A country comparison of place-based activity response to COVID-19 policies

Spatial risk for a superspreading environment: Insights from six urban facilities in six global cities across four continents

Introduction

This study sets out to provide scientific evidence on the spatial risk for the formation of a superspreading environment.

Methods

Focusing on six common types of urban facilities (bars, cinemas, gyms and fitness centers, places of worship, public libraries and shopping malls), it first tests whether visitors' mobility characteristics differ systematically for different types of facility and at different locations. The study collects detailed human mobility and other locational data in Chicago, Hong Kong, London, São Paulo, Seoul and Zurich. Then, considering facility agglomeration, visitors' profile and the density of the population, facilities are classified into four potential spatial risk (PSR) classes. Finally, a kernel density function is employed to derive the risk surface in each city based on the spatial risk class and nature of activities.

Results

Results of the human mobility analysis reflect the geographical and cultural context of various facilities, transport characteristics and people's lifestyle across cities. Consistent across the six global cities, geographical agglomeration is a risk factor for bars. For other urban facilities, the lack of agglomeration is a risk factor. Based on the spatial risk maps, some high-risk areas of superspreading are identified and discussed in each city.

Discussion

Integrating activity-travel patterns in risk models can help identify areas that attract highly mobile visitors and are conducive to superspreading. Based on the findings, this study proposes a place-based strategy of non-pharmaceutical interventions that balance the control of the pandemic and the daily life of the urban population.

Efficacy of COVID-19 Public Health Measures in Philadelphia, New York City, Baltimore, and Chicago

Introduction

From the early days of the pandemic, US cities have implemented a variety of public health measures to reduce the spread of COVID-19. This study investigates which policies were most effective in reducing cases of COVID-19 in four major cities: Philadelphia, New York City, Baltimore, and Chicago.

Methods

Through a cross-comparative analysis, we developed a timeline that tracked the implementation of a range of public health measures along with changes in COVID-19 cases, hospitalizations, and deaths. Data were collected from publicly available government sites and from press releases.

Results

The results from the stay-at-home orders illustrate the delayed impact it has in reducing COVID-19 cases and hospitalizations. The mask mandate led to the immediate and sustained reduction in cases across all four cities. During the spike of COVID-19 in the Fall of 2020, restrictions on indoor dining contributed significantly to reducing COVID-19 cases.

Discussion

Of all the measures that were examined, the implementation of mask mandates was most closely associated with a decline in COVID-19 cases, hospitalizations, and deaths across all four cities. Restrictions on indoor dining were also associated with a reduction in COVID-19 cases. Future studies should further investigate the adherence to different policies to better understand their impacts.

Security motives and negative affective experiences during the early months of the COVID-19 pandemic

OBJECTIVE

Self-regulation can help individuals cope during stressful events, but little is known about why and when this might occur. We examined if being more focused on prevention was linked to negative affective experiences during the COVID-19 pandemic. We also examined possible underlying mechanisms for this association, and whether social support buffered it.

DESIGN

Pre-registered longitudinal study, with surveys every 2 weeks over one month (N = 1269).

MAIN OUTCOME MEASURES

Regulatory focus and worry for health (T1), adherence to self-isolation and preventive health behaviours (T2), negative affective experiences, positive affect, frequency of online interactions, and perceived social support (T3).

RESULTS

Prevention focus was associated with health worries at baseline and linked to greater adherence to preventive health behaviours (T2). Only adherence to self-isolation was linked to more negative affective experiences (T3). Exploratory analyses showed that prevention focus was linked to more negative affective experiences (T3), but only for participants with fewer online interactions with their family and less perceived social support from family and friends.

CONCLUSIONS

Prevention motives in threatening times can be a double-edged sword, with benefits for health behaviours and consequences for negative affective experiences. Having a strong social network during these times can alleviate these consequences.

