Human mobility behavior in COVID-19: A systematic literature review and bibliometric analysis

Research trends of bio-application of major components in lignocellulosic biomass (cellulose, hemicellulose and lignin) in orthopedics fields based on the bibliometric analysis: A review

Cellulose, hemicellulose, and lignin are the major bio-components in lignocellulosic biomass (BC-LB), which possess excellent biomechanical properties and biocompatibility to satisfy the demands of orthopedic applications. To understand the basis and trends in the development of major bio-components in BC-LB in orthopedics, the bibliometric technology was applied to get unique insights based on the published papers (741) in the Web of Science (WOS) database from January 1st, 2001, to February 14th, 2023. The analysis includes the annual distributions of publications, keywords co-linearity, research hotspots exploration, author collaboration networks, published journals, and clustering of co-cited literature. The results reveal a steady growth in publications focusing on the application of BC-LB in orthopedics, with China and the United States leading in research output. The "International Journal of Biological Macromolecules" was identified as the most cited journal for BC-LB research in orthopedics. The research hotspots encompassed bone tissue engineering, cartilage tissue engineering, and drug delivery systems, indicating the fundamental research and potential development in these areas. This study also highlights the challenges associated with the clinical application of BC-LB in orthopedics and provides valuable insights for future advancements in the field.

Temporal dynamics of public transportation ridership in Seoul before, during, and after COVID-19 from urban resilience perspective

We delve into the temporal dynamics of public transportation (PT) ridership in Seoul, South Korea, navigating the periods before, during, and after the COVID-19 pandemic through a spatial difference-in-difference model (SDID). Rooted in urban resilience theory, the study employs micro-level public transportation card data spanning January 2019 to December 2023. Major findings indicate a substantial ridership decline during the severe COVID impact phase, followed by a period in the stable and post-COVID phases. Specifically, compared to the pre-COVID phase, PT ridership experienced a 32.1% decrease in Severe, followed by a reduced magnitude of 21.8% in Stable and 13.5% in post-COVID phase. Interestingly, the observed decrease implies a certain level of adaptability, preventing a complete collapse. Also, contrasting with findings in previous literature, our study reveals a less severe impact, with reductions ranging from 27.0 to 34.9%. Moreover, while the ridership in the post-COVID phase exhibits recovery, the ratio (Post/Pre) staying below 1.0 suggests that the system has not fully returned to its pre-pandemic state. This study contributes to the urban resilience discourse, illustrating how PT system adjusts to COVID, offering insights for transportation planning.

Mapping COVID-19's potential infection risk based on land use characteristics: A case study of commercial activities in two Egyptian cities

The contagious COVID-19 has recently emerged and evolved into a world-threatening pandemic outbreak. After pursuing rigorous prophylactic measures two years ago, most activities globally reopened despite the emergence of lethal genetic strains. In this context, assessing and mapping activity characteristics-based hot spot regions facilitating infectious transmission is essential. Hence, our research question is: How can the potential hotspots of COVID-19 risk be defined intra-cities based on the spatial planning of commercial activity in particular? In our research, Zayed and October cities, Egypt, characterized by various commercial activities, were selected as testbeds. First, we analyzed each activity's spatial and morphological characteristics and potential infection risk based on the Centre for Disease Control and Prevention (CDCP) criteria and the Kriging Interpolation method. Then, using Google Mobility, previous reports, and semi-structured interviews, points of interest and population flow were defined and combined with the last step as interrelated horizontal layers for determining hotspots. A validation study compared the generated activity risk map, spatial COVID-19 cases, and land use distribution using logistic regression (LR) and Pearson coefficients (rxy). Through visual analytics, our findings indicate the central areas of both cities, including incompatible and concentrated commercial activities, have high-risk peaks (LR = 0.903, rxy = 0.78) despite the medium urban density of districts, indicating that urban density alone is insufficient for public health risk reduction. Health perspective-based spatial configuration of activities is advised as a risk assessment tool along with urban density for appropriate decision-making in shaping pandemic-resilient cities.

Evaluation of Global Post-Outbreak COVID-19 Treatment Interventions: A Systematic Review and Bibliometric Analysis of Randomized Controlled Trials

Purpose

The outbreak of COVID-19 has led to a global pandemic with millions of cases and deaths. Many randomized controlled trials (RCTs) were conducted to establish effective therapies. However, the methodological quality of these trials is paramount, as it directly impacts the reliability of results. This systematic review and bibliometric analysis aim to assess the methodological approach, execution diversity, global trends, and distribution of COVID-19 treatment RCTs post-outbreak, covering the period from the second wave and onward up to the present.

Methods

We utilize articles from three electronic databases published from September 1, 2020, to April 1, 2023. Inclusion and exclusion criteria were applied to identify relevant RCTs. Data extraction involved the collection of various study details. Risk of Bias (RoB) 2 tool assessed methodological quality, while implementation variability was evaluated against registration information. Bibliometric analysis, including keyword co-occurrence and country distribution, used VOSviewer and Tableau software.

Results

Initially, 501 studies were identified, but only 22 met the inclusion criteria, of which 19 had registration information. The methodological quality assessment revealed deficiencies in five main domains: randomization process (36%), deviations from intended interventions (9%), missing outcome data (4%), measurement of the outcome (18%), and selection of reported results (4%). An analysis of alignment between research protocols and registration data revealed common deviations in eight critical aspects. Bibliometric findings showcased global collaboration in COVID-19 treatment RCTs, with Iran and Brazil prominently contributing, while keyword co-occurrence analysis illuminated prominent research trends and terms in study titles and abstracts.

Conclusion

This study offers valuable insights into the evaluation of COVID-19 treatment RCTs. The scarcity of high-quality RCTs highlights the importance of enhancing trial rigor and transparency in global health emergencies.

The relation between barrier-free environment perception and campus commuting satisfaction

Introduction

The COVID-19 pandemic, which began in the last quarter of 2019, has had a significant impact on urban transportation. With increasing demand for urban transport, the internal roads and public spaces of university campuses play an important role in facilitating commuting and communication between various functional zones. While considerable research has been conducted on route planning, pedestrian-vehicle segregation, and safety management in the internal transportation environment of university campuses, empirical investigations exploring barrier-free inclusive campus environment design and the subjective evaluation of road and public space users in the aftermath of the COVID-19 pandemic are lacking. Recent developments in travel behavior models and positive psychology have led to an increased focus on the correlation among subjective perceptions, attitudes, emotions, and commuting satisfaction in urban transportation and planning design.

Methods

To elucidate this relationship, a study was conducted on the new campus of Central South University in Changsha, Hunan Province, China. Using 312 valid samples, a structural equation model was constructed to analyse the relationship between commuting satisfaction and the barrier-free environment perception of university students regarding the internal transportation environment of the campus.

Results

The results revealed that individuals' instantaneous barrier-free environment perceptions and long-term established positive emotions had a significant positive effect on commuting satisfaction. Furthermore, positive emotions were found to mediate the relationship between commuting attitudes induced by COVID-19, barrier-free environment perceptions, and commuting satisfaction.

Discussion

The results of this study provide a theoretical basis for the necessity of accessibility design in the post-COVID era. In addition, this study considers the perspective of users to provide ideas for the planning and construction of barrier-free campus environments that are based on convenient and inclusive design.

The impact of COVID-19 vaccination on human mobility: The London case

The COVID-19 pandemic has become a global public health crisis, causing significant morbidity and mortality worldwide. As an early response, different lockdowns were imposed in the UK (and the world) to limit the spread of the disease. Although effective, these measures profoundly impacted mobility patterns across cities, significantly reducing the number of people commuting to work or travelling for leisure. As different governments introduced massive vaccination programs to tackle the pandemic, cities have significantly but slowly increased human mobility, enabling the resumption of travel, work, and social activities. Nevertheless, how much can this return to normal mobility patterns be attributed to vaccines? In this study, we answer this question using a statistical approach, analysing two different open urban mobility datasets to quantify the effect vaccination rollouts have had on increased human activities.

Understanding changing public transit travel patterns of urban visitors during COVID-19: A multi-stage study

COVID-19 has caused huge disruptions to urban travel and mobility. As a critical transportation mode in cities, public transit was hit hardest. In this study, we analyze public transit usage of urban visitors with a nearly two-year smart card dataset collected in Jeju, South Korea - a major tourism city in the Asia Pacific. The dataset captures transit usage behavior of millions of domestic visitors who traveled to Jeju between January 1, 2019 and September 30, 2020. By identifying a few key pandemic stages based on COVID-19 timeline, we employ ridge regression models to investigate the impact of pandemic severity on transit ridership. We then derive a set of mobility indicators - from perspectives of trip frequency, spatial diversity, and travel range - to quantify how individual visitors used the transit system during their stay in Jeju. By further employing time series decomposition, we extract the trend component for each mobility indicator to study long-term dynamics of visitors' mobility behavior. According to the regression analysis, the pandemic had a dampening effect on public transit ridership. The overall ridership was jointly affected by national and local pandemic situations. The time series decomposition result reveals a long-term decay of individual transit usage, hinting that visitors in Jeju tended to use the transit system more conservatively as the pandemic endured. The study provides critical insights into urban visitors' transit usage behavior during the pandemic and sheds light on how to restore tourism, public transit usage, and overall urban vibrancy with some policy suggestions.

Epidemic modelling of monitoring public behavior using surveys during pandemic-induced lockdowns

BACKGROUND

Implementing a lockdown for disease mitigation is a balancing act: Non-pharmaceutical interventions can reduce disease transmission significantly, but interventions also have considerable societal costs. Therefore, decision-makers need near real-time information to calibrate the level of restrictions.

METHODS

We fielded daily surveys in Denmark during the second wave of the COVID-19 pandemic to monitor public response to the announced lockdown. A key question asked respondents to state their number of close contacts within the past 24 hours. Here, we establish a link between survey data, mobility data, and hospitalizations via epidemic modelling of a short time-interval around Denmark's December 2020 lockdown. Using Bayesian analysis, we then evaluate the usefulness of survey responses as a tool to monitor the effects of lockdown and then compare the predictive performance to that of mobility data.

RESULTS

We find that, unlike mobility, self-reported contacts decreased significantly in all regions before the nation-wide implementation of non-pharmaceutical interventions and improved predicting future hospitalizations compared to mobility data. A detailed analysis of contact types indicates that contact with friends and strangers outperforms contact with colleagues and family members (outside the household) on the same prediction task.

CONCLUSIONS

Representative surveys thus qualify as a reliable, non-privacy invasive monitoring tool to track the implementation of non-pharmaceutical interventions and study potential transmission paths.

Non-commuting intentions during COVID-19 in Nanjing, China: A hybrid latent class modeling approach

Non-commuting travel is essential for people to meet daily demands and regulate mental health, which is greatly disrupted due to the COVID-19 pandemic. To explore non-commuting intentions during COVID-19 across different groups of residents, this paper uses online survey data in Nanjing and constructs a hybrid latent class choice model that combines sociodemographic characteristics and psychological factors. Results showed that the respondents can be divided into two groups: the "cautious" group versus the "fearless" group. The "cautious" group with lower willingness to travel tend to be older, higher-income, higher-educated, female and full-time employees. Furthermore, the "cautious" group with higher perceived susceptibility is more obedient to government policies. In contrast, the "fearless" group is significantly affected by perceived severity and is more inclined to turn to personal protection against the pandemic. These results suggested that non-commuting trips were influenced not only by individual characteristics but also by psychological factors. Finally, the paper provides implications for the government to formulate COVID-19 management measures for the heterogeneity of different groups.

Associations between institutional-social-ecological factors and COVID -19 case-fatality: Evidence from 134 countries using multiscale geographically weighted regression (MGWR)

During the period in which the Omicron coronavirus variant was rapidly spreading, the impact of the institutional-social-ecological dimensions on the case-fatality rate was rarely afforded attention. By adopting the diagnostic social-ecological system (SES) framework, the present paper aims to identify the impact of institutional-social-ecological factors on the case-fatality rate of COVID-19 in 134 countries and regions and test their spatial heterogeneity. Using statistical data from the Our World In Data website, the present study collected the cumulative case-fatality rate from 9 November 2021 to 23 June 2022, along with 11 country-level institutional-social-ecological factors. By comparing the goodness of fit of the multiple linear regression model and the multiscale geographically weighted regression (MGWR) model, the study demonstrated that the effects of SES factors exhibit significant spatial heterogeneity in relation to the case-fatality rate of COVID-19. After substituting the data into the MGWR model, six SES factors were identified with an R square of 0.470 based on the ascending effect size: COVID-19 vaccination policy, age dependency ratio, press freedom, gross domestic product (GDP), COVID-19 testing policy, and population density. The GWR model was used to test and confirm the robustness of the research results. Based on the analysis results, it is suggested that the world needs to meet four conditions to restore normal economic activity in the wake of the COVID-19 pandemic: (i) Countries should increase their COVID-19 vaccination coverage and maximize COVID-19 testing expansion. (ii) Countries should increase public health facilities available to provide COVID-19 treatment and subsidize the medical costs of COVID-19 patients. (iii) Countries should strictly review COVID-19 news reports and actively publicize COVID-19 pandemic prevention knowledge to the public through a range of media. (iv) Countries should adopt an internationalist spirit of cooperation and help each other to navigate the COVID-19 pandemic. The study further tests the applicability of the SES framework to the field of COVID-19 prevention and control based on the existing research, offering novel policy insights to cope with the COVID-19 pandemic that coexists with long-term human production and life for a long time.

Near-term impact of COVID-19 pandemic on seniors' crash size and severity

Recent research revealed that COVID-19 pandemic was associated with noticeable changes in travel demand, traffic volumes, and traffic safety measures. Despite the reduction of traffic volumes across the US, several recent studies indicated that crash rates increased across different states during COVID-19 pandemic. Although some recent studies have focused on examining the changes in traffic conditions and crash rates before and during the pandemic, not enough research has been conducted to identify risk factors to crash severity. Even the limited research addressing the contributing factors to crash severity were focused on the pool category of drivers and no insight is available regarding older drivers, one of the most vulnerable groups to traffic collision and coronavirus. Moreover, these studies investigated the early impact of the COVID-19 pandemic mostly using up to three months of data. However, near-term and long-term effects of the COVID-19 pandemic are still unknown on traffic collisions. Therefore, this study aims to contribute to the literature by studying the near-term impact of the COVID-19 pandemic on crash size and severity among older drivers. To this end, a relatively large sample of crash data with senior drivers at fault was obtained and analyzed. To identify the main contributing factors affecting crash outcomes, Exploratory Factor Analysis was conducted on a high-dimension data set to identify potential latent factors which were validated through Confirmatory Factor Analysis. After that, Structural Equation Modeling technique was performed to examine the associations among the identified independent latent factors and the dependent variable. Additionally, SEM model identified the impact of the COVID-19 pandemic on seniors' crash severity. The findings reveal that several latent variables were the significant predictors of crash severity of older drivers including "Driving maneuver & crash location", "Road features and traffic control devices", "Driver condition & behavior", "Road geometric characteristics", "Crash time and lighting", and "Road class" latent factors. The binary variable of "Pandemic" was found to be as highly significant as the last four latent factors mentioned above. This means not only were older drivers more likely to be involved in higher crash size with higher severity level during the pandemic period, but also "Pandemic" was a risk factor to seniors as much as "Driver condition & behavior", "Road geometric characteristics", "Crash time & lighting", and "Road class" factors. The results of this study provide useful insights that may improve road safety among senior drivers during pandemic periods like COVID-19.

Use of noise prediction models for road noise mapping in locations that do not have a standardized model: a short systematic review

Faced with the accelerated growth of cities and the consequent increase in the number of motor vehicles, urban noise levels caused by vehicular traffic have increased considerably. To assess noise levels in cities and implement noise control measures or identify the problem's location in different urban areas, it is necessary to obtain the noise levels to which people are exposed. Noise maps are tools that have applications as they are cartographic representations of the noise level distribution in an area and over a period of time. This article aims to identify, select, evaluate, and synthesize information, through a systematic literature review, on using different road noise prediction models, in sound mapping computer programs in countries that do not have a standard noise prediction model. The analysis period was from 2018 to 2022. From a previous analysis of articles, the choice of topic was based on identifying various models for predicting road noise in countries without a standardized sound mapping model. The papers compiled by a systematic literature review showed that studies concentrated in China, Brazil, and Ecuador, the most used traffic noise prediction models, were the RLS-90 and the NMPB, and the most used mapping programs were SoundPLAN and ArcGIS with a grid size of 10 × 10 m. Most measurements were carried out during a 15-min period at a height from the ground level of 1.5 m. In addition, it was observed that research on noise maps in countries that do not have a local model has been increasing over time.

The impact of COVID-19 on visitors' wayfinding within healthcare centers

The novel COVID-19 pandemic has caused substantial calamities in developing countries such as Iran, which initially suffered from inadequate infrastructure essential for the pandemic control. Due to the ongoing development of this malady, healthcare centers are recognized as one of the most significant hotspots within public settings so they are directly pertinent to the physical and mental health of visitors. The main objective for conducting the present study is to investigate the impact of the COVID-19 pandemic on the visitors' wayfinding procedure within Qa'em hospital, located in Rasht, northern Iran. The adopted methodology in the present study is based on a comparison between the collected data regarding the wayfinding behavior of visitors before and after the outbreak of the COVID-19 pandemic using mixed methods, namely Space Syntax, gate counting, people following, and semi-structured interviews. The obtained empirical results displayed that visitors were significantly confused and hesitant throughout their wayfinding process after the outbreak of the pandemic. Indeed, spatial accessibility and legibility were not found to be adequate for facilitating the wayfinding of the visitors. Moreover, the requirements for the reconfiguration of furniture layout in the waiting areas, according to the underlying notions of social distancing, became conspicuous as the pragmatic implications for the post-pandemic healthcare centers.

A network-based analysis to assess COVID-19 disruptions in the Bogotá BRT system

The global COVID-19 crisis has severely affected mass transit in the cities of the global south. Fear of widespread propagation in public spaces and the dramatic decrease in human mobility due to lockdowns have resulted in a significant reduction of public transport options. We analyze the case of TransMilenio in Bogotá, a massive Bus Rapid Transit system that is the main mode of transport for an urban area of roughly 10 million inhabitants. Concerns over social distancing and new health regulations reduced the number of trips to under 20% of its historical values during extended periods of time during the lockdowns. This has sparked a renewed interest in developing innovative data-driven responses to COVID-19 resulting in large corpora of TransMilenio data being made available to the public. In this paper we use a database updated daily with individual passenger card swipe validation microdata including entry time, entry station, and a hash of the card's ID. The opportunity of having daily detailed minute-to-minute ridership information and the challenge of extracting useful insights from the massive amount of raw data (∼1,000,000 daily records) require the development of tailored data analysis approaches. Our objective is to use the natural representation of urban mobility offered by networks to make pairwise quantitative similarity measurements between daily commuting patterns and then use clustering techniques to reveal behavioral disruptions as well as the most affected geographical areas due to the different pandemic stages. This method proved to be efficient for the analysis of large amount of data and may be used in the future to make temporal analysis of similarly large datasets in urban contexts.

Systematic literature review on impacts of COVID-19 pandemic and corresponding measures on mobility

The unprecedented COVID-19 outbreak has significantly influenced our daily life, and COVID-19's spread is inevitably associated with human mobility. Given the pandemic's severity and extent of spread, a timely and comprehensive synthesis of the current state of research is needed to understand the pandemic's impact on human mobility and corresponding government measures. This study examined the relevant literature published to the present (March 2023), identified research trends, and conducted a systematic review of evidence regarding transport's response to COVID-19. We identified key research agendas and synthesized the results, examining: (1) mobility changes by transport modes analyzed regardless of government policy implementation, using empirical data and survey data; (2) the effect of diverse government interventions to reduce mobility and limit COVID-19 spread, and controversial issues on travel restriction policy effects; and (3) future research issues. The findings showed a strong relationship between the pandemic and mobility, with significant impacts on decreased overall mobility, a remarkable drop in transit ridership, changes in travel behavior, and improved traffic safety. Government implemented various non-pharmaceutical countermeasures, such as city lockdowns, travel restrictions, and social distancing. Many studies showed such interventions were effective. However, some researchers reported inconsistent outcomes. This review provides urban and transport planners with valuable insights to facilitate better preparation for future health emergencies that affect transportation.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11116-023-10392-2.

Estimating Mode of Transport in Daily Mobility during the COVID-19 Pandemic Using a Multinomial Logistic Regression Model

At the beginning of 2020 there was a spinning point in the travel behavior of people around the world because of the pandemic and its consequences. This paper analyzes the specific behavior of travelers commuting to work or school during the COVID-19 pandemic based on a sample of 2000 respondents from two countries. We obtained data from an online survey, applying multinomial regression analysis. The results demonstrate the multinomial model with an accuracy of almost 70% that estimates the most used modes of transport (walking, public transport, car) based on independent variables. The respondents preferred the car as the most frequently used means of transport. However, commuters without car prefer public transport to walking. This prediction model could be a tool for planning and creating transport policy, especially in exceptional cases such as the limitation of public transport activities. Therefore, predicting travel behavior is essential for policymaking based on people's travel needs.

A bibliometric and visualized analysis of liver fibrosis from 2002 to 2022

Fibrosis of the liver is a degenerative alteration that occurs in the majority of chronic liver disorders. Further progression can lead to cirrhosis, liver failure, and hepatocellular carcinoma, which can seriously affect the health and lives of patients. The field of liver fibrosis research has flourished in the last 20 years, with approximately 9000 articles retrieved from the Web of Science Core Collection database alone. In order to identify future research hotspots and potential paths in a thorough and scientifically reliable manner, it is important to organize and visualize the research on this topic from a holistic and very general perspective. This study used bibliometric analysis with CiteSpace and VOSviewer software to provide a quantitative analysis, hotspot mining, and commentary of articles published in the field of liver fibrosis over the last 20 years. This bibliometric analysis contains a total of 8994 articles with 45667 authors from 6872 institutions in 97 countries, published in 1371 journals and citing 156 309 references. The literature volume has steadily increased over the last 20 years. Research has focused on gastroenterology and hepatology, pharmacology and pharmacy, and medicine, research, and experimental areas. We found that the pathological mechanisms, diagnostic and quantitative methods, etiology, and antifibrotic strategies constitute the knowledge structure of liver fibrosis. Finding mechanisms for liver fibrosis regression, identifying precise noninvasive diagnostic and prognostic biomarkers, and creating efficient liver fibrosis patient treatments are the main goals of current research.

Quantitative resilience assessment of the network-level metro rail service's responses to the COVID-19 pandemic

The metro rail system has proven to be the most efficient high-capacity carriers. During the unprecedented coronavirus disease 2019 (COVID-19) challenge, non-pharmaceutical interventions become a widely adopted strategy to limit physical movements and interactions. For situational awareness and decision support, data-driven analytics about serviceability are invaluable to metro agencies and decision-makers of cities. This paper presents a data-driven analytical framework that quantitatively evaluates COVID-19-caused resilience performance of metro rails. Several characteristics (e.g., vulnerability, robustness, rapidity, and degree to return) of the metro system's responses to the disturbance were identified and modeled with multivariate multiple regression. The applicability and rationality of the resilience evaluation model were validated by the metro transit data of the United States. The preliminary results disclosed that metro rail transit encountered more vulnerability (90.6%) in passenger trips than motorbus and light rail (around 70%). A set of statistical models were employed to disentangle the effect of socio-demographic variables and COVID-19-related factors on the metro transit. The disclosed emerging knowledge of resilience provides an in-depth understanding of mobility trends for the public and time-sensitive decision support for the policy effects, to further improve the service and management of the metro system under the spread of the epidemic.

Quantifying human mobility resilience to the COVID-19 pandemic: A case study of Beijing, China

Human mobility, as a fundamental requirement of everyday life, has been most directly impacted during the COVID-19 pandemic. Existing studies have revealed its ensuing changes. However, its resilience, which is defined as people's ability to resist such impact and maintain their normal mobility, still remains unclear. Such resilience reveals people's response capabilities to the pandemic and quantifying it can help us better understand the interplay between them. Herein, we introduced an integrated framework to quantify the resilience of human mobility to COVID-19 based on its change process. Taking Beijing as a case study, the resilience of different mobility characteristics among different population groups, and under different waves of COVID-19, were compared. Overall, the mobility range and diversity were found to be less resilient than decisions on whether to move. Females consistently exhibited lower resilience than males; middle-aged people exhibited the lowest resilience under the first wave of COVID-19 while older adult's resilience became the lowest during the COVID-19 rebound. With the refinement of pandemic-control measures, human mobility resilience was enhanced. These findings reveal heterogeneities and variations in people's response capabilities to the pandemic, which can help formulate targeted and flexible policies, and thereby promote sustainable and resilient urban management.

Global tourism, climate change and energy sustainability: assessing carbon reduction mitigating measures from the aviation industry

As many business activities-especially those associated with the energy-intensive industries-continue to be major sources of greenhouse gas emissions, and hence significantly contributing to global warming, there is a perceived need to identify ways to make business activities eventually carbon neutral. This paper explores the implications of a changing climate for the global tourism business and its intertwining global aviation industry that operates in a self-regulatory environment. Adopting a bibliometric analysis of the literature in the domain of global tourism and climate change (772 articles), the paper reveals the underlying sustainability issues that entail unsustainable energy consumption. The aviation industry as a significant source of carbon emission within the sector is then examined by analyzing the top 20 largest commercial airlines in the world with respect to its ongoing mitigating measures in meeting the Paris Agreement targets. While self-regulatory initiatives are taken to adopt Sustainable Aviation Fuels (SAF) as alternative fuel production and consumption for drastically reducing carbon emission, voluntary alignment and commitment to long-term targets remain inconsistent. A concerted strategic approach to building up complementary sustainable infrastructures among the global network of airports based in various international tourist destination cities to enable a measurable reduction in carbon emission is necessary to achieve a transformational adaptation of a business sector that is of essence to the recovery of the global economy while attempting to tackle climate change in a post-COVID-19 era.

Bibliometric analysis of research on Alzheimer’s disease and non-coding RNAs: Opportunities and challenges

Background

Non-coding RNAs (ncRNA) are a kind of RNA that does not encode protein, which play an important role in Alzheimer’s disease (AD). However, there is a lack of bibliometric analysis and visualization analysis of the research related to AD and ncRNAs.

Materials and methods

Literature related to AD and ncRNAs in the last decade were searched through the Web of Science Core Collection (WOSCC). The relevant information from all the searched articles was collected. The bibliometric visualization website, CiteSpace, and VOSviewer were used for visualization analysis of countries/regions, institutions, authors, and keywords.

Results

In total, 1,613 kinds of literature were published in the field. Literature in this field were published in 494 journals. The Journal of Alzheimer’s Disease was the most popular journal. China, Louisiana State University System, and Lukiw WJ were the countries/regions, institutions, and authors with the highest scientific productivity, respectively. The research hotspots in this field focused on the role and mechanism of ncRNAs, especially microRNAs, in AD. The level of research was mainly based on basic research, focusing on animal and cellular levels, and related to proteomics. “Circular RNAs,” “regulation of neuroinflammation,” and “tau protein” were the future research directions.

Conclusion

Taken together, the field of AD and ncRNAs is developing well. The research hotspots and frontiers in this field can provide a reference for researchers to choose their research direction.

An overview and thematic analysis of research on cities and the COVID-19 pandemic: Toward just, resilient, and sustainable urban planning and design

Since early 2020, researchers have made efforts to study various issues related to cities and the pandemic. Despite the wealth of research on this topic, there are only a few review articles that explore multiple issues related to it. This is partly because of the rapid pace of publications that makes systematic literature review challenging. To address this issue, in the present study, we rely on bibliometric analysis techniques to gain an overview of the knowledge structure and map key themes and trends of research on cities and the pandemic. Results of the analysis of 2,799 articles show that research mainly focuses on six broad themes: air quality, meteorological factors, built environment factors, transportation, socio-economic disparities, and smart cities, with the first three being dominant. Based on the findings, we discuss major lessons that can be learned from the pandemic and highlight key areas that need further research.

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.

Mobility and Policy Responses During the COVID-19 Pandemic in 2020

Objective: This paper quantitatively explores determinants of governments’ non-pharmaceutical policy responses to the COVID-19 pandemic. Our focus is on the extent to which geographic mobility affected the stringency of governmental policy responses.

Methods: Using cross-country, daily frequency data on geographic mobility and COVID-19 policy stringency during 2020, we investigate some of the determinants of policy responses to COVID-19. In order to causally identify the effect of geographic mobility on policy stringency, we pursue an instrumental variable strategy that exploits climate data to identify arguably exogenous variation in geographic mobility.

Results: We find that societies that are more geographically mobile have governmental policy responses that are less stringent. Examining disaggregated mobility data, we show that the negative relation between geographic mobility and policy stringency is the stronger for commercially-oriented movements than for geographic movements that relate to civil society.

Conclusion: The results suggest that policy-makers are more willing to trade-off public health for economic concerns relative to other civil concerns.

A systematic review of the impacts of the coronavirus crisis on urban transport: Key lessons learned and prospects for future cities

The COVID-19 pandemic continues to have a significant impact on the transport sector worldwide. Lockdown and physical distancing requirements continue to be enforced in many cities leading to severe travel restrictions and travel demand reduction to limit the spread of the disease. This article provides bibliometric evidence-based insights into how the pandemic has exposed the vulnerability of established public transport systems and shared mobility solutions. It shows how some transport interventions can accelerate the shift to sustainable urban mobility practices such as micro-mobility and active transport. To accomplish this, the article examines recent studies (244 publications) from the Scopus database using a rigorous systematic literature review approach covering the period from January 2020 to February 2021. Importantly, the mapping of bibliographic coupling and co-citation analysis showed four heterogeneous clusters representing research efforts into "environment", "travel behavior and mode choice", "public transport", and "interventions". Inductive reasoning is used to analyze the disruptions that cities have encountered worldwide, the rapid interventions that were put in place, the aftershocks and the short and long-term impacts. Finally, the paper summarizes the lessons learned and opportunities ahead, and the challenges that must be overcome. The article also outlines pathways to build on the momentum of sustainable practices as part of a holistic approach for enabling resilient transport solutions for the new urban world.

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.

The breathing human infrastructure: Integrating air quality, traffic, and social media indicators

Outdoor air pollution is a complex system that is responsible for the deaths of millions of people annually, yet the integration of interdisciplinary data necessary to assess air quality's multiple metrics is still lacking. This case study integrates atmospheric indicators (concentrations of criteria pollutants including particulate matter and gaseous pollutants), traffic indicators (permanent traffic monitoring station data), and social indicators (community responses in Twitter archives) representing the interplay of the three critical pillars of the United Nations' Triple Bottom Line: environment, economy, and society. During the watershed moment of the COVID-19 pandemic lockdowns in Florida, urban centers demonstrated the gaps and opportunities for understanding the relationships, through correlations rather than causations, between urban air quality, traffic emissions, and public perceptions. The relationship between the perception and the traffic variables were strongly correlated, however no correlation was observed between the perception and actual air quality indicators, except for NO₂. These observations might consequently infer that traffic serves as people's proxy for air quality, regardless of actual air quality, suggesting that social media messaging around asthma may be a way to monitor traffic patterns in areas where no infrastructure currently exists or is prohibited to build. It also indicates that people are less likely to be reliable sensors to accurately measure air quality due to bias in their observations of traffic volume and/or confirmation biases in broader social discourse. Results presented herein are of significance in demonstrating the capacity for interdisciplinary studies to consider the predictive capacities of social media and air pollution, its use as both lever and indicator of public support for air quality legislation and clean-air transitions, and its ability to overcome limitations of surface monitoring stations.

Management of Smart and Sustainable Cities in the Post-COVID-19 Era: Lessons and Implications

Nowadays, the concept of smart sustainable governance is wrapped around basic principles such as: (i) transparency, (ii) accountability, (iii) stakeholders’ involvement, and iv) citizens’ participation. It is through these principles that are influenced by information and communication technologies (ICT), Internet of Things (IoT), and artificial intelligence, that the practices employed by citizens and their interaction with electronic government (e-government) are diversified. Previously, the misleading concepts of the smart city implied only the objective of the local level or public officials to utilize technology. However, the recent European experience and research studies have led to a more comprehensive notion that refers to the search for intelligent solutions which allow modern sustainable cities to enhance the quality of services provided to citizens and to improve the management of urban mobility. The smart city is based on the usage of connected sensors, data management, and analytics platforms to improve the quality and functioning of built-environment systems. The aim of this paper is to understand the effects of the pandemic on smart cities and to accentuate major exercises that can be learned for post-COVID sustainable urban management and patterns. The lessons and implications outlined in this paper can be used to enforce social distancing community measures in an effective and timely way, and to optimize the use of resources in smart and sustainable cities in critical situations. The paper offers a conceptual overview and serves as a stepping-stone to extensive research and the deployment of sustainable smart city platforms and intelligent transportation systems (a sub-area of smart city applications) after the COVID-19 pandemic using a case study from Russia. Overall, our results demonstrate that the COVID-19 crisis encompasses an excellent opportunity for urban planners and policy makers to take transformative actions towards creating cities that are more intelligent and sustainable.

How residential energy consumption has changed due to COVID-19 pandemic? An agent-based model

Integrating occupant behavior with residential energy use for detailed energy quantification has attracted research attention. However, many of the available models fail to capture unseen behavior, especially in unprecedented situations such as COVID-19 lockdowns. In this study, we adopted a hybrid approach consisting of agent-based simulation, machine learning and energy simulation techniques to simulate the urban energy consumption considering the occupants' behavior. An agent-based model is developed to simulate the in-home and out-of-home activities of individuals. Separate models were developed to recognize physical characteristics of residential dwellings, including heating equipment, source of energy, and thermostat setpoints. The developed modeling framework was implemented as a case study for the Central Okanagan region of British Columbia, where alternative COVID-19 scenarios were tested. The results suggested that during the pandemic, the daily average in-home-activity duration (IHD) increased by approximately 80%, causing the energy consumption to increase by around 29%. After the pandemic, the average daily IHD is expected to be higher by approximately 32% compared with the pre-pandemic situation, which translates to an approximately 12% increase in energy consumption. The results of this study can help us understand the implications of the imposed COVID-19 lockdown with respect to energy usage in residential locations.

ANN-Based traffic volume prediction models in response to COVID-19 imposed measures

Many countries around the globe have imposed several response measures to suppress the rapid spread of the COVID-19 pandemic since the beginning of 2020. These measures have impacted routine daily activities, along with their impact on economy, education, social and recreational activities, and domestic and international travels. Intuitively, the different imposed policies and measures have indirect impacts on urban traffic mobility. As a result of those imposed measures and policies, urban traffic flows have changed. However, those impacts are neither measured nor quantified. Therefore, estimating the impact of these combined yet different policies and measures on urban traffic flows is a challenging task. This paper demonstrates the development of an artificial neural networks (ANN) model which correlates the impact of the imposed response measure and other factors on urban traffic flows. The results show that the adopted ANN model is capable of mapping the complex relationship between traffic flows and the response measures with a high level of accuracy and good performance. The predicted values are closed to the observed ones. They are clustered around the regression line, with a coefficient of determination ( R 2 ) of 0.9761. Furthermore, the developed model can be generalized to determine the anticipated demand levels resulted from imposing any of the response measures in the post-pandemic era. This model can be used to manage traffic during mega-events. It can be also utilized for disaster or emergency situations, where traffic flow estimates are highly required for operational and planning purposes.

Tempo-spatial infection risk assessment of airborne virus via CO2 concentration field monitoring in built environment

The aerosol transmission was academically recognized as a possible transmission route of Coronavirus disease 2019 (COVID-19). We established an approach to assess the indoor tempo-spatial airborne-disease infection risks through aerosol transmission via real-time CO₂ field measurement and occupancy monitoring. Compared to former studies, the proposed method can evaluate real-time airborne disease infection risks through aerosol transmission routes. The approach was utilized in a university office. The accumulated infection risk was calculated for three occupants with practical working schedules (from occupancy recording) and one hypothesis occupant with a typical working schedule. COVID-19 was used as an example. Results demonstrated that the individual infection risks diversified with different dwell times and working places in the office. For the three occupants with a practical working schedule, their 3-day accumulated infection risks were respectively 0.050%, 0.035%, 0.027% and 0.041% due to 11.6, 9.0 and 13.8 h exposure with an initial infector percentage of 1%. The results demonstrate that location and dwell time are both important factors influencing the infection risk of certain occupant in built environment, whereas existing literature seldom took these two points into consideration simultaneously. On the contrary, our proposed approach treated the infection risks as place-by-place, time-by-time and person-by-person diversified in the built environment. The risk assessment results can provide early warning for building occupants and contribute to the transmission control of air-borne disease.

A review of the global climate change impacts, adaptation, and sustainable mitigation measures

Climate change is a long-lasting change in the weather arrays across tropics to polls. It is a global threat that has embarked on to put stress on various sectors. This study is aimed to conceptually engineer how climate variability is deteriorating the sustainability of diverse sectors worldwide. Specifically, the agricultural sector's vulnerability is a globally concerning scenario, as sufficient production and food supplies are threatened due to irreversible weather fluctuations. In turn, it is challenging the global feeding patterns, particularly in countries with agriculture as an integral part of their economy and total productivity. Climate change has also put the integrity and survival of many species at stake due to shifts in optimum temperature ranges, thereby accelerating biodiversity loss by progressively changing the ecosystem structures. Climate variations increase the likelihood of particular food and waterborne and vector-borne diseases, and a recent example is a coronavirus pandemic. Climate change also accelerates the enigma of antimicrobial resistance, another threat to human health due to the increasing incidence of resistant pathogenic infections. Besides, the global tourism industry is devastated as climate change impacts unfavorable tourism spots. The methodology investigates hypothetical scenarios of climate variability and attempts to describe the quality of evidence to facilitate readers' careful, critical engagement. Secondary data is used to identify sustainability issues such as environmental, social, and economic viability. To better understand the problem, gathered the information in this report from various media outlets, research agencies, policy papers, newspapers, and other sources. This review is a sectorial assessment of climate change mitigation and adaptation approaches worldwide in the aforementioned sectors and the associated economic costs. According to the findings, government involvement is necessary for the country's long-term development through strict accountability of resources and regulations implemented in the past to generate cutting-edge climate policy. Therefore, mitigating the impacts of climate change must be of the utmost importance, and hence, this global threat requires global commitment to address its dreadful implications to ensure global sustenance.

Assessing the impact of mobility on the incidence of COVID-19 in Dublin City

COVID-19 has had a major impact on the transport systems around the world. Several transport-related policies were implemented in short period of time to contain the spread of the pandemic. These policies had a major influence on travel behavior and people's perception towards the safety of different modes of transport, especially public transport, thus affecting several sustainable mobility initiatives. To build a resilient and sustainable transport system and to rebuild trust in public transport, it is important to understand the role of mobility in the spread of COVID-19 pandemic. The present study investigates the relationship between mobility and reported COVID-19 infections using data from Dublin city. Different modes of transport including traffic volume, bus passengers, pedestrians and cyclists were considered in the study during a forty week period. Multiple scenarios involving two-week lag and three-week lag of mobility data and COVID-19 infections were considered in building statistical models. Results showed that, 36.2% of the reported COVID-19 infections after a two-week lag and 33% of the infections after a three-week lag. Our research examines the links between movements and COVID-19 numbers, but clearly this was not the only reason for increased case numbers as many other events impacted on increased numbers. The study further discusses the policy implications and strategies for ensuring a resilient and sustainable transport system.

The daily urban dynamic indicator: Gauging the urban dynamic in Porto during the COVID-19 pandemic

The SARS-CoV-2 outbreak motivated the development of a myriad of weekly and daily indicators that track economic activity to estimate and predict the consequences of the pandemic. With some exceptions, these indicators are calculated at the country level and are mainly focused on tracking economic factors, disregarding local urban phenomena. To address this, we present the Urban Dynamic Indicator (UDI), a novel composite indicator designed to measure a city's daily urban dynamic. The UDI is applied to Porto municipality, in Portugal, and it corresponds to a latent factor obtained through a factor analysis over seasonal adjusted daily data regarding traffic intensity, public transportation usage, internet usage in public buses, NO₂ emissions and noise level. The UDI's values show that, by the end of 2020, despite the approach of economic activity to its pre-pandemic values, as suggested by the Portuguese Daily Economic Indicator (DEI), Porto urban dynamic did not recover completely. The UDI enriches the information available for Porto city planners and policymakers to respond to crisis situations and to gauge the application of local policies that contribute to urban sustainable planning. Furthermore, the methodology defined in this work can be followed for the development of daily urban dynamic indicators elsewhere.

Adapting Urban Transport Planning to the COVID-19 Pandemic: An Integrated Fermatean Fuzzy Model

The critical worldwide problem of adapting urban transport planning to COVID-19 is for the first time comprehensively addressed and solved in this study. It primarily aims to help transport planners increase the resilience of transport systems. Firstly, a multi-level decision-making hierarchy structure based on four main criteria and 17 sub-criteria is introduced for relevant stakeholders to provide a practical framework for assessing existing transport plans. Then, a three-stage integrated Fermatean fuzzy model for adapting urban transport planning to the pandemic is presented. The model hybridizes the method based on the removal effects of criteria (MEREC) and combined compromise solution (CoCoSo) method into a unique methodological framework under the Fermatean fuzzy environment. A case study provides decision-making guidelines on how to adapt transport plans to COVID-19 in the real-world context of Belgrade, Serbia. The research findings show that the pandemic significantly changed the priorities of transport planning strategies and measures. "Non-motorized travel" is now the best alternative since its numerous short-term measures lead to better transport service. The major advantages of the introduced model are higher flexibility and a more precise fusion of experts' preference information. The integrated Fermatean fuzzy model could be used for adapting other emerging problems to COVID-19.

Wishful thinking? Addressing the long-term implications of COVID-19 for transport in Nigeria

With their inherent economic and infrastructure challenges, developing countries must assess commuters' travel behaviour and establish whether consumers' desire for sustainable transportation is feasible or merely wishful thinking. Using a qualitative research design, these issues were explored based on semi-structured interviews conducted with 72 participants across the six geopolitical zones of Nigeria. Findings suggest that the desires and dreams of the commuters are great, and they want to reduce their journeys, work from home, and do online shopping. However, when they consider the inherent challenges, they can only hope for a better future. COVID-19 has impacted the ownership or use of motorised and non-motorised transport, but this is also influenced by affordability, convenience, and awareness. Likewise, there are long-term effects on home and work location choices, but this is further influenced by the large informal economy of the country, job accessibility and the infrastructural developments across the country.

Evolution of urban mobility behaviour in Brussels as a result of the COVID‐19 pandemic

The goal of this research is to understand the impact of COVID‐19 restriction measures on the change in urban mobility in Brussels, Belgium. With daily data over the past 2 years depicting both the affluence to different places and the level and type of restrictions, we investigate through regression analysis their impacts on the changes in driving, public transport and cycling use. We find that cycling increased significantly (+63%), and that driving levels have returned to pre‐COVID levels after a significant reduction in spring 2020, while the return to public transport has been slower. We also find that the change in cycling use was not influenced by COVID‐19 restrictions, although telework and closing of retail establishments strongly affected the other modes.

Transportation Planning, Mobility Habits and Sustainable Development in the Era of COVID-19 Pandemic

Starting from December 2019, the world has faced an unprecedented health crisis caused by the new coronavirus (COVID-19) due to SARS-CoV-2 [...]

Role of latent factors and public policies in travel decisions under COVID-19 pandemic: Findings of a hybrid choice model

Policy measures to control the spread of COVID-19 imposed by different countries have a devastating impact on people's travel behaviors. Differing from the normal situation where general concerns on travel time and cost determine the travel choices, the uncertainty underlying behavior change in the case of a pandemic might be largely attributed to the latent aspects, i.e., social responsibility, risk perception, attitudes, which could diminish the effects of main attributes on travel decisions. Therefore, this paper examines the effects of COVID-19 related policies on individuals' travel choices influenced by the latent aspects. A stated choice experiment was designed to collect people's responses under policy measures to various transportation modes. Results of a hybrid choice model show that COVID-19 related policies significantly affect individuals' transportation mode choice decisions during pandemic situations. The attributes, like travel time and travel cost, which significantly impact travel behavior in normal situations, become less relevant. Moreover, the travel preferences during the pandemic are significantly associated with latent factors of social responsibility, fear of infection, perceived risk, and travel anxiety. In general, public transportation is identified as an insecure alternative compared with other private modes, and people who are more socially responsible tend to travel less during the pandemic. Outcomes of this study could be of value to policymakers and public health emergencies, e.g., government authorities to utilize such knowledge in providing social support for these COVID-19 countermeasures and designing customized policies for specific population groups.

Service contracting as a policy response for public transport recovery during the Covid-19 Pandemic: A preliminary evaluation

We examine and assess the service contracting (SC) program implemented for the first time in Metro Manila, Philippines as a response to the impact of the pandemic on road-based public transport sector. We develop an evaluation framework, consisting of three indicators: social amelioration, increase in transport supply and performance improvement. These indicators are the purported objectives of SC. Using a mix of qualitative and quantitative methods, our evaluation suggests that although SC has brought positive impact in terms of the first two indicators, there is no robust evidence so far that may suggest that SC has improved the performance of public transport service delivery. We also find that while the primary objective of providing social amelioration to affected operators is appropriate during the time of the pandemic, this has also brought challenges in financially sustaining the program and in effecting improvements to public transport services. Our work aims to contribute as an empirical case study on the upsides and downsides of service contracting implemented as a business model for public transport provision during the pandemic.

Office buildings occupancy analysis and prediction associated with the impact of the COVID-19 pandemic

Buildings' occupancy is one of the important factors causing the energy performance and sustainability gap in buildings. Better occupancy prediction decreases this gap both in the design stage and in the use phase of the building. Machine learning-based models proved to be very accurate and fast for occupancy prediction when buildings are exploited under normal conditions. Meanwhile, during the Covid-19 pandemic occupancy of the offices has dramatically changed. The study presents 2 office buildings' long-term monitoring results for different periods of the pandemic. It aims to analyse actual occupancies during the pandemic and its influence on the ELM (Extreme Learning Machine) based occupancy-forecasting models' reliability. The results show much lower actual occupancies in the offices than given in standards and methodologies; it is still low even when quarantines are cancelled. Average peak occupancy within the whole measured period is: for Building A - 12-20% and for Building B - 2-23%. The daily occupancy schedules differ for both offices as they belong to different industries. ELM-SA model has shown low accuracies during pandemic periods as a result of lower occupancies - R² = 0.27-0.56.

Examining spatiotemporal evolution of racial/ethnic disparities in human mobility and COVID-19 health outcomes: Evidence from the contiguous United States

Social distancing has become a key countermeasure to contain the dissemination of COVID-19. This study examined county-level racial/ethnic disparities in human mobility and COVID-19 health outcomes during the year 2020 by leveraging geo-tracking data across the contiguous US. Sets of generalized additive models were fitted under cross-sectional and time-varying settings, with percentage of mobility change, percentage of staying home, COVID-19 infection rate, and case-fatality ratio as dependent variables, respectively. After adjusting for spatial effects, built environment, socioeconomics, demographics, and partisanship, we found counties with higher Asian populations decreased most in travel, counties with higher White and Asian populations experienced the least infection rate, and counties with higher African American populations presented the highest case-fatality ratio. Control variables, particularly partisanship and education attainment, significantly influenced modeling results. Time-varying analyses further suggested racial differences in human mobility varied dramatically at the beginning but remained stable during the pandemic, while racial differences in COVID-19 outcomes broadly decreased over time. All conclusions hold robust with different aggregation units or model specifications. Altogether, our analyses shine a spotlight on the entrenched racial segregation in the US as well as how it may influence the mobility patterns, urban forms, and health disparities during the COVID-19.

Comparison of COVID-19 infection risks through aerosol transmission in supermarkets and small shops

Aerosol transmission is academically recognized as possible transmission route of Coronavirus disease 2019 (COVID-19). We established an approach to assess the airborne-disease infection risks through aerosol transmission based on the dose-response model and aerosol transport model. The accuracy of evaluation was guaranteed with on-site surveyed ventilation rate and occupant behavior. With the proposed approach, COVID-19 infection risks in 5 typical supermarkets and 21 small shops were evaluated. With one original infected early-shift staff, the average aerosols concentrations at steady-state are 1.06 × 10^-3 RNA copies/m³ in the supermarkets and 4.73 × 10^-2 RNA copies/m³ in the small shops. With the assumption of 5% original infected staff in the retail buildings, the infection probability of one customer is 1.40 × 10^-6 for visiting one small shop and 6.22 × 10^-6 for visiting one supermarket. The averaged infection risk in the supermarkets is higher than the small shops (p-value<0.001). On the other hand, the infection risks are higher for the staff working with the infected staff compared with the customers. The proposed approach can be applied to other occupied buildings and assist the pandemic control policy making for sustainable cities and society.

Development of Electromobility in European Union Countries under COVID-19 Conditions

The introduction of electromobility contributes to an increase in energy efficiency and lower air pollution. European countries have not been among the world’s leading countries in this statistic. In addition, there have been different paces in the implementation of electromobility in individual countries. The main purpose of this paper is to determine the directions of change and the degrees of concentration in electromobility in European Union (EU) countries, especially after the economic closure as a result of the COVID-19 pandemic. The specific objectives are to indicate the degree of concentration of electromobility in the EU and changes in this area, especially during the COVID-19 pandemic; to determine the dynamics of changes in the number of electric cars in individual EU countries, showing the variability in this aspect, while also taking into account the crisis caused by COVID-19; to establish the association between the number of electric cars and the parameters of the economy. All EU countries were selected for study by the use of the purposeful selection procedure, as of December 31, 2020. The analyzed period covered the years 2011–2020. It was found that in the longer term, the development of electromobility in the EU, measured by the number of electric cars, is closely related to the economic situation in this area. The crisis caused by the COVID-19 pandemic has influenced the economic situation in all EU countries, but has not slowed down the pace of introducing electromobility, and may have even accelerated it. In all EU countries, in the first year of the COVID-19 pandemic, the dynamics of introducing electric cars into use increased. The growth rate in the entire EU in 2020 was 86%, while in 2019 it was 48%. The reason was a change in social behavior related to mobility under conditions of risk of infection. COVID-19 has become a positive catalyst for change. The prospects for the development of this type of transport are very good because activities related to the development of the electromobility sector perfectly match the needs related to the reduction of pollution to the environment.

Influence of COVID-19 Mobility-Restricting Policies on Individual Travel Behavior in Malaysia

The COVID-19 pandemic has resulted in new postpandemic travel patterns as a result of the stay-at-home policies and restricted movement orders imposed by the Malaysian government. The purpose of this study was to investigate the changes in individual travel behavior after the government imposed a series of lockdowns, also known as movement control orders (MCO). From March to April 2021, a questionnaire survey was distributed throughout Malaysia, and 435 complete responses were collected. Results indicated that the respondents predominantly chose private cars for various traveling purposes during the pandemic. When choosing a travel mode during the pandemic, married respondents and essential workers placed a significantly higher priority on pandemic-related - items such as cleanliness, infection concern, social distance, and wearing face masks, compared to single respondents and nonessential workers. Binary logistic regression models were developed to estimate individuals’ propensity to make trips for different purposes, i.e., work/study, social activities, recreational activities, and religious activities. Results indicated that essential workers were nearly three times more likely than the general population to make a work trip during the pandemic. Regarding social and recreational trips, males were more likely to make such types of trips as compared to females. Furthermore, those who perceived a higher risk of infection were less likely to make social and recreational trips. Regarding religious trips, males were significantly more likely to make such trips during the pandemic as compared to females. In addition, Muslims had significantly higher odds of making a trip for religious purposes during the pandemic. The findings of this study could be useful in transportation planning when considering travel restrictions during pandemics based on peoples’ travel purposes and mode choices.

Spatiotemporal patterns of the COVID-19 control measures impact on industrial production in Wuhan using time-series earth observation data

Understanding the spatiotemporal patterns of the COVID-19 impact on industrial production could improve the estimation of the economic loss and sustainable work resumption policies in cities. In this study, assuming and checking a correlation between the land surface temperature (LST) and industrial production, we applied the BFAST algorithm and linear regression models on multi-temporal MODIS data to derive monthly time-series deviation of LST with a spatial resolution of 1 × 1 km, to quantificationally explore the fine-scale spatiotemporal patterns of the COVID-19 control measures impact on industrial production, within Wuhan city. The results demonstrate that (1) the trend of time-series LST could partly reflect the impact of the COVID-19 pandemic on industrial production, and the year-around industrial production was less than expectations, with a fall of 14.30%; (2) the most serious COVID-19 impact on industrial production appeared in Mar. and Apr., then, after the lifting of lockdown, some regions (approximate 4.90%) firstly returned to expected levels in Jun, and almost all regions (98.49%) have completed the resumption of work and production before Nov.; (3) the southwest and south-central had more serious impact of the COVID-19 pandemic, approximate twice as much as that in the north and suburban, in Wuhan. The results and findings elaborated the spatiotemporal distribution and their changes during 2020 within Wuhan, which could provide a beneficial support for assessment of the COVID-19 pandemic and implementation of resumption plans for sustainable development.

How did human dwelling and working intensity change over different stages of COVID-19 in Beijing?

The COVID-19 pandemic has changed human daily activities significantly. Understanding the nature, causes, and extent of these changes is essential to evaluate the pandemic's influence on commerce, transportation, employment, and environment, among others. However, existing studies mainly focus on changes to general human mobility patterns; few have investigated changes in specific human daily activities. Based on one-year longitudinal mobile phone positioning data for more than 31 million users in Beijing, we tracked intensity changes in two basic human daily activities, dwelling and working, over the stages of COVID-19. The results show that during COVID-19 outbreak, human working intensity decreased about 60% citywide, while dwelling intensity decreased about 40% in some work and education areas. After COVID-19 was under control, intensity in most regions has recovered, but that in schools, hotels, entertainment venues, and tourism areas has not. These intensity changes at regional scale are due to behavior changes at individual scale: about 43% of residents left Beijing before COVID-19, while only 16% have returned back; all commuters decreased their commuting times during COVID-19, while only 75% have reverted to normal. The findings reveal variations in human activities caused by COVID-19 that can support targeted urban management in the post-epidemic era.

COVID-19, internet, and mobility: The rise of telework, telehealth, e-learning, and e-shopping

This study provides new evidence on changes in a range of online activities due to the Coronavirus disease (COVID-19) pandemic. Online activities replaced physical participation in activities and contributed to changes in urban mobility during the pandemic. Using data from a nationwide survey in Greece, the paper examines changes in the importance and the frequency of engaging in online activities before and during COVID-19. Findings show that both the importance and the frequency of engaging in telework, teleconferencing, online learning (e-learning), telehealth, and online shopping (e-shopping) significantly increased during COVID-19 compared to pre-COVID-19. Substantial increases in importance were reported for telework (31% increase), teleconferencing (34% increase), online learning (34% increase), and telehealth (21% increase). Those who, on a daily basis, teleworked, teleconferenced, and made video calls with family or friends quadrupled during COVID-19, while daily online learners increased seven-fold. Telehealth and online shopping also increased but more modestly. Urban mobility in the post-COVID-19 era is likely to depend on the degree of prevalence and acceptance of these remote online activities, together with a set of complex and interconnected factors related to urban form, the spatial planning and decision-making system, and social awareness about the future of cities.

Relationships among COVID-19 Prevention Practices, Risk Perception and Individual Characteristics: A Temporal Analysis

The effectiveness of public health measures in containing an infectious disease largely depends on how the general public is taking the prevention practices in daily lives. Previous studies have shown that different risk perceptions and sociodemographic characteristics may lead to vastly different prevention behaviors. This paper applies a temporal perspective in examining the changing patterns of prevention practices over time and their dynamic relationships with the perceived risk towards COVID-19 and its individual characteristics. Three key timelines (February, April, and June of 2020) were identified to represent the early, lockdown, and reopening stages of the first wave. Data were drawn from an online survey conducted in the Greater Toronto Area (GTA) of Canada (n = 470). Chi-square tests and logistic regression models revealed important temporal patterns in practicing different hygienic and mobility-related prevention measures and the respondents' risk perceptions during the three timelines. The factors predicting the level of prevention practices vary across the three timelines, based on the specific type of prevention, and within the changing public health contexts. This study contributes to the literature on COVID-19 by incorporating a temporal perspective in conceptualizing prevention predictors. It provides crucial insights for developing timely public health strategies to improve infectious disease prevention at different stages and for individuals with varying backgrounds.

Impact of COVID-19 on Urban Energy Consumption of Commercial Tourism City

In 2020, the COVID-19 pandemic has spread worldwide. To alleviate this spread, various blockade policies have been implemented in many areas. This has led to a sluggish demand in the world's major economies, sharp drop in the trade index, and negative growth in energy consumption. To formulate a better epidemic prevention policy for urban energy consumption of commercial tourism cities, this study summarizes the major statistics of energy supply and demand before and during the epidemic period based on actual data. The characteristics of energy consumption in different sectors, including hotels, transportation, tourism culture, and public utilities, are then analyzed in detail. Finally, the energy consumption features of commercial tourism cities represented by Macao are compared to those of other typical countries (e.g., Italy, United States, Japan, and Brazil). These analyses demonstrate the impact of COVID-19 on the energy consumption in commercial tourism cities, which provides insights for the government or energy providers to formulate policies to adapt to this pandemic.