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This study evaluates the association between antivirals (Molnupiravir and Nirmatrelvir-Ritonavir) and all-cause and respiratory mortality and organ dysfunction among high-risk COVID-19 patients during an Omicron outbreak. Two cohorts, Nirmatrelvir-Ritonavir versus control and Molnupiravir versus control, were constructed with inverse probability treatment weighting to balance baseline characteristics. Cox proportional hazards models evaluated the association of their use with all-cause mortality, respiratory mortality, and all-cause sepsis (a composite of circulatory shock, respiratory failure, acute liver injury, coagulopathy, and acute liver impairment). Patients recruited were hospitalized and diagnosed with the COVID-19 Omicron variant between February 22, 2022 and April 15, 2022, and followed up until May 15, 2022. The study included 17,704 patients. There were 4.67 and 22.7 total mortalities per 1000 person-days in the Nirmatrelvir-Ritonavir and control groups respectively before adjustment (weighted incidence rate ratio, - 18.1 [95% CI - 23.0 to - 13.2]; hazard ratio, 0.18 [95% CI, 0.11-0.29]). There were 6.64 and 25.9 total mortalities per 1000 person-days in the Molnupiravir and control groups respectively before adjustment (weighted incidence rate ratio per 1000 person-days, - 19.3 [95% CI - 22.6 to - 15.9]; hazard ratio, 0.23 [95% CI 0.18-0.30]). In all-cause sepsis, there were 13.7 and 35.4 organ dysfunction events per 1000 person-days in the Nirmatrelvir-Ritonavir and control groups respectively before adjustment (weighted incidence rate ratio per 1000 person-days, - 21.7 [95% CI - 26.3 to - 17.1]; hazard ratio, 0.44 [95% CI 0.38-0.52]). There were 23.7 and 40.8 organ dysfunction events in the Molnupiravir and control groups respectively before adjustment (weighted incidence ratio per 1000 person-days, - 17.1 [95% CI, - 20.6 to - 13.6]; hazard ratio, 0.63 [95% CI 0.58-0.69]). Among COVID-19 hospitalized patients, use of either Nirmatrelvir-Ritonavir or Molnupiravir compared with no antiviral use was associated with a significantly lower incidence of 28-days all-cause and respiratory mortality and sepsis., (© 2023. The Author(s).)

Background: Nosocomial outbreaks with superspreading of coronavirus disease 2019 due to a possible airborne transmission have not been reported., Methods: Epidemiological analysis, environmental samplings, and whole-genome sequencing (WGS) were performed for a hospital outbreak., Results: A superspreading event that involved 12 patients and 9 healthcare workers (HCWs) occurred within 9 days in 3 of 6 cubicles at an old-fashioned general ward with no air exhaust built within the cubicles. The environmental contamination by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA was significantly higher in air grilles (>2 m from patients' heads and not within reach) than on high-touch clinical surfaces (36.4%, 8 of 22 vs 3.4%, 1 of 29, P = .003). Six (66.7%) of 9 contaminated air exhaust grilles were located outside patient cubicles. The clinical attack rate of patients was significantly higher than of HCWs (15.4%, 12 of 78 exposed patients vs 4.6%, 9 of 195 exposed HCWs, P = .005). Moreover, the clinical attack rate of ward-based HCWs was significantly higher than of nonward-based HCWs (8.1%, 7 of 68 vs 1.8%, 2 of 109, P = .045). The episodes (mean ± standard deviation) of patient-care duty assignment in the cubicles was significantly higher among infected ward-based HCWs than among noninfected ward-based HCWs (6.0 ± 2.4 vs 3.0 ± 2.9, P = .012) during the outbreak period. The outbreak strains belong to SARS-CoV-2 lineage B.1.36.27 (GISAID clade GH) with the unique S-T470N mutation on WGS., Conclusions: This nosocomial point source superspreading event due to possible airborne transmission demonstrates the need for stringent SARS-CoV-2 screening at admission to healthcare facilities and better architectural design of ventilation systems to prevent such outbreaks. Portable high-efficiency particulate filters were installed in each cubicle to improve ventilation before resumption of clinical service., (© The Author(s) 2021. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com.)

With the COVID-19 vaccination widely implemented in most countries, propelled by the need to revive the tourism economy, there is a growing prospect for relieving the social distancing regulation and reopening borders in tourism-oriented countries and regions. This need incentivizes stakeholders to develop border control strategies that fully evaluate health risks if mandatory quarantines are lifted. In this study, we have employed a computational approach to investigate the contact tracing integrated policy in different border-reopening scenarios in Hong Kong, China. Explicitly, by reconstructing the COVID-19 transmission from historical data, specific scenarios with joint effects of digital contact tracing and other concurrent measures (i.e., controlling arrival population and community nonpharmacological interventions) are applied to forecast the future development of the pandemic. Built on a modified SEIR epidemic model with a 30% vaccination coverage, the results suggest that scenarios with digital contact tracing and quick isolation intervention can reduce the infectious population by 92.11% compared to those without contact tracing. By further restricting the inbound population with a 10,000 daily quota and applying moderate-to-strong community nonpharmacological interventions (NPIs), the average daily confirmed cases in the forecast period of 60 days can be well controlled at around 9 per day (95% CI: 7-12). Two main policy recommendations are drawn from the study. First, digital contact tracing would be an effective countermeasure for reducing local virus spread, especially when it is applied along with a moderate level of vaccination coverage. Second, implementing a daily quota on inbound travelers and restrictive community NPIs would further keep the local infection under control. This study offers scientific evidence and prospective guidance for developing and instituting plans to lift mandatory border control policies in preparing for the global economic recovery.

The impact of Coronavirus Disease 2019 (COVID-19) on cause-specific mortality has been investigated on a global scale. However, less is known about the excess all-cause mortality and air pollution-human activity responses. This study estimated the weekly excess all-cause mortality during COVID-19 and evaluated the impacts of air pollution and human activities on mortality variations during the 10th to 52nd weeks of 2020 among sixteen countries. A SARIMA model was adopted to estimate the mortality benchmark based on short-term mortality during 2015-2019 and calculate excess mortality. A quasi-likelihood Poisson-based GAM model was further applied for air pollution/human activity response evaluation, namely ground-level NO 2 and PM 2.5 and the visit frequencies of parks and workplaces. The findings showed that, compared with COVID-19 mortality (i.e., cause-specific mortality), excess all-cause mortality changed from -26.52% to 373.60% during the 10th to 52nd weeks across the sixteen countries examined, revealing higher excess all-cause mortality than COVID-19 mortality in most countries. For the impact of air pollution and human activities, the average country-level relative risk showed that one unit increase in weekly NO 2 , PM 2.5 , park visits and workplace visits was associated with approximately 1.54% increase and 0.19%, 0.23%, and 0.23% decrease in excess all-cause mortality, respectively. Moreover, compared with the impact on COVID-19 mortality, the relative risks of weekly NO 2 and PM 2.5 were lower, and the relative risks of weekly park and workplace visits were higher for excess all-cause mortality. These results suggest that the estimation based on excess all-cause mortality reduced the potential impact of air pollution and enhanced the influence of human activities compared with the estimation based on COVID-19 mortality.

Identifying the space-time patterns of areas with a higher risk of transmission and the associated built environment and demographic characteristics during the COVID-19 pandemic is critical for developing targeted intervention measures in response to the pandemic. This study aims to identify areas with a higher risk of COVID-19 transmission in different periods in Hong Kong and analyze the associated built environment and demographic factors using data of individual confirmed cases. We detect statistically significant space-time clusters of COVID-19 at the Large Street Block Group (LSBG) level in Hong Kong between January 23 and April 14, 2020. Two types of high-risk areas are identified (residences of and places visited by confirmed cases) and two types of cases (imported and local cases) are considered. The demographic and built environment features for the identified high-risk areas are further examined. The results indicate that high transport accessibility, dense and high-rise buildings, a higher density of commercial land and higher land-use mix are associated with a higher risk for places visited by confirmed cases. More green spaces, higher median household income, lower commercial land density are linked to a higher risk for the residences of confirmed cases. The results in this study not only can inform policymakers to improve resource allocation and intervention strategies but also can provide guidance to the public to avoid conducting high-risk activities and visiting high-risk places., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

The World Health Organization considered the wide spread of COVID-19 over the world as a pandemic. There is still a lack of understanding of its origin, transmission, and treatment methods. Understanding the influencing factors of COVID-19 can help mitigate its spread, but little research on the spatial factors has been conducted. Therefore, this study explores the effects of urban geometry and socio-demographic factors on the COVID-19 cases in Hong Kong. For each patient, the places they visited during the incubation period before going to hospital were identified, and matched with corresponding attributes of urban geometry (i.e., building geometry, road network and greenspace) and socio-demographic factors (i.e., demographic, educational, economic, household and housing characteristics) based on the coordinates. The local cases were then compared with the imported cases using stepwise logistic regression, logistic regression with case-control of time, and least absolute shrinkage and selection operator regression to identify factors influencing local disease transmission. Results show that the building geometry, road network and certain socio-economic characteristics are significantly associated with COVID-19 cases. In addition, the results indicate that urban geometry is playing a more important role than socio-demographic characteristics in affecting COVID-19 incidence. These findings provide a useful reference to the government and the general public as to the spatial vulnerability of COVID-19 transmission and to take appropriate preventive measures in high-risk areas., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

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

COVID-19 can be considered as the largest public health incident since 2019, posing disturbances to the medical, economic, and social systems. Understanding different levels of city resilience to the impact of COVID-19 on sustainable urban development is therefore essential. In this paper, we analyzed the evolution of the pandemic, the recovery of urban activities and major drivers of urban resilience to COVID-19 based on urban activity patterns derived from NO 2 monitoring stations from 217 cities in China. It is observed that nearly all urban activities have been affected by the epidemic with a reduced NO 2 emission, indicating a significant decline in the intensity of urban activity. The recovery patterns of human activity among different cities show that: (1) northern cities where low-resilience cities agglomerated have been severely affected. As of April 31, 2020, 12 northern cities have not recovered to pre-epidemic levels; (2) about three-quarters of the cities went through multiple stages to return to the pre-pandemic level, which related to both pandemic stress and prevention measures; (3) about 46.04% of the cities experienced increasing activity patterns that exceeded the pre-epidemic activity level. It is also observed that urban resilience can behave quite differently driven by variations in green coverage, economic aggregate, employment security, and medical system. Our study highlights the potential of an elevated urban resilience by optimizing the urban layout, improving the quantity and quality of green space, and enhancing the medical system. [Display omitted] • Most of the southern cities of China show higher urban activity than pre-pandemic. • Low/high-resilient urban agglomerations located in the north-northeast/south China. • Most cities undergo two stages of recovery at the beginning and end of March 2020. • Urban greenery is positively correlated to urban resilience. • Hospital capacity, instead of number, is more crucial to urban resilience. [ABSTRACT FROM AUTHOR]

This study analyzes a large-scale navigation dataset that captures travel activities of domestic inbound visitors in Jeju, Korea in the first nine months of 2020. A collection of regression models are introduced to quantify the dynamic effects of local and national COVID-19 indicators on their travel behavior. Results suggest that behavior of inbound travelers was jointly affected by pandemic severity locally and remotely. The daily number of new cases in Jeju has a greater impact on reducing travel activities than the national-level daily new cases of COVID-19. The impacts of the pandemic did not diminish over time but produced heterogeneous effects on travels with different trip purposes. Our findings reveal the persistence of COVID-19's effects on travel behavior and the variability in travelers' responses across tourism activities with different levels of perceived health risks. The implications for crisis management and recovery strategies are also discussed. [ABSTRACT FROM AUTHOR]

The novel coronavirus disease 2019 (COVID-19) has caused significantly changes in worldwide environmental and socioeconomics, especially in the early stage. Previous research has found that air pollution is potentially affected by these unprecedented changes and it affects COVID-19 infections. This study aims to explore the non-linear association between yearly and daily global air pollution and the confirmed cases of COVID-19. The concentrations of tropospheric air pollution (CO, NO2, O3, and SO2) and the daily confirmed cases between 23 January 2020 and 31 May 2020 were collected at the global scale. The yearly discrepancies of air pollutions and daily air pollution are associated with total and daily confirmed cases, respectively, based on the generalized additive model. We observed that there are significant spatially and temporally non-stationary variations between air pollution and confirmed cases of COVID-19. For the yearly assessment, the number of confirmed cases is associated with the positive fluctuation of CO, O3, and SO2 discrepancies, while the increasing NO2 discrepancies leads to the significant peak of confirmed cases variation. For the daily assessment, among the selected countries, positive linear or non-linear relationships are found between CO and SO2 concentrations and the daily confirmed cases, whereas NO2 concentrations are negatively correlated with the daily confirmed cases; variations in the ascending/declining associations are identified from the relationship of the O3-confirmed cases. The findings indicate that the non-linear relationships between global air pollution and the confirmed cases of COVID-19 are varied, which implicates the needs as well as the incorporation of our findings in the risk monitoring of public health on local, regional, and global scales. [ABSTRACT FROM AUTHOR]

The novel coronavirus disease (COVID-19) has become a public health problem at a global scale because of its high infection and mortality rate. It has affected most countries in the world, and the number of confirmed cases and death toll is still growing rapidly. Susceptibility studies have been conducted in specific countries, where COVID-19 infection and mortality rates were highly related to demographics and air pollution, especially PM2.5, but there are few studies on a global scale. This paper is an exploratory study of the relationship between confirmed COVID-19 cases and death toll per million population, population density, and PM2.5 concentration on a worldwide basis. A multivariate linear regression based on Moran eigenvector spatial filtering model and Geographically weighted regression model were undertaken to analyze the relationship between population density, PM2.5 concentration, and COVID-19 infection and mortality rate, and a geostatistical method with bivariate local spatial association analysis was adopted to explore their spatial correlations. The results show that there is a statistically significant positive relationship between COVID-19 confirmed cases and death toll per million population, population density, and PM2.5 concentration, but the relationship displays obvious spatial heterogeneity. While some adjacent countries are likely to have similar characteristics, it suggests that the countries with close contacts/sharing borders and similar spatial pattern of population density and PM2.5 concentration tend to have similar patterns of COVID-19 risk. The analysis provides an interpretation of the statistical and spatial association of COVID-19 with population density and PM2.5 concentration, which has implications for the control and abatement of COVID-19 in terms of both infection and mortality. [ABSTRACT FROM AUTHOR]

Understanding the relationship between the built environment and the risk of COVID-19 transmission is essential to respond to the pandemic. This study explores the relationship between the built environment and COVID-19 risk using the confirmed cases data collected in Hong Kong. Using the information on the residential buildings and places visited for each case from the dataset, we assess the risk of COVID-19 and explore their geographic patterns at the level of Tertiary Planning Unit (TPU) based on incidence rate (R1) and venue density (R2). We then investigate the associations between several built-environment variables (e.g., nodal accessibility and green space density) and COVID-19 risk using global Poisson regression (GPR) and geographically weighted Poisson regression (GWPR) models. The results indicate that COVID-19 risk tends to be concentrated in particular areas of Hong Kong. Using the incidence rate as an indicator to assess COVID-19 risk may underestimate the risk of COVID-19 transmission in some suburban areas. The GPR and GWPR models suggest a close and spatially heterogeneous relationship between the selected built-environment variables and the risk of COVID-19 transmission. The study provides useful insights that support policymakers in responding to the COVID-19 pandemic and future epidemics. [ABSTRACT FROM AUTHOR]

This paper aims to analyze how the associations of built environment and usage of urban space changed due to the social restrictions during COVID-19 pandemic in the United States. A total of 57,185 census block groups (CBGs) in 459 cities were analyzed. Built environment elements including road network, green space, and land use associated with usage of urban space (measured by over two billion mobile phone data) before/during COVID-19 were evaluated at both CBG and city level. Our results showed drastic decreases of urban space usage, ranging from 0.25 % to 76.35 %, among cities during COVID-19 social restriction. Specifically, CBG-level road length-node ratio, road intersection index and land use densities contributed less to usage of urban space during COVID-19. Meanwhile, the heterogeneity of city-level road network, commercial, public places, as well as hospitals and health cares exhibited stronger relationship with usage of urban space. The heterogeneity of green space at both CBG and city level showed less associations with usage of urban space during COVID-19. The discrepancies of the built environment effects on usage of urban space between CBG and city level suggest the importance of varying-scale analysis. Future interventions should consider space usages and health inequality from a space-varying perspective for post-pandemic period. • The changing associations of built environment with usage of urban space during COVID-19 were investigated • The changing associations were evaluated based on the census-block-group (CBG) level and city level in 459 U.S. cities • CBG-level road complexity and urban facility density contributed less on the usage of urban space during social restriction • City-level built environment exhibited stronger relationship with usage of urban space during social restriction [ABSTRACT FROM AUTHOR]

Restrictions on human and industrial activities due to the coronavirus (COVID-19) pandemic have resulted in an unprecedented reduction in energy consumption and air pollution around the world. Quantifying these changes in environmental conditions due to government-enforced containment measures provides a unique opportunity to understand the patterns, origins and impacts of air pollutants. During the lockdown in Pakistan, a significant reduction in energy demands and a decline of ∼1786 GWh (gigawatt hours) in electricity generation is reported. We used satellite observational data for nitrogen dioxide (NO 2), carbon monoxide (CO), sulphur dioxide (SO 2), aerosol optical depth (AOD) and land surface temperature (LST) to explore the associated environmental impacts of shifts in energy demands and emissions across Pakistan. During the strict lockdown period (March 23 to April 15, 2020), we observed a reduction in NO 2 emissions by 40% from coal-based power plants followed by 30% in major urban areas compared to the same period in 2019. Also, around 25% decrease in AOD (at 550 nm) thickness in industrial and energy sectors was observed although no major decrease was evident in urban areas. Most of the industrial regions resumed emissions during the 3rd quarter of April 2020 while the urban regions maintained reduced emissions for a longer period. Nonetheless, a gradual increase has been observed since April 16 due to relaxations in lockdown implementations. Restrictions on transportation in the cities resulted in an evident drop in the surface urban heat island (SUHI) effect, particularly in megacities. The changes reported as well as the analytical framework provides a baseline benchmark to assess the sectoral pollution contributions to air quality, especially in the scarcity of ground-based monitoring systems across the country. Image 1 [ABSTRACT FROM AUTHOR]