Understanding the impact of COVID‐19 pandemic on teleworkers' experiences of perceived threat and professional isolation: The moderating role of friendship

Drawing from conservation of resource theory and the social support resource theory, this study examines how the severity of an exogenous disruptive event - the COVID-19 pandemic - in one's community influences teleworkers' well-being outcomes indirectly through their perceptions of pandemic-related threat and experience of professional isolation, as well as the buffering effect of friendship on these relationships. Utilizing time-lagged data from participants of a two-wave survey panel (N = 351) and objective data of COVID-19 severity from counties around the United States, we found that perceived threat, but not professional isolation, mediated the negative effect of proportion of confirmed COVID-19 cases in the community on teleworkers' well-being outcomes. Further, consistent with our predictions, support from friends significantly weakened the negative effects of threat and professional isolation on well-being. Key theoretical and practical implications of this study are discussed.

Optimized pleat geometry at specific pleat height in pleated filters for air purification

The performance of an air purifier is determined by its clean air delivery rate (CADR), expressed as the product of the air flowrate and filter collection efficiency. A high CADR in a commercial air purifier is achieved using a pleated air filter with high filtration performance at a given air flowrate. However, obtaining this high filtration performance depends on the knowledge of the manufacturer, including the optimal pleating design, which remains uncertain to date. In this study, test pleated filters were prepared by folding a flat electrostatically-charged E12 filter medium to obtain different pleating geometries. The proper pleat geometry of the pleated filter, which has a minimum pressure drop at a fixed air flowrate, was dependent on the pleat length (P_L ) and pleating ratio (α = pleat height/pleat width), wherein as the P_L increases, the α range required to achieve a minimum pressure drop similarly increases. Based on the filter quality factor (q_F ), the optimal pleat geometry of a pleated filter was between α values of 6-8, irrespective of the P_L . Although both P_L and α have certain influences on the q_F , α has a greater influence on the pleated filter with no dead zone.

A Descriptive Comparison of Mass Testing During the COVID-19 Pandemic in Montreal, Paris, Bamako, and Recife

Objective: The aim of this descriptive article was to compare mass testing for SARS-CoV-2 during the first wave of the COVID-19 pandemic in Montreal, Canada; Bamako, Mali; Paris, France; and Recife, Brazil.

Methods: Data was collected through interviews with key informants involved in the testing response and a review of the grey literature. The TIDieR-PHP checklist was then used to provide the basis of the intervention descriptions and to compare the data between cities.

Results: Descriptive comparisons revealed that the type of test, the testing process, and materials used were similar between the cities during the first wave of the pandemic. In addition, all cities experienced similar material and personnel resource shortages, directly affecting testing accessibility and capacity. The main differences were related to testing capacity and implementation timelines, which were dependent on the state of the health care systems, governance, and access to resources.

Conclusion: Results of this study highlight the similarities and differences in testing between the cities and demonstrate the importance of comprehensive intervention descriptions to highlight lessons learned, increase knowledge sharing, and inform policy decisions.

Care in the time of COVID: An interpretative phenomenological analysis of the impact of COVID-19 control measures on post-partum mothers’ experiences of pregnancy, birth and the health system

Background

Findings suggest pandemic control measures have modified maternal health practices, compromising the quality of care provided to new and expectant mothers and interfering with their birthing experiences. For this reason, this study explored the lived experiences of post-partum Victorian mothers during the pandemic as well as the potential influence of control measures over their perceptions regarding the health system.

Methods

This study used a qualitative approach. Recruitment was conducted between May and June 2021, using both the Australian Breastfeeding Association’s social media pages and snowball recruitment. Interviews were semi-structured using open-ended questions relating to key themes. Seven Victorian post-partum mothers were identified and their transcripts analysed using Interpretative Phenomenological Analysis.

Results

Mothers described how unexpected changes to maternal care exacerbated feelings of uncertainty regarding pregnancy and birth. Mothers also differentiated between impacts by the health system and the role healthcare professionals played in moderating these effects. Whilst visitor restrictions provided some benefit, restrictions to familial and social support left many of the mothers feeling alone during their pregnancy and interfered with their immediate post-partum experience.

Conclusion

This study illustrates the importance of evidence-based practice in maternal care and provides insights for both health professionals and policy analysts in developing new or modifying existing guidelines that better balance the needs of expectant and post-partum mothers with pandemic control measures.

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.

Data analytics during pandemics: a transportation and location planning perspective

The recent COVID-19 pandemic once again showed the value of harnessing reliable and timely data in fighting the disease. Obtained from multiple sources via different collection streams, an immense amount of data is processed to understand and predict the future state of the disease. Apart from predicting the spatio-temporal dynamics, it is used to foresee the changes in human mobility patterns and travel behaviors and understand the mobility and spread speed relationship. During this period, data-driven analytic approaches and Operations Research tools are widely used by scholars to prescribe emerging transportation and location planning problems to guide policy-makers in making effective decisions. In this study, we provide a review of studies which tackle transportation and location problems during the COVID-19 pandemic with a focus on data analytics. We discuss the major data collecting streams utilized during the pandemic era, highlight the importance of rapid and reliable data sharing, and give an overview of the challenges and limitations on the use of data.

Association between the Human Development Index and Confirmed COVID-19 Cases by Country

It is important to understand the ultimate control of COVID-19 in all countries around the world in relation to the characteristics of developed countries, LDCs, and the variety of transmission characteristics of COVID-19. Therefore, this study aimed to identify factors associated with confirmed cases of COVID-19 with a focus on the Human Development Index (HDI). The units of analysis used for the current study were countries, and dataset were aggregated from multiple sources. This study used COVID-19 data from Our World in Data, the Global Health Security Index, and the WORLD BANK. A total of 171 countries were included in the analysis. A multi-variable linear regression with a hierarchical framework was employed to investigate whether the HDI is associated with confirmed COVID-19 cases after controlling for the demographic and healthcare system characteristics of the study countries. For Model 2, which controlled for demographic and healthcare system characteristics, HDI (β = 0.46, p < 0.001, 95% CI = 2.64−10.87) and the number of physicians per 1000 people (β = 0.34, p < 0.01, 95% CI = 0.21−0.75) had significant associations with the total number of confirmed COVID-19 cases per million people. Countries with a high HDI level are able to conduct higher per capita testing, resulting in higher numbers of confirmed cases than in countries with lower HDI levels. This study has shown evidence that could be used by governments and international organizations to identify national characteristics and provide the international cooperation necessary to develop effective prevention and intervention methods to deal with the global pandemic.

The Importance of Behavioral and Native Factors on COVID-19 Infection and Severity: Insights from a Preliminary Cross-Sectional Study

The COVID-19 pandemic has had a major impact on a global scale. Understanding the innate and lifestyle-related factors influencing the rate and severity of COVID-19 is important for making evidence-based recommendations. This cross-sectional study aims at establishing a potential relationship between human characteristics and vulnerability/resistance to SARS-CoV-2. We hypothesize that the impact of the virus is not the same due to cultural and ethnic differences. A cross-sectional study was performed using an online questionnaire. The methodology included the development of a multi-language survey, expert evaluation, and data analysis. Data were collected using a 13-item pre-tested questionnaire based on a literature review between 9 December 2020 and 21 July 2021. Data were statistically analyzed using logistic regression. For a total of 1125 respondents, 332 (29.5%) were COVID-19 positive; among them, 130 (11.5%) required home-based treatment, and 14 (1.2%) intensive care. The significant and most influential factors on infection included age, physical activity, and health status (p < 0.05), i.e., better physical activity and better health status significantly reduced the possibility of infection, while older age significantly increased it. The severity of infection was negatively associated with the acceptance (adherence and respect) of preventive measures and positively associated with tobacco (p < 0.05), i.e., smoking regularly significantly increases the severity of COVID-19 infection. This suggests the importance of behavioral factors compared to innate ones. Apparently, individual behavior is mainly responsible for the spread of the virus. Therefore, adopting a healthy lifestyle and scrupulously observing preventive measures, including vaccination, would greatly limit the probability of infection and prevent the development of severe COVID-19.

A GIS-Based Spatiotemporal Modelling of Urban Traffic Accidents in Tabriz City during the COVID-19 Pandemic

The main aim of the present study was to investigate the spatiotemporal trends of urban traffic accident hotspots during the COVID-19 pandemic. The severity index was used to determine high-risk areas, and the kernel density estimation method was used to identify risk of traffic accident hotspots. Accident data for the time period of April 2018 to November 2020 were obtained from the traffic police of Tabriz (Iran) and analyzed using GIS spatial and network analysis procedures. To evaluate the impacts of COVID-19, we used the seasonal variation in car accidents to analyze the change in the total number or urban traffic accidents. Eventually, the sustainability of urban transport was analyzed based on the demographic and land use data to identify the areas with a high number of accidents and its respective impacts for the local residences. Based on the results, the lockdown measures in response to the pandemic have led to significant reductions in road traffic accidents. From the perspective of urban planning, the spatiotemporal urban traffic accident analysis indicated that areas with high numbers of elderly people and children were most affected by car accidents. As we identified the hotspots of urban traffic accidents and evaluated their spatiotemporal correlation with land use and demography characteristics, we conclude that the results of this study can be used by urban managers and support decision making to improve the situation, so that fewer accidents will happen in the future.

Risk assessment for precise intervention of COVID-19 epidemic based on available big data and spatio-temporal simulation method: Empirical evidence from different public places in Guangzhou, China

Risk assessment of the intra-city spatio-temporal spreading of COVID-19 is important for providing location-based precise intervention measures, especially when the epidemic occurred in the densely populated and high mobile public places. The individual-based simulation has been proven to be an effective method for the risk assessment. However, the acquisition of individual-level mobility data is limited. This study used publicly available datasets to approximate dynamic intra-city travel flows by a spatio-temporal gravity model. On this basis, an individual-based epidemic model integrating agent-based model with the susceptible-exposed-infectious-removed (SEIR) model was proposed and the intra-city spatio-temporal spreading process of COVID-19 in eleven public places in Guangzhou China were explored. The results indicated that the accuracy of dynamic intra-city travel flows estimated by available big data and gravity model is acceptable. The spatio-temporal simulation method well presented the process of COVID-19 epidemic. Four kinds of spatial-temporal transmission patterns were identified and the pattern was highly dependent on the urban spatial structure and location. It indicated that location-based precise intervention measures should be implemented according to different regions. The approach of this research can be used by policy-makers to make rapid and accurate risk assessments and to implement intervention measures ahead of epidemic outbreaks.

Trauma-Related Distress During the COVID-19 Pandemic In 59 Countries

The COVID-19 pandemic has upended life like few other events in modern history, with differential impacts on varying population groups. This study examined trauma-related distress among 6,882 adults ages 18 to 94 years old in 59 countries during April to May 2020. More than two-thirds of participants reported clinically significant trauma-related distress. Increased distress was associated with unemployment; identifying as transgender, nonbinary, or a cisgender woman; being from a higher income country; current symptoms and positive diagnosis of COVID-19; death of a loved one; restrictive government-imposed isolation; financial difficulties; and food insecurity. Other factors associated with distress included working with potentially infected individuals, care needs at home, a difficult transition to working from home, conflict in the home, separation from loved ones, and event restrictions. Latin American and Caribbean participants reported more trauma-related distress than participants from Europe and Central Asia. Findings inform treatment efforts and highlight the need to address trauma-related distress to avoid long-term mental health consequences.

Rapid Evidence Review of Community Engagement and Resources in the UK during the COVID-19 Pandemic: How Can Community Assets Redress Health Inequities?

Community engagement, such as participating in arts, nature or leisurely activities, is positively associated with psychological and physiological wellbeing. Community-based engagement during the COVID-19 pandemic facilitated informal and local mutual aid between individuals. This rapid evidence review assesses the emergence of community-based arts, nature, music, theatre and other types of cultural engagement amongst UK communities in response to the COVID-19 pandemic. Here, we focus on all community engagement with a sub-focus on provisions accessed by and targeted towards vulnerable groups. Two hundred and fifty-six resources were included that had been created between February 2020 and January 2021. Resources were identified through Google Scholar, PubMed, Web of Science, MedRXic, PsycharXiv and searches for grey literature and items in the public domain. The majority reported services that had been adapted to become online, telephone-based or delivered at a distance from doorsteps. Several quality assessment frameworks were used to evaluate the quality of data. Whilst a number of peer-reviewed, grey literature and public domain articles were identified, less than half of the identified literature met quality thresholds. The pace of the response to the pandemic may have meant that robust evaluation procedures were not always in place.

Restrictive and stimulative impacts of COVID-19 policies on activity trends: A case study of Kyoto

This paper employs regression with ARIMA errors (RegARIMA) to quantify the impacts of multiple non-pharmaceutical interventions, daily new cases, seasonal and calendar effects, and other factors on activity trends across the timeline of the ongoing COVID-19 pandemic in Japan. The discussion focuses on two controversial policy sets imposed by the Japanese government that aim to contain the pandemic and to stimulate the recovery of the economy. The containing effect was achieved by stay-at-home requests and declaring a "State of Emergency" in the combat against the first waves of infectious cases. After observing reduced cases, Go-to-travel and Go-to-eat campaigns were launched in July 2020 to encourage recreational travel and to revive the economy. To better understand the impact of the policies we utilize "Google trends" which measure how much these policies are looked up online. We suggest this reflects how much they are part of the public discussion. A case study is conducted in Kyoto, a city famous for tourism. The proposed RegARIMA model is compared with linear regression and time series models. The outperformances in measuring the magnitude of intervention impacts and forecasting the future trends are confirmed by using a total of twelve activity and mobility indices as the dependent variable. Nine indices are released by Google and Apple and three are obtained from local Wi-Fi packet sensors. The effect of the State of Emergency declaration is found to erode at the second implementation, and the second stage of the Go-to-travel campaign successfully stimulated travel demand in the autumn sighting season of 2020.

One year of COVID-19 in Italy: are containment policies enough to shape the pandemic pattern?

A successful fight against COVID-19 greatly depends on citizens' adherence to the restrictive measures, which may not suffice alone. Making use of a containment index, data on sanctions, and Google's movement trends across Italian provinces, complemented by other sources, we investigate the extent to which compliance with the mobility limitations has affected the number of infections and deaths over time, for the period running from February 24, 2020 to February 23, 2021. We find proof of a deterrent effect on mobility given by the increase in sanction rate and positivity rate among the population. We also show how the pandemic dynamics have changed between the first and the second wave of the emergency. Lots of people could be spared by incorporating greater interventions and many more are at stake, despite the recent boost in vaccinations. Informing citizens about the effects and purposes of the restrictive measures has become increasingly important throughout the various phases of the pandemic.

Spatiotemporal clustering patterns and sociodemographic determinants of COVID-19 (SARS-CoV-2) infections in Helsinki, Finland

This study aims to elucidate the variations in spatiotemporal patterns and sociodemographic determinants of SARS-CoV-2 infections in Helsinki, Finland. Global and local spatial autocorrelation were inspected with Moran's I and LISA statistics, and Getis-Ord Gi* statistics was used to identify the hot spot areas. Space-time statistics were used to detect clusters of high relative risk and regression models were implemented to explain sociodemographic determinants for the clusters. The findings revealed the presence of spatial autocorrelation and clustering of COVID-19 cases. High–high clusters and high relative risk areas emerged primarily in Helsinki's eastern neighborhoods, which are socioeconomically vulnerable, with a few exceptions revealing local outbreaks in other areas. The variation in COVID-19 rates was largely explained by median income and the number of foreign citizens in the population. Furthermore, the use of multiple spatiotemporal analysis methods are recommended to gain deeper insights into the complex spatiotemporal clustering patterns and sociodemographic determinants of the COVID-19 cases.

Assessment of the Risk of Depression in Residents Staying at Long-Term Care Institutions in Poland During the COVID-19 Pandemic Depending on the Quality of Cognitive Functioning

Background: The development of the COVID-19 pandemic has prompted the implementation of many procedures to safeguard against further increases in illness. Unfortunately, this has drastically reduced residents' contact with their families, which has increased feelings of loneliness and isolation. This is particularly difficult in long-term care facilities, where the risk of developing depression is higher than in the general population. Objectives: The aim of the study was to assess the risk of depression among the residents of long-term care institutions in Poland during the COVID-19 pandemic and to determine the relationship between the risk of depression and the occurrence of cognitive impairment in the study group. Methods: The study included 273 residents from long-term care institutions in Poland. The risk of depression was determined based on an originally designed questionnaire. The cognitive state of the subjects was assessed using the screening test Mini-Mental State Examination (MMSE). Both the depression risk assessment and the MMSE test were conducted twice: in March and December 2020. Results: In March, severe dementia was present in 28.2% of the residents and normal MMSE scores were observed in 16.1% of the subjects; in December, the prevalence of severe dementia increased to 31.1% and that of normal scores decreased to 10.3%. In March, no participant was found to be at high risk of depression and moderate risk was observed in 14.3% of the subjects; in December, 2.6% of the residents had a high risk score and 45.4% had a moderate risk score. Statistical analysis revealed that higher MMSE scores correspond with a higher risk of depression. Conclusion: A higher risk of depression was observed with the development of the pandemic. Residents with cognitive impairment were characterised by a lower risk of depression compared to individuals with normal MMSE scores. During the study, progression of cognitive impairment was observed in the residents.

Dynamic activity chain pattern estimation under mobility demand changes during COVID-19

During the coronavirus disease 2019 pandemic, the activity engagement and travel behavior of city residents have been impacted by government restrictions, such as temporary city-wide lockdowns, the closure of public areas and public transport suspension. Based on multiple heterogeneous data sources, which include aggregated mobility change reports and household survey data, this paper proposes a machine learning approach for dynamic activity chain pattern estimation with improved interpretability for examining behavioral pattern adjustments. Based on historical household survey samples, we first establish a computational graph-based discrete choice model to estimate the baseline travel tour parameters before the pandemic. To further capture structural deviations of activity chain patterns from day-by-day time series, we define the activity-oriented deviation parameters within an interpretable utility-based nested logit model framework, which are further estimated through a constrained optimization problem. By incorporating the long short-term memory method as the explainable module to capture the complex periodic and trend information before and after interventions, we predict day-to-day activity chain patterns with more accuracy. The performance of our model is examined based on publicly available datasets such as the 2017 National Household Travel Survey in the United States and the Google Global Mobility Dataset throughout the epidemic period. Our model could shed more light on transportation planning, policy adaptation and management decisions during the pandemic and post-pandemic phases.

Influencing Factors of Environmental Risk Perception during the COVID-19 Epidemic in China

The spread of COVID-19 is having a serious impact on socioeconomic development, and increased environmental risk perception (ERP). ERP provide new ideas for the orderly recovery of society. However, there have been studies that often pay attention to individual factors, and less concerned about the external environment. In fact, ERP will be affected by the external environment and individual factors. We used a Python script to collect 65,277 valid Weibo comments during the COVID-19 epidemic in China to assess urban residents' environmental risk perception (ERP). SnowNLP emotion analysis was used to measure the ERP of 366 urban in China, and the structural proportion characteristics and spatial-temporal differentiation of ERP were analyzed. Then, an order logistic regression model was used to investigate the relationship between economic level, social security, medical facilities and ERP. The study investigated the Chinese cities have a higher ERP during the COVID-19 period, and it shows marked fluctuations. As COVID-19 spreads, the ERP shows a distribution pattern of "high in the southeast and low in the northwest" with Hu line as the boundary and "from high to low" with Wuhan as the high value center. COVID-19 serves as catalysts for ERP, the impact of COVID-19 is enhanced after socioeconomic factors are considered. The economic level effectively regulates ERP, except the stage of accelerating diffusion. ERP is effectively stabilized by social security and medical facilities. After considering all the variables simultaneously, we found that the mitigation effect of social security and medical facilities on ERP has improved.

Effects of transport-related COVID-19 policy measures: A case study of six developed countries

This study attempts to provide scientifically-sound evidence for designing more effective COVID-19 policies in the transport and public health sectors by comparing 418 policy measures (244 are transport measures) taken in different months of 2020 in Australia, Canada, Japan, New Zealand, the UK, and the US. The effectiveness of each policy is measured using nine indicators of infections and mobilities corresponding to three periods (i.e., one week, two weeks, and one month) before and after policy implementation. All policy measures are categorized based on the PASS approach (P: prepare-protect-provide; A: avoid-adjust; S: shift-share; S: substitute-stop). First, policy effectiveness is compared between policies, between countries, and over time. Second, a dynamic Bayesian multilevel generalized structural equation model is developed to represent dynamic cause-effect relationships between policymaking, its influencing factors and its consequences, within a unified research framework. Third, major policy measures in the six countries are compared. Finally, findings for policymakers are summarized and extensively discussed.

Impacts of COVID-19 and anti-pandemic policies on urban transport-an empirical study in China

The COVID-19 pandemic that began in the last quarter of 2019 seriously impacted the transportation industry. Countries around the world adopted various restrictions and policies to prevent the spread of the pandemic, which resulted in a sharp drop in the demand for transportation. China was the first country to detect the pandemic and the fastest to recover. Existing policies and impacts were reviewed to analyze the impact of the pandemic on China's urban transportation sector and propose measures that may be taken to reduce the impact of COVID-19. This study reviews the impact on urban transportation system operations and how government should respond to a viral pandemic. The recovery measures during and after the pandemic and their hierarchical response system are analyzed. Furthermore, to empirically explore the effect of the recovery measures, this study adopted the Event Study Methodology (ESM) to quantitatively analyze the impact of the epidemic as well as anti-pandemic policies on the traffic flow sequence in the resurgence of COVID-19 in Beijing. The research findings provided solid policy implications and experiences for constructing sustainable urban transportation system and improve flexibility, reliability, and resilience of traffic governance in post-pandemic era.

Observing the silent world under COVID-19 with a comprehensive impact analysis based on human mobility

Since spring 2020, the human world seems to be exceptionally silent due to mobility reduction caused by the COVID-19 pandemic. To better measure the real-time decline of human mobility and changes in socio-economic activities in a timely manner, we constructed a silent index (SI) based on Google's mobility data. We systematically investigated the relations between SI, new COVID-19 cases, government policy, and the level of economic development. Results showed a drastic impact of the COVID-19 pandemic on increasing SI. The impact of COVID-19 on human mobility varied significantly by country and place. Bi-directional dynamic relationships between SI and the new COVID-19 cases were detected, with a lagging period of one to two weeks. The travel restriction and social policies could immediately affect SI in one week; however, could not effectively sustain in the long run. SI may reflect the disturbing impact of disasters or catastrophic events on the activities related to the global or national economy. Underdeveloped countries are more affected by the COVID-19 pandemic.

Cardiac PET and SPECT During the COVID-19 Pandemic

The severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) spread rapidly around the world in the early months of 2020 before the COVID-19 outbreak was officially declared a pandemic in March 2020. Worldwide volumes of non-emergent testing, such as cardiac PET and SPECT, decreased dramatically at the beginning of the lockdown as health systems attempted to limit the spread of the COVID-19 virus. Published reports of increasing cardiovascular mortality compared to months prior to the pandemic raised concerns that lack of access to appropriate cardiovascular testing was adversely affecting patient outcomes. Medical societies published guidance for the best practices of cardiovascular nuclear medicine laboratories to address this emerging cardiovascular epidemic. These nuclear cardiology expert consensus recommendations were remarkably consistent with those from other health organizations and heavily emphasized patient triage, screening of symptoms, strict PPE usage, and limiting patient dwell time in the nuclear medicine lab by favoring shorter testing protocols. Survey responses indicated that nuclear medicine labs took heed of these recommendations and adjusted practices to meet the cardiovascular needs of their population while minimizing transmission risk.

COVID-19, community response, public policy, and travel patterns: A tale of Hong Kong

The COVID-19 outbreak has necessitated a critical review of urban transportation and its role in society against the backdrop of an exogenous shock. This article extends the transportation literature regarding community responses to the COVID-19 pandemic and what lessons can be obtained from the case of Hong Kong in 2020. Individual behavior and collective responsibility are considered crucial to ensure both personal and community wellbeing in a pandemic context. Trends in government policies, the number of infectious cases, and community mobility are examined using multiple data sources. The mobility changes that occurred during the state of emergency are revealed by a time-series analysis of variables that measure both the epidemiological severity level and government stringency. The results demonstrate a high response capability of the local government, inhabitants, and communities. Communities in Hong Kong are found to have reacted faster than the implementation of health interventions, whereas the government policies effectively reduced the number of infection cases. The ways in which community action are vital to empower flexible and adaptive community responses are also explored. The results indicate that voluntary community involvement constitutes a necessary condition to help inform and reshape future transport policy and response strategies to mitigate the pandemic.

Preventing COVID-19 spread in closed facilities by regular testing of employees-An efficient intervention in long-term care facilities and prisons?

BACKGROUND

Different levels of control measures were introduced to contain the global COVID-19 pandemic, many of which have been controversial, particularly the comprehensive use of diagnostic tests. Regular testing of high-risk individuals (pre-existing conditions, older than 60 years of age) has been suggested by public health authorities. The WHO suggested the use of routine screening of residents, employees, and visitors of long-term care facilities (LTCF) to protect the resident risk group. Similar suggestions have been made by the WHO for other closed facilities including incarceration facilities (e.g., prisons or jails), wherein parts of the U.S., accelerated release of approved inmates is taken as a measure to mitigate COVID-19.

METHODS AND FINDINGS

Here, the simulation model underlying the pandemic preparedness tool CovidSim 1.1 (http://covidsim.eu/) is extended to investigate the effect of regularly testing of employees to protect immobile resident risk groups in closed facilities. The reduction in the number of infections and deaths within the risk group is investigated. Our simulations are adjusted to reflect the situation of LTCFs in Germany, and incarceration facilities in the U.S. COVID-19 spreads in closed facilities due to contact with infected employees even under strict confinement of visitors in a pandemic scenario without targeted protective measures. Testing is only effective in conjunction with targeted contact reduction between the closed facility and the outside world-and will be most inefficient under strategies aiming for herd immunity. The frequency of testing, the quality of tests, and the waiting time for obtaining test results have noticeable effects. The exact reduction in the number of cases depends on disease prevalence in the population and the levels of contact reductions. Testing every 5 days with a good quality test and a processing time of 24 hours can lead up to a 40% reduction in the number of infections. However, the effects of testing vary substantially among types of closed facilities and can even be counterproductive in U.S. IFs.

CONCLUSIONS

The introduction of COVID-19 in closed facilities is unavoidable without a thorough screening of persons that can introduce the disease into the facility. Regular testing of employees in closed facilities can contribute to reducing the number of infections there, but is only meaningful as an accompanying measure, whose economic benefit needs to be assessed carefully.