Social-distancing fatigue: Evidence from real-time crowd-sourced traffic data

Social dilemma of nonpharmaceutical interventions: Determinants of dynamic compliance and behavioral shifts

In fighting infectious diseases posing a global health threat, ranging from influenza to Zika, nonpharmaceutical interventions (NPI), such as social distancing and face covering, remain mitigation measures public health can resort to. However, the success of NPI lies in sufficiently high levels of collective compliance, otherwise giving rise to recurrent infections that are not only driven by pathogen evolution but also changing vigilance in the population. Here, we show that compliance with each NPI measure can be highly dynamic and context-dependent during an ongoing epidemic, where individuals may prefer one to another or even do nothing, leading to intricate temporal switching behavior of NPI adoptions. By characterizing dynamic regimes through the perceived costs of NPI measures and their effectiveness in particular regarding face covering and social distancing, our work offers insights into overcoming barriers in NPI adoptions.

Variation in the Impact of New York on Pause on Traffic Congestion by Racialized Economic Segregation and Environmental Burden

During the 2019 coronavirus pandemic, stay-at-home policies such as New York's (NY) NY on Pause dramatically reduced traffic congestion. Despite high traffic burden in NY's environmental justice communities, this reduction has not been evaluated through an environmental justice lens-our objective in this analysis. We obtained census tract-level traffic congestion data from Google traffic maps hourly for 2018-2020. We defined congestion as the percent of streets in a census tract with heavy traffic (red- or maroon-color). We used the Index of Concentration at the Extremes (ICE) to measure racialized economic segregation and the CDC's Environmental Justice Index (EJI) as a measure of combined environmental, social, and chronic disease burden. We divided census tracts into quintiles of ICE and EJI and used linear mixed models stratified by ICE and EJI quintile in an interrupted time series design. Prior to NY on Pause, less marginalized and burdened census tracts (Q5) tended to have higher levels of traffic congestion; during NY on Pause, this trend reversed. For both ICE and EJI, more marginalized and burdened (Q1-Q2 vs. Q4-Q5) tracts had smaller absolute decreases in percent traffic congestion. For example, percent traffic congestion in ICE Q5 decreased by 7.8% (% change: -36.6%), but in Q1, it decreased by 4.2% (% change: -51.7%). NY on Pause, while protecting residents during COVID-19, may have resulted in inequitable reductions in traffic congestion. It is critical that such inequities are measured and acknowledged so that future policies to reduce traffic congestion and respond to pandemics can enhance equity.

Impact of traffic congestion on asthma-related hospital visits in major Texas cities

Asthma is one of the most prevalent chronic conditions in the United States and is particularly sensitive to environmental changes in urban areas. While it is known that traffic congestion contributes to increased vehicle emissions and poorer air quality, its direct association with asthma incidence has not been thoroughly explored. This study aimed to address this void by analyzing 148 city-level observations from 2016 to 2020 in Texas, using data from the Texas A&M Transportation Institute and Definitive Healthcare. We investigated the association between traffic congestion, measured by the travel time index, and annual city-level asthma hospital discharges, while adjusting for refinery productivity, minority groups, and education levels through multivariate regression. Our findings revealed a significant positive correlation between the travel time index and asthma visits, indicating that higher traffic congestion is associated with increased hospital visits for asthma. This finding remains consistent across different models, regardless of whether control variables are included. For the control variables, we found that higher refinery productivity was linked to elevated risks of asthma-related hospitalizations, aligning with previous research findings. Although correlations with Black or African American and Hispanic or Latino populations, as well as those with less than a high school education, were not statistically significant, a positive trend was observed. These results emphasize the impact of traffic congestion on asthma prevalence and the necessity for targeted public health interventions and urban planning strategies.

COVID-19 in college: Risk perception and planned protective behavior

Objective: The Theory of Planned Behavior has been applied to COVID-19 protective behaviors, but evidence suggests this theory may be less predictive over time and less valid in individualistic societies. The current study applied this theory among American college students as vaccines became available and added perceived risk. Participants: 242 undergraduate students at two universities. Methods: Participants completed an online survey and analyses were conducted using PROCESS. Results: Perceived risk was indirectly related to protective behavior via intentions which were significantly impacted by positive attitudes, descriptive norms, and perceived behavioral control. Conclusions: Even within an individualistic culture and when vaccines were becoming available, the Theory of Planned Behavior predicts protective behaviors. Including risk perception also furthers understanding of this theory by identifying one factor related to norms and perceived behavioral control. These results may inform the design of interventions designed to increase compliance with pandemic-related policies and other positive behaviors.

The paradox of pandemic mitigation? Moderating role of pandemic severity on the impact of social distancing policies: a cultural value perspective

BACKGROUND

Social distancing policies were of utmost importance during the early stages of the COVID-19 pandemic. These policies aimed to mitigate the severity of local outbreaks by altering public behavior. However, if the severity of the pandemic reduces, the impact of these policies on actual behavior may decrease. This study aims to examine, from a global perspective, whether the impact of social distancing policies on actual mobility is moderated by local pandemic severity and whether this moderating effect varies across cultural value contexts.

METHODS

We combined multiple publicly available global datasets for structural equation model analysis. 17,513 rows of data from 57 countries included in all databases were analyzed. Multilevel moderated moderation models were constructed to test the hypotheses.

RESULTS

More stringent policies in a region mean less regional mobility (β = -0.572, p < 0.001). However, the severity of local outbreaks negatively moderated this effect (β = -0.114, p < 0.001). When the pandemic was not severe, the influence of policy intensity on mobility weakened. Furthermore, based on Schwartz's cultural values theory, cultural values of autonomy (β = -0.109, p = 0.011), and egalitarianism (β = -0.108, p = 0.019) reinforced the moderating effect of pandemic severity. On the other hand, cultural values of embeddedness (β = 0.119, p = 0.006) and hierarchy (β = 0.096, p = 0.029) attenuated the moderating effect.

CONCLUSIONS

Social distancing policies aim to reduce the severity of local pandemics; however, the findings reveal that mitigating local pandemics may reduce their impact. Future policymakers should be alert to this phenomenon and introduce appropriate incentives to respond. The results also show that the moderating role of pandemic severity varies across cultures. When policies are promoted to deal with global crises, policymakers must seriously consider the resistance and potential incentives of cultural values.

Built environment factors moderate pandemic fatigue in social distance during the COVID-19 pandemic: A nationwide longitudinal study in the United States

Non-pharmaceutical interventions (NPIs) remain some of the most effective measures for coping with the ever-changing coronavirus disease 2019 (COVID-19) pandemic. Pandemic fatigue, which manifests as the declined willingness to follow the recommended protective behaviors (e.g., keeping social distance policies, wearing masks), has commanded increasing attention from researchers and policymakers after the prolonged NPIs and COVID-19 worldwide. However, long-term changes in pandemic fatigue are not well understood, especially amidst the ever-changing pandemic landscape. Built environment factors have been shown to positively affect mental and physical health, but it is still unclear whether built environments can moderate pandemic fatigue. In this study, we used Google mobility data to investigate longitudinal trends of pandemic fatigue in social distance since the onset of NPIs enforcement in the United States. The results indicated that pandemic fatigue continuously worsened over nearly two years of NPIs implementation, and a sharp increase occurred after the vaccination program began. Additionally, we detected a significant moderation effect of greenspace and urbanicity levels on pandemic fatigue. People living in areas with high levels of greenness or urbanicity experienced lower levels of pandemic fatigue. These findings not only shed new light on the effects of greenness and urbanicity on COVID-19 pandemic fatigue, but also provide evidence for developing more tailored and effective strategies to cope with pandemic fatigue.

Chain mediating effect of insomnia, depression, and anxiety on the relationship between nightmares and cognitive deficits in adolescents

BACKGROUND

The study explored the differences in nightmare, insomnia, depression, anxiety, and cognitive deficits among adolescents and the chain mediating effects of insomnia, depression, and anxiety on the relationship between nightmares and cognitive deficits in adolescents.

METHODS

An online survey was used to collect demographic data of 6014 adolescents and assess nightmare, insomnia, depression, anxiety, and cognitive deficits using the Chinese Version of Nightmare Distress Questionnaire, Insomnia Severity Index, Patient Health Questionnaire 9, Generalized Anxiety Disorder 7, and Perceived Deficits Questionnaire-Depression. Spearman correlation analysis and the SPSS function "PROCESS macro" were used for correlation and mediation analyses, respectively.

RESULTS

Female adolescents, senior high school, