COVID-19 Infection and Mortality: Association with PM2.5 Concentration and Population Density—An Exploratory Study

A Literature Review of the Effects of Air Pollution on COVID-19 Health Outcomes Worldwide: Statistical Challenges and Data Visualization

Several peer-reviewed papers and reviews have examined the relationship between exposure to air pollution and COVID-19 spread and severity. However, many of the existing reviews on this topic do not extensively present the statistical challenges associated with this field, do not provide comprehensive guidelines for future researchers, and review only the results of a relatively small number of papers. We reviewed 139 papers, 127 of which reported a statistically significant positive association between air pollution and adverse COVID-19 health outcomes. Here, we summarize the evidence, describe the statistical challenges, and make recommendations for future research. To summarize the 139 papers with data from geographical locations around the world, we also present anopen-source data visualization tool that summarizes these studies and allows the research community to contribute evidence as new research papers are published.

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

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

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

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

Structural Differences of PM2.5 Spatial Correlation Networks in Ten Metropolitan Areas of China

The cross-impact of environmental pollution among cities has been reported in more research works recently. To implement the coordinated control of environmental pollution, it is necessary to explore the structural characteristics and influencing factors of the PM2.5 spatial correlation network from the perspective of the metropolitan area. This paper utilized the gravity model to construct the PM2.5 spatial correlation network of ten metropolitan areas in China from 2019 to 2020. After analyzing the overall characteristics and node characteristics of each spatial correlation network based on the social network analysis (SNA) method, the quadratic assignment procedure (QAP) regression analysis method was used to explore the influence mechanism of each driving factor. Patent granted differences, as a new indicator, were also considered during the above. The results showed that: (1) In the overall network characteristics, the network density of Chengdu and the other three metropolitan areas displayed a downward trend in two years, and the network density of Wuhan and Chengdu was the lowest. The network density and network grade of Hangzhou and the other four metropolitan areas were high and stable, and the network structure of each metropolitan area was unstable. (2) From the perspective of the node characteristics, the PM2.5 spatial correlation network all performed trends of centralization and marginalization. Beijing-Tianjin-Hebei and South Central Liaoning were “multi-core” metropolitan areas, and the other eight were “single-core” metropolitan areas. (3) The analysis results of QAP regression illustrated that the top three influencing factors of the six metropolitan areas were geographical locational relationship, the secondary industrial proportion differences, respectively, and patent granted differences, and the other metropolitan areas had no dominant influencing factors.

Does air pollution explain COVID-19 fatality and mortality rates? A multi-city study in São Paulo state, Brazil

Since air pollution compromise the respiratory system and COVID-19 disease is caused by a respiratory virus, it is expected that air pollution plays an important role in the current COVID-19 pandemic. Exploratory studies have observed positive associations between air pollution and COVID-19 cases, deaths, fatality, and mortality rate. However, no study focused on Brazil, one of the most affected countries by the pandemic. Thus, this study aimed to understand how long-term exposure to PM₁₀, PM_2.5, and NO₂ contributed to COVID-19 fatality and mortality rates in São Paulo state in 2020. Air quality data between 2015 and 2019 in 64 monitoring stations within 36 municipalities were considered. The COVID-19 fatality was calculated considering cases and deaths from the government's official data and the mortality rate was calculated considering the 2020 population. Linear regression models were well-fitted for PM_2.5 concentration and fatality (R² = 0.416; p = 0.003), NO₂ concentration and fatality (R² = 0.232; p = 0.005), and NO₂ concentration and mortality (R² = 0.273; p = 0.002). This study corroborates other authors' findings and enriches the discussion for having considered a longer time series to represent long-term exposure to the pollutants and for having considered one of the regions with the highest incidence of COVID-19 in the world. Thus, it reinforces measures to reduce the concentration of air pollutants which are essential for public health and will increase the chance to survive in future respiratory disease epidemics.

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

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

Positive association between outdoor air pollution and the incidence and severity of COVID-19. A review of the recent scientific evidences

In June 2020, we published a review focused on assessing the influence of various air pollutants on the transmission of SARS-CoV-2, and the severity of COVID-19 in patients infected by the coronavirus. The results of most of those reviewed studies suggested that chronic exposure to certain air pollutants might lead to more severe and lethal forms of COVID-19, as well as delays/complications in the recovery of the patients. Since then, a notable number of studies on this topic have been published, including also various reviews. Given the importance of this issue, we have updated the information published since our previous review. Taking together the previous results and those of most investigations now reviewed, we have concluded that there is a significant association between chronic exposure to various outdoor air pollutants: PM_2.5, PM₁₀, O₃, NO₂, SO₂ and CO, and the incidence/risk of COVID-19 cases, as well as the severity/mortality of the disease. Unfortunately, studies on the potential influence of other important air pollutants such as VOCs, dioxins and furans, or metals, are not available in the scientific literature. In relation to the influence of outdoor air pollutants on the transmission of SARS-CoV-2, although the scientific evidence is much more limited, some studies point to PM_2.5 and PM₁₀ as potential airborne transmitters of the virus. Anyhow, it is clear that environmental air pollution plays an important negative role in COVID-19, increasing its incidence and mortality.

Open Business Model of COVID-19 Transformation of an Urban Public Transport System: The Experience of a Large Russian City

Dialectics, or developmental transformation, would eventually cause any system to change. The level and depth of these changes vary and depend on the power of external influence and system reservation mechanisms. The art of managing system processes consists of two main aspects. The first aspect involves the sagacity of managers and predicting general environmental change trends (and their impacts on the managed system). The second involves adjusting to these trends, maximizing possible benefits, and minimizing the negative manifestations of this process. Innovation plays an important role, contributing to system transformations with maximal effect and minimal loss. Public transport systems are important elements in cities, as they provide spatial mobility for at least half of the citizens of a city who cannot use individual transportation. Modern urbanization and peculiarities of the social–economic statuses of many citizens contribute to the fact that organized public transportation is unprofitable. The low solvency of citizens who use public transportation services means that passenger transport systems do not work with enough profitability. As a result, governing institutions often choose to subsidize unprofitable transporter activities, thereby prolonging the functioning of unprofitable routes. This is possible only in conditions of sustainability (in regards to a non-optimal system), when the environment is calm, and its negative impact is low. “Black swans” (according to N. Taleb) are bifurcation factors that break the sustainability of non-optimal system. Urban public transport (UPT) of a large Russian city, Tyumen, experienced it in 2020, in connection with the COVID-19 lockdown. The sharp decrease in population mobility in Tyumen, in 2020–2021, caused the need for a complete transformation of the transport service system. However, managers did not want to fundamentally change a system that consensually suited most counterparties. The search for new balances in a system that demands transformation is one way for sustainable provision. This article looks at the transformation and sustainability of a UPT system in the large Russian city of Tyumen, under conditions affected by the negative impact of COVID-19. Results of a comparative (i.e., pre-crisis (2019) and crisis (2020)) Pareto analysis of the contributions of different UPT routes are presented. Transformation of the structure of the UPT route system can overcome the “crisis” COVID-19 period and minimize its financial-economic costs.

"Distance-Driven" Versus "Density-Driven": Understanding the Role of "Source-Case" Distance and Gathering Places in the Localized Spatial Clustering of COVID-19-A Case Study of the Xinfadi Market, Beijing (China)

The frequent occurrence of local COVID-19 today gives a strong necessity to better understand the effects of "source-case" distance and gathering places, which are often considered to be the key factors of the localized spatial clustering of an epidemic. In this study, the localized spatial clustering of COVID-19 cases, which originated in the Xinfadi market in Beijing from June-July 2020, was investigated by exploring the spatiotemporal characteristics of the clustering using descriptive statistics, point pattern analysis, and spatial autocorrelation calculation approaches. Spatial lag zero-inflated negative binomial regression model and geographically weighted Poisson regression with spatial effects were also introduced to explore the factors which influenced the clustering of COVID-19 cases at the micro spatial scale. It was found that the local epidemic can be significantly divided into two stages which are asymmetric in time. A significant spatial spillover effect of COVID-19 was identified in both global and local modeling estimation. The dominant role of the "source-case" distance effect, which was reflected in both global and local scales, was revealed. Relatively, the role of gathering places is not significant at the initial stage of the epidemic, but the upward trend of the significance of some places is obvious. The trend from "distance-driven" to "density-driven" of the localized spatial clustering of COVID-19 was predicted. The effectiveness of blocking the transformation trend will be a key issue for the global response to the local COVID-19.

How Culture and Sociopolitical Tensions Might Influence People’s Acceptance of COVID-19 Control Measures That Use Individual-Level Georeferenced Data

This study extends an earlier study in the United States and South Korea on people’s privacy concerns for and acceptance of COVID-19 control measures that use individual-level georeferenced data (IGD). Using a new dataset collected via an online survey in Hong Kong, we first examine the influence of culture and recent sociopolitical tensions on people’s privacy concerns for and acceptance of three types of COVID-19 control measures that use IGD: contact tracing, self-quarantine monitoring, and location disclosure. We then compare Hong Kong people’s views with the views of people in the United States and South Korea using the pooled data of the three study areas. The results indicate that, when compared to people in the United States and South Korea, people in Hong Kong have a lower acceptance rate for digital contact tracing and higher acceptance rates for self-quarantine monitoring using e-wristbands and location disclosure. Further, there is geographic heterogeneity in the age and gender differences in privacy concerns, perceived social benefits, and acceptance of COVID-19 control measures: young people (age < 24) and women in Hong Kong and South Korea have greater privacy concerns than men. Further, age and gender differences in privacy concerns, perceived social benefits, and acceptance of COVID-19 control measures in Hong Kong and South Korea are larger than those in the United States, and people in Hong Kong have the largest age and gender differences in privacy concerns, perceived social benefits, and acceptance of COVID-19 measures among the three study areas.