Safe traveling in public transport amid COVID-19

Model predicted human mobility explains COVID-19 transmission in urban space without behavioral data

The SARS-CoV-2 virus is primarily transmitted through in-person interactions, and so its growth in urban space is a complex function of human mobility behaviors that cannot be adequately explained by standard epidemiological models. Recent studies leveraged fine-grained urban mobility data to accurately model the viral spread, but such data pose privacy concerns and are often difficult to collect, especially in low- and middle-income countries (LMICs). Here, we show that the metapopulation epidemiological model incorporated with a simple gravity mobility model can be sufficient to capture most of the complex epidemic dynamics in urban space, largely reducing the need for empirical mobility data. Extensive experiments on 30 cities in the United States, India and Brazil show that our model consistently reproduces complex, distinctive COVID-19 growth curves with high accuracy. It also provides a theoretical explanation of the emergence of urban "superspreading", where a few high-risk neighborhoods account for most subsequent infections. Furthermore, with the aid of the proposed framework, we can inform mobility-aware travel restrictions to achieve a better balance between social cost and disease prevention, which facilitates sustainable epidemic control and supports the gradual transition to a post-pandemic world.

Infectious disease responses to human climate change adaptations

Many recent studies have examined the impact of predicted changes in temperature and precipitation patterns on infectious diseases under different greenhouse gas emissions scenarios. But these emissions scenarios symbolize more than altered temperature and precipitation regimes; they also represent differing levels of change in energy, transportation, and food production at a global scale to reduce the effects of climate change. The ways humans respond to climate change, either through adaptation or mitigation, have underappreciated, yet hugely impactful effects on infectious disease transmission, often in complex and sometimes nonintuitive ways. Thus, in addition to investigating the direct effects of climate changes on infectious diseases, it is critical to consider how human preventative measures and adaptations to climate change will alter the environments and hosts that support pathogens. Here, we consider the ways that human responses to climate change will likely impact disease risk in both positive and negative ways. We evaluate the evidence for these impacts based on the available data, and identify research directions needed to address climate change while minimizing externalities associated with infectious disease, especially for vulnerable communities. We identify several different human adaptations to climate change that are likely to affect infectious disease risk independently of the effects of climate change itself. We categorize these changes into adaptation strategies to secure access to water, food, and shelter, and mitigation strategies to decrease greenhouse gas emissions. We recognize that adaptation strategies are more likely to have infectious disease consequences for under-resourced communities, and call attention to the need for socio-ecological studies to connect human behavioral responses to climate change and their impacts on infectious disease. Understanding these effects is crucial as climate change intensifies and the global community builds momentum to slow these changes and reduce their impacts on human health, economic productivity, and political stability.

Evaluating infection risks in buses based on passengers' dynamic temporal and typical spatial scenarios: A case study of COVID-19

Conventional buses, as an indispensable part of the urban public transport system, impose cross-infection risks on passengers. To assess differential risks due to dynamic staying durations and locations, this study considered four spatial distributions (i = 1-4) and six temporal scenarios (j = 1-6) of buses. Based on field measurements and a risk assessment approach combining both short-range and room-scale effects, risks are evaluated properly. The results showed that temporal asynchrony between infected and susceptible individuals significantly affects disease transmission rates. The Control Case assumes that infected and susceptible individuals enter and leave synchronously. However, ignoring temporal asynchrony scenarios, i.e., the Control Case, resulted in overestimation (+30.7 % to +99.6 %) or underestimation (-15.2 % to -69.9 %) of the actual risk. Moreover, the relative difference ratios of room-scale risks between the Control Case and five temporal scenarios are impacted by ventilation. Short-range risk exists only if infected and susceptible individuals have temporal overlap on the bus. Considering temporal and spatial asynchrony, a more realistic total reproduction number (R) can be obtained. Subsequently, the total R was assessed under five temporal scenarios. On average, for the Control Case, the total R was estimated to be +27.3 % higher than j = 1, -9.3 % lower than j = 2, +12.8 % higher than j = 3, +33.0 % lower than j = 4, and + 77.6 % higher than j = 5. This implies the need for a combination of active prevention and real-time risk monitoring to enable rigid travel demand and control the spread of the epidemic.

Sustainable urban mobility: Flexible bus service network design in the post-pandemic era

The excessive traffic congestion in vehicles lowers the service quality of urban bus system, reduces the social distance of bus passengers, and thus, increases the spread speed of epidemics, such as coronavirus disease. In the post-pandemic era, it is one of the main concerns for the transportation agency to provide a sustainable urban bus service to balance the travel convenience in accessibility and the travel safety in social distance for bus passengers, which essentially reduces the in-vehicle passenger congestion or smooths the boarding-alighting unbalance of passengers. Incorporating the route choice behavior of passengers, this paper proposes a sustainable service network design strategy by selecting one subset of the stops to maximize the total passenger-distance (person × kilometers) with exogenously given loading factor and stop-spacing level, which can be captured by constrained non-linear programming model. The loading factor directly determines the in-vehicle social distance, and the stop-spacing level can efficiently reduce the ridership with short journey distance. Therefore, the sustainable service network design can be used to help the government minimize the spread of the virus while guaranteeing the service quality of transport patterns in the post-pandemic era. A real-world case study is adopted to illustrate the validity of the proposed scheme and model.

A comprehensive walkability evaluation system for promoting environmental benefits

Pedestrian-oriented urban strategies such as the Paris 15-minute City are needed to respond to the global boiling. Quantitative evaluation of pedestrian-oriented urban objectives is important for various cities, and in this paper, a walkability evaluation system for the advanced model is developed considering the characteristics of a large city. The system calculates the walkability of Seoul. The evaluation system uses the Betweenness index as a weight in the urban network analysis. Considering stations with a high betweenness in urban traffic is essential for evaluating a pedestrian-oriented metropolis. Our findings in this study are that the UNA index in WES is critical for transit-oriented, walkable cities. The large city needs to find the location for mobility hubs or stations to observe the last mile. Installing a mobility hub or station at a high-value location in the city center is functionally important. In a pedestrian-oriented city, citizens can walk and bike the last mile in a busy city center. Walkable cities can encourage active transport and ultimately create more sustainable and environmentally friendly transportation systems. This study offers valuable insights into pedestrian infrastructure, urban systems, and policies that promote green transportation.

Epidemic spreading on multi-layer networks with active nodes

Investigations on spreading dynamics based on complex networks have received widespread attention these years due to the COVID-19 epidemic, which are conducive to corresponding prevention policies. As for the COVID-19 epidemic itself, the latent time and mobile crowds are two important and inescapable factors that contribute to the significant prevalence. Focusing on these two factors, this paper systematically investigates the epidemic spreading in multiple spaces with mobile crowds. Specifically, we propose a SEIS (Susceptible-Exposed-Infected-Susceptible) model that considers the latent time based on a multi-layer network with active nodes which indicate the mobile crowds. The steady-state equations and epidemic threshold of the SEIS model are deduced and discussed. And by comprehensively discussing the key model parameters, we find that (1) due to the latent time, there is a "cumulative effect" on the infected, leading to the "peaks" or "shoulders" of the curves of the infected individuals, and the system can switch among three states with the relative parameter combinations changing; (2) the minimal mobile crowds can also cause the significant prevalence of the epidemic at the steady state, which is suggested by the zero-point phase change in the proportional curves of infected individuals. These results can provide a theoretical basis for formulating epidemic prevention policies.

Roboethics of tourism and hospitality industry: A systematic review

This study aims to give a comprehensive analysis of customers' acceptance and use of AI gadgets and its relevant ethical issues in the tourism and hospitality business in the era of the Internet of Things. Adopting a PRISMA methodology for Systematic Reviews and Meta-Analyses, the present research reviews how tourism and hospitality scholars have conducted research on AI technology in the field of tourism and the hospitality industry. Most of the journal articles related to AI issues published in Web of Science, ScienceDirect.com and the journal websites were considered in this review. The results of this research offer a better understanding of AI implementation with roboethics to investigate AI-related issues in the tourism and hospitality industry. In addition, it provides decision-makers in the hotel industry with practical references on service innovation, participation in the design of AI devices and AI device applications, meeting customer needs, and optimising customer experience. The theoretical implications and practical interpretations are further identified.

Attitudinal analysis of vaccination effects to lead endemic phases

To achieve endemic phases, repeated vaccinations are necessary. However, individuals may grapple with whether to get vaccinated due to potential side effects. When an individual is already immune due to previous infections or vaccinations, the perceived risk from vaccination is often less than the risk of infection. Yet, repeated rounds of vaccination can lead to avoidance, impeding the establishment of endemic phases. We explore this phenomenon using an individual-based Monte Carlo simulation, validating our findings with game theory. The Nash equilibrium encapsulates individuals' non-cooperative behavior, while the system's optimal value represents the societal benefits of altruistic cooperation. We define the difference between these as the price of anarchy. Our simulations reveal that the price of anarchy must fall below a threshold of 12.47 for endemic phases to be achieved in a steady state. This suggests that for a basic reproduction number of 10, a consistent vaccination rate greater than 89% is required. These findings offer new insights into vaccination-related decision-making and can inform effective strategies to tackle infectious diseases.

Surface-Functionalized Metal-Organic Frameworks for Binding Coronavirus Proteins

Since the outbreak of SARS-CoV-2, a multitude of strategies have been explored for the means of protection and shielding against virus particles: filtration equipment (PPE) has been widely used in daily life. In this work, we explore another approach in the form of deactivating coronavirus particles through selective binding onto the surface of metal-organic frameworks (MOFs) to further the fight against the transmission of respiratory viruses. MOFs are attractive materials in this regard, as their rich pore and surface chemistry can easily be modified on demand. The surfaces of three MOFs, UiO-66(Zr), UiO-66-NH2(Zr), and UiO-66-NO2(Zr), have been functionalized with repurposed antiviral agents, namely, folic acid, nystatin, and tenofovir, to enable specific interactions with the external spike protein of the SARS virus. Protein binding studies revealed that this surface modification significantly improved the binding affinity toward glycosylated and non-glycosylated proteins for all three MOFs. Additionally, the pores for the surface-functionalized MOFs can adsorb water, making them suitable for locally dehydrating microbial aerosols. Our findings highlight the immense potential of MOFs in deactivating respiratory coronaviruses to be better equipped to fight future pandemics.

Face masks while exercising trial (MERIT): a cross-over randomised controlled study

OBJECTIVES

Physical exertion is a high-risk activity for aerosol emission of respiratory pathogens. We aimed to determine the safety and tolerability of healthy young adults wearing different types of face mask during moderate-to-high intensity exercise.

DESIGN

Cross-over randomised controlled study, completed between June 2021 and January 2022.

PARTICIPANTS

Volunteers aged 18-35 years, who exercised regularly and had no significant pre-existing health conditions.

INTERVENTIONS

Comparison of wearing a surgical, cloth and filtering face piece (FFP3) mask to no mask during 4×15 min bouts of exercise. Exercise was running outdoors or indoor rowing at moderate-to-high intensity, with consistency of distance travelled between bouts confirmed using a smartphone application (Strava). Each participant completed each bout in random order.

OUTCOMES

The primary outcome was change in oxygen saturations. Secondary outcomes were change in heart rate, perceived impact of face mask wearing during exercise and willingness to wear a face mask for future exercise.

RESULTS

All 72 volunteers (mean age 23.9) completed the study. Changes in oxygen saturations did not exceed the prespecified non-inferiority margin (2% difference) with any mask type compared with no mask. At the end of exercise, the estimated average difference in oxygen saturations for cloth mask was -0.07% (95% CI -0.39% to 0.25%), for surgical 0.28% (-0.04% to 0.60%) and for FFP3 -0.21% (-0.53% to 0.11%). The corresponding estimated average difference in heart rate for cloth mask was -1.20 bpm (95% CI -4.56 to 2.15), for surgical 0.36 bpm (95% CI -3.01 to 3.73) and for FFP3 0.52 bpm (95% CI -2.85 to 3.89). Wearing a face mask caused additional symptoms such as breathlessness (n=13, 18%) and dizziness (n=7, 10%). 33 participants broadly supported face mask wearing during exercise, particularly indoors, but 22 were opposed.

CONCLUSION

This study adds to previous findings (mostly from non-randomised studies) that exercising at moderate-to-high intensity wearing a face mask appears to be safe in healthy, young adults.

TRIAL REGISTRATION NUMBER

NCT04932226.

Examining the causal relationship between bike-share and public transit in response to the COVID-19 pandemic

As urban transportation systems often face disruptive events, including natural and man-made disasters, the importance of resilience in the transportation sector has recently been on the rise. In particular, the worldwide spread of the COVID-19 pandemic resulted in a significant decrease in citizens' public transit use to avoid unnecessary physical contact with others. Accordingly, bike-share has been highlighted as one of the sustainable modes that can replace public transit and, thus, improve the overall resilience of the urban transportation systems in response to COVID-19. This study aims to examine the changes in causal relationships between bike-share and public transit throughout the COVID-19 pandemic in Seoul, Korea. We analyzed bike-share and public transit ridership from Jan 2018 to Dec 2020. We developed a weekly panel vector autoregressive (PVAR) model to identify the bike-transit relationships before and after the pandemic. Our results showed that COVID-19 weakens the competitive relationships between bike-share and bus transit and modal integration between bike-share and subway transit. This study also found that bus and subway transit were more competitive with each other after the outbreak of COVID-19. The study's findings suggest that bike-share can increase the overall resilience of the urban transportation system during the pandemic situation, particularly for those who rely on public transit for their mobility.

Transmission Dynamics and Effectiveness of Control Measures during COVID-19 Surge, Taiwan, April-August 2021

An unprecedented surge of COVID-19 cases in Taiwan in May 2021 led the government to implement strict nationwide control measures beginning May 15. During the surge, the government was able to bring the epidemic under control without a complete lockdown despite the cumulative case count reaching >14,400 and >780 deaths. We investigated the effectiveness of the public health and social measures instituted by the Taiwan government by quantifying the change in the effective reproduction number, which is a summary measure of the ability of the pathogen to spread through the population. The control measures that were instituted reduced the effective reproduction number from 2.0-3.3 to 0.6-0.7. This decrease was correlated with changes in mobility patterns in Taiwan, demonstrating that public compliance, active case finding, and contact tracing were effective measures in preventing further spread of the disease.

The Control of Metabolic CO2 in Public Transport as a Strategy to Reduce the Transmission of Respiratory Infectious Diseases

The global acceptance of the SARS-CoV-2 airborne transmission led to prevention measures based on quality control and air renewal. Among them, carbon dioxide (CO2) measurement has positioned itself as a cost-efficiency, reliable, and straightforward method to assess indoor air renewal indirectly. Through the control of CO2, it is possible to implement and validate the effectiveness of prevention measures to reduce the risk of contagion of respiratory diseases by aerosols. Thanks to the method scalability, CO2 measurement has become the gold standard for diagnosing air quality in shared spaces. Even though collective transport is considered one of the environments with the highest rate of COVID-19 propagation, little research has been done where the air inside vehicles is analyzed. This work explores the generation and accumulation of metabolic CO2 in a tramway (Zaragoza, Spain) operation. Importantly, we propose to use the indicator ppm/person as a basis for comparing environments under different conditions. Our study concludes with an experimental evaluation of the benefit of modifying some parameters of the Heating-Ventilation-Air conditioning (HVAC) system. The study of the particle retention efficiency of the implemented filters shows a poor air cleaning performance that, at present, can be counteracted by opening windows. Seeking a post-pandemic scenario, it will be crucial to seek strategies to improve air quality in public transport to prevent the transmission of infectious diseases.