the closeness centrality ranking. These central neighborhoods are also the top neighborhoods in the number of reported cases at the end of an epidemic wave as indicated by the exponential decay behavior of the disease arrival time in relation to the number of accumulated reported cases with decay constant [Formula: see text] days. We believe that these results can help in the development of new strategies to impose restriction measures in the cities guiding decision-makers with smart actions in public health policies, as well as supporting future research on urban mobility and epidemiology.

Individual-based simulation model for COVID-19 transmission in Daegu, Korea.

OBJECTIVES: The aims of this study were to obtain insights into the current coronavirus disease 2019 (COVID-19) epidemic in the city of Daegu, which accounted for 6,482 of the 9,241 confirmed cases in Korea as of March 26, 2020, to predict the future spread, and to analyze the impact of school opening. METHODS: Using an individual-based model, we simulated the spread of COVID-19 in Daegu. An individual can be infected through close contact with infected people in a household, at work/school, and at religious and social gatherings. We created a synthetic population from census sample data. Then, 9,000 people were randomly selected from the entire population of Daegu and set as members of the Shincheonji Church. We did not take into account population movements to and from other regions in Korea. RESULTS: Using the individual-based model, the cumulative confirmed cases in Daegu through March 26, 2020, were reproduced, and it was confirmed that the hotspot, i.e., the Shincheonji Church had a different probability of infection than non-hotspot, i.e., the Daegu community. For 3 scenarios (I: school closing, II: school opening after April 6, III: school opening after April 6 and the mean period from symptom onset to hospitalization increasing to 4.3 days), we predicted future changes in the pattern of COVID-19 spread in Daegu. CONCLUSIONS: Compared to scenario I, it was found that in scenario III, the cumulative number of patients would increase by 107 and the date of occurrence of the last patient would be delayed by 92 days.

The Evaluation of Geographical Health Facilities Structure in Makassar City, Indonesia.

Cities across the world, during the last period, have been shocked by the outbreak of the COVID-19 pandemic. The world of planning has since persevered in providing a response, in terms of how to anticipate this outbreak in the future. Various kinds of concepts have been issued, with various views and points of view. However, one of the needs for this planning is an appropriate evaluation of the geographic structure of existing health facilities, in order to properly provide consideration for future urban planning. This study attempts to provide an integrated model of how to evaluate the geographic structure of health facilities with a case study in Makassar City, Indonesia. By combining big data and spatial analysis, it is expected that it will find patterns and directions for acceptable health facilities planning.

Resolving the enigma of Iquitos and Manaus: A modeling analysis of multiple COVID-19 epidemic waves in two Amazonian cities.

The two nearby Amazonian cities of Iquitos and Manaus endured explosive COVID-19 epidemics and may well have suffered the world's highest infection and death rates over 2020, the first year of the pandemic. State-of-the-art epidemiological and modeling studies estimated that the populations of both cities came close to attaining herd immunity (>70% infected) at the termination of the first wave and were thus protected. This makes it difficult to explain the more deadly second wave of COVID-19 that struck again in Manaus just months later, simultaneous with the appearance of a new P.1 variant of concern, creating a catastrophe for the unprepared population. It was suggested that the second wave was driven by reinfections, but the episode has become controversial and an enigma in the history of the pandemic. We present a data-driven model of epidemic dynamics in Iquitos, which we also use to explain and model events in Manaus. By reverse engineering the multiple epidemic waves over 2 y in these two cities, the partially observed Markov process model inferred that the first wave left Manaus with a highly susceptible and vulnerable population (≈40% infected) open to invasion by P.1, in contrast to Iquitos (≈72% infected). The model reconstructed the full epidemic outbreak dynamics from mortality data by fitting a flexible time-varying reproductive number [Formula: see text] while estimating reinfection and impulsive immune evasion. The approach is currently highly relevant given the lack of tools available to assess these factors as new SARS-CoV-2 virus variants appear with different degrees of immune evasion.

A Study of Public Attitudes toward Shanghai's Image under the Influence of COVID-19: Evidence from Comments on Sina Weibo.

With the advent of the Internet era, Chinese users tend to choose to express their opinions on social media platforms represented by Sina Weibo. The changes in people's emotions toward cities from the microblogging texts can reflect the image of cities presented on mainstream social media, and thus target a good image of cities. In this paper, we collected microblog data containing "Shanghai" from 1 January 2019 to 1 September 2022 by Python technology, and we used three methods: Term Frequency-Inverse Document Frequency keyword statistics, Latent Dirichlet Allocation theme model construction, and sentiment analysis by Zhiwang Sentiment Dictionary. We also explore the impact of the COVID-19 epidemic on Shanghai's urban image in the context of the "Shanghai Territorial Static Management", an important public opinion topic during the COVID-19 epidemic. The results of the study show that the "Shanghai-wide static management" of COVID-19 epidemic has significantly reduced the public's perception of Shanghai and negatively affected the city's image. By analyzing the data results, we summarize the basic characteristics of Shanghai's city image and provide strategies for communicating Shanghai's city image in the post-epidemic era.

Spatial-temporal distribution of incidence, mortality, and case-fatality ratios of coronavirus disease 2019 and its social determinants in Brazilian municipalities.

The COVID-19 pandemic caused impact on public health worldwide. Brazil gained prominence during the pandemic due to the magnitude of disease. This study aimed to evaluate the spatial-temporal dynamics of incidence, mortality, and case fatality of COVID-19 and its associations with social determinants in Brazilian municipalities and epidemiological week. We modeled incidence, mortality, and case fatality rates using spatial-temporal Bayesian model. "Bolsa Família Programme" (BOLSAFAM) and "proportional mortality ratio" (PMR) were inversely associated with the standardized incidence ratio (SIR), while "health insurance coverage" (HEALTHINSUR) and "Gini index" were directly associated with the SIR. BOLSAFAM and PMR were inversely associated with the standardized mortality ratio (SMR) and standardized case fatality ratio (SCFR). The highest proportion of excess risk for SIR and the SMR started in the North, expanding to the Midwest, Southeast, and South regions. The highest proportion of excess risk for the SCFR outcome was observed in some municipalities in the North region and in the other Brazilian regions. The COVID-19 incidence and mortality in municipalities that most benefited from the cash transfer programme and with better social development decreased. The municipalities with a higher proportion of non-whites had a higher risk of becoming ill and dying from the disease.

Research of the impact of economic decline on air quality in Wuhan under COVID-19 epidemic.

A novel economic impact model is proposed by this paper to analyze the impact of economic downturn on the air quality in Wuhan during the epidemic period, and to explore the effective solutions to improve the urban air pollution. The Space Optimal Aggregation Model (SOAM) is used to evaluate the air quality of Wuhan from January to April in 2019 and 2020. The analysis results show that the air quality of Wuhan from January to April 2020 is better than that of the same period in 2019, and it shows a gradually better trend. This shows that although the measures of household isolation, shutdown and production stoppage adopted during the epidemic period in Wuhan caused economic downturn, it objectively improved the air quality of the city. In addition, the impact of economic factors on PM2.5, SO2 and NO2 is 19%, 12% and 49% respectively calculated by the SOMA. This shows that industrial adjustment and technology upgrading for enterprises that emit a large amount of NO2 can greatly improve the air pollution situation in Wuhan. The SOMA can be extended to any city to analyze the impact of the economy on the composition of air pollutants, and it has extremely important application value at the level of industrial adjustment and transformation policy formulation.

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.

Association of the corona virus (Covid-19) epidemic with environmental risk factors.

The current outbreak of the novel coronavirus SARS-CoV-2 (coronavirus disease 2019; previously 2019-nCoV), epicenter in Hubei Province (Wuhan), People's Republic of China, has spread too many other countries. The transmission of the corona virus occurs when people are in the incubation stage and do not have any symptoms. Therefore, the role of environmental factors such as temperature and wind speed becomes very important. The study of Acute Respiratory Syndrome (SARS) indicates that there is a significant relationship between temperature and virus transmission and three important factors, namely temperature, humidity and wind speed, cause SARS transmission. Daily data on the incidence and mortality of Covid-19 disease were collected from World Health Organization (WHO) website and World Meter website (WMW) for several major cities in Iran and the world. Data were collected from February 2020 to September 2021. Meteorological data including temperature, air pressure, wind speed, dew point and air quality index (AQI) index are extracted from the website of the World Meteorological Organization (WMO), The National Aeronautics and Space Administration (NASA) and the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Statistical analysis carried out for significance relationships. The correlation coefficient between the number of infected people in one day and the environmental variables in the countries was different from each other. The relationship between AQI and number of infected was significant in all cities. In Canberra, Madrid and Paris, a significant inverse relationship was observed between the number of infected people in one day and wind speed. There is a significant positive relationship between the number of infected people in a day and the dew point in the cities of Canberra, Wellington and Washington. The relationship between the number of infected people in one day and Pressure was significantly reversed in Madrid and Washington, but positive in Canberra, Brasilia, Paris and Wuhan. There was significant relationship between Dew point and prevalence. Wind speed showed a significant relationship in USA, Madrid and Paris. AQI was strongly associated with the prevalence of covid19. The purpose of this study is to investigate some environmental factors in the transmission of the corona virus.

Effect of cities and distance on COVID-19 spreading in the United States.

The COVID-19 pandemic has evolved over time through multiple spatial and temporal dynamics. The varying extent of interactions among different geographical areas can result in a complex pattern of spreading so that influences between these areas can be hard to discern. Here, we use cross-correlation analysis to detect synchronous evolution and potential interinfluences in the time evolution of new COVID-19 cases at the county level in the United States. Our analysis identified two main time periods with distinguishable features in the behavior of correlations. In the first phase, there were few strong correlations that only emerged between urban areas. In the second phase of the epidemic, strong correlations became widespread and there was a clear directionality of influence from urban-to-rural areas. In general, the effect of distance between two counties was much weaker than that of the counties' population. Such analysis can provide possible clues on the evolution of the disease and may identify parts of the country where intervention may be more efficient in limiting the disease spread.

A wastewater-based risk index for SARS-CoV-2 infections among three cities on the Canadian Prairie.

Wastewater surveillance (WWS) is useful to better understand the spreading of coronavirus disease 2019 (COVID-19) in communities, which can help design and implement suitable mitigation measures. The main objective of this study was to develop the Wastewater Viral Load Risk Index (WWVLRI) for three Saskatchewan cities to offer a simple metric to interpret WWS. The index was developed by considering relationships between reproduction number, clinical data, daily per capita concentrations of virus particles in wastewater, and weekly viral load change rate. Trends of daily per capita concentrations of SARS-CoV-2 in wastewater for Saskatoon, Prince Albert, and North Battleford were similar during the pandemic, suggesting that per capita viral load can be useful to quantitatively compare wastewater signals among cities and develop an effective and comprehensible WWVLRI. The effective reproduction number (Rt) and the daily per capita efficiency adjusted viral load thresholds of 85 × 106 and 200 × 106 N2 gene counts (gc)/population day (pd) were determined. These values with rates of change were used to categorize the potential for COVID-19 outbreaks and subsequent declines. The weekly average was considered 'low risk' when the per capita viral load was 85 × 106 N2 gc/pd. A 'medium risk' occurs when the per capita copies were between 85 × 106 and 200 × 106 N2 gc/pd. with a rate of change <100 %. The start of an outbreak is indicated by a 'medium-high' risk classification when the week-over-week rate of change was >100 %, and the absolute magnitude of concentrations of viral particles was >85 × 106 N2 gc/pd. Lastly, a 'high risk' occurs when the viral load exceeds 200 × 106 N2 gc/pd. This methodology provides a valuable resource for decision-makers and health authorities, specifically given the limitation of COVID-19 surveillance based on clinical data.

The impact of Brazil's transport network on the spread of COVID-19.

The transport network between cities is key in understanding epidemic outbreaks, especially in a vast country like Brazil with 5569 cities spread out over 8.5 million square kilometers. In order to study the COVID-19 spread in Brazil, we built a transport network where each city is a node and the edges are connections by land and air. Our findings have shown that by adding air connections, the average path length substantially decreases (70%) while the clustering coefficient remains almost unchanged, very typical of small-world networks. The airways are shortcuts connecting previously distant cities and hubs, therefore shrinking the distances in the network. Also, the cities with airports are central nodes, which makes them dissemination hotspots and key targets for interventions.

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

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