Associations between COVID-19 risk, multiple environmental exposures, and housing conditions: A study using individual-level GPS-based real-time sensing data.

Few studies have used individual-level data to explore the association between COVID-19 risk with multiple environmental exposures and housing conditions. Using individual-level data collected with GPS-tracking smartphones, mobile air-pollutant and noise sensors, an activity-travel diary, and a questionnaire from two typical neighborhoods in a dense and well-developed city (i.e., Hong Kong), this study seeks to examine 1) the associations between multiple environmental exposures (i.e., different types of greenspace, PM2.5, and noise) and housing conditions (i.e., housing types, ownership, and overcrowding) with individuals' COVID-19 risk both in residential neighborhoods and along daily mobility trajectories; 2) which social groups are disadvantaged in COVID-19 risk through the perspective of the neighborhood effect averaging problem (NEAP). Using separate multiple linear regression and logistical regression models, we found a significant negative association between COVID-19 risk with greenspace (i.e., NDVI) both in residential areas and along people's daily mobility trajectories. Meanwhile, we also found that high open space and recreational land exposure and poor housing conditions were positively associated with COVID-19 risk in high-risk neighborhoods, and noise exposure was positively associated with COVID-19 risk in low-risk neighborhoods. Further, people with work places in high-risk areas and poor housing conditions were disadvantaged in COVID-19 risk.

Examining the Influence of Housing Conditions and Daily Greenspace Exposure on People's Perceived COVID-19 Risk and Distress.

Many people have worried about COVID-19 infection, job loss, income reduction, and family conflict during the COVID-19 pandemic. Some social groups may be particularly vulnerable due to their residential neighborhoods and daily activities. On the other hand, people's daily exposure to greenspace offers promising pathways for reducing these worries associated with COVID-19. Using data collected with a questionnaire and a two-day activity diary from two typical neighborhoods in Hong Kong, this study examines how people's housing conditions and daily greenspace exposure affect their perceived COVID-19 risk and distress (i.e., worries about job loss, income reduction, and family conflict) during the pandemic. First, the study compares people's perceived COVID-19 risk and distress based on their residential neighborhoods. Further, it examines the associations between people's perceived COVID-19 risk and distress with their housing conditions and daily greenspace exposure using ordinal logistic regression models. The results indicate that living in a high-risk neighborhood, being married, renting a residential unit, and living in a large household are significantly associated with a higher neighborhood-based perceived COVID-19 risk and distress during the pandemic. In addition, people also reported lower mobility-based perceived COVID-19 risk when compared to their neighborhood-based perceived COVID-19 risk, while they still have a high perceived COVID-19 risk in their occupational venues if they have to work in a high-risk district (e.g., Kowloon). Lastly, daily greenspace exposure (i.e., woodland) could reduce people's perceived COVID-19 risk and distress. These results have important implications for the public health authority when formulating the measures during the COVID-19 pandemic.

Comparison of Manual and Automated Nucleic Acid Extraction Methods in Virus Transport Medium.

BACKGROUND: The implementation of an automated nucleic acid extraction system has many advantages over the manual methods. The purpose of this study was to evaluate the validity of two different methods for nucleic acid extraction in virus transport medium. METHODS: We collected 20 nasopharyngeal swabs in viral transport medium from the emergency department of the Asia University Hospital for the detection of SARS-CoV-2. The performance of the MaelstromTM 8 (Taiwan Advanced Nanotech) and the QIAamp Viral RNA Mini Kit (Qiagen) were compared for the extraction of nucleic acid from viral transport medium. The extracts were used for the validation of the RNA extraction procedures. The RNase P target was amplified in a one-step reverse transcription-quantitative PCR (RT-qPCR) reaction, as internal control for the extraction method. RESULTS: In this study, the agreement between the two methods was good and Pearson's correlation coefficient (r) was 0.919 (p < 0.001). The mean cycle threshold value of the two methods was 29.1. CONCLUSIONS: Overall, the performance values of the MaelstromTM 8 and the QIAamp Viral RNA Mini Kit were comparable to each other. In summary, the MaelstromTM 8 provides a standardized procedure, avoidance of sample-to-sample cross contaminations, is easy to use, improves turnaround time and requires less hands-on time as compared to the manual extraction method. The MaelstromTM 8 is more suitable for clinical laboratories that carry small or medium-sized samples for nucleic acid extraction.

Rapid Cobas Liat SARS-CoV-2 Assay in Comparison with the Laboratory-Developed Real-Time RT-PCR Test.

BACKGROUND: The objective of this study was to compare the validity of two different assays for the detection of SARS-CoV-2. METHODS: We collected 50 nasopharyngeal swabs in universal transport medium from the emergency department of Asia University Hospital for the detection of SARS-CoV-2 using reverse transcription-polymerase chain reaction (RT-PCR). The samples for the Liat SARS-CoV-2 influenza A/B test were stored at -70℃ after SARS-CoV-2 testing using the RT-PCR in order to assess method comparison. RESULTS: In this study, the Limit of detection (LOD) of the cobas Liat SARS-CoV-2 and influenza A/B nucleic acid test is 12 copies/µL and the assay obtained 100% positive agreement and negative percent agreement with RT-PCR. CONCLUSIONS: In summary, a prefect agreement exists between the detection of SARS-CoV-2 conducted with the cobas Liat SARS-CoV-2 and influenza A/B nucleic acid test and the RT-PCR. The cobas Liat SARS-CoV-2 and influenza A/B nucleic acid test is a reliable method for the detection of SARS-CoV-2, and it only requires 20 minutes to obtain the results. On the other hand, the cobas Liat SARS-CoV-2 and influenza A/B nucleic acid test is accurate, easy to use, and provides a faster turnaround time than testing performed in the high-throughput platform.

The superspreading places of COVID-19 and the associated built-environment and socio-demographic features: A study using a spatial network framework and individual-level activity data.

Previous studies observed that most COVID-19 infections were transmitted by a few individuals at a few high-risk places (e.g., bars or social gathering venues). These individuals, often called superspreaders, transmit the virus to an unexpectedly large number of people. Further, a small number of superspreading places (SSPs) where this occurred account for a large number of COVID-19 transmissions. In this study, we propose a spatial network framework for identifying the SSPs that disproportionately spread COVID-19. Using individual-level activity data of the confirmed cases in Hong Kong, we first identify the high-risk places in the first four COVID-19 waves using the space-time kernel density method (STKDE). Then, we identify the SSPs among these high-risk places by constructing spatial networks that integrate the flow intensity of the confirmed cases. We also examine what built-environment and socio-demographic features would make a high-risk place to more likely become an SSP in different waves of COVID-19 by using regression models. The results indicate that some places had very high transmission risk and suffered from repeated COVID-19 outbreaks over the four waves, and some of these high-risk places were SSPs where most (about 80%) of the COVID-19 transmission occurred due to their intense spatial interactions with other places. Further, we find that high-risk places with dense urban renewal buildings and high median monthly household rent-to-income ratio have higher odds of being SSPs. The results also imply that the associations between built-environment and socio-demographic features with the high-risk places and SSPs are dynamic over time. The implications for better policymaking during the COVID-19 pandemic are discussed.

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.

Comparing the space-time patterns of high-risk areas in different waves of COVID-19 in Hong Kong.

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.

Identifying the space-time patterns of COVID-19 risk and their associations with different built environment features in Hong Kong.

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.

Spatial analysis of the impact of urban geometry and socio-demographic characteristics on COVID-19, a study in Hong Kong.

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.

Investigating the Relationship between the Built Environment and Relative Risk of COVID-19 in Hong Kong

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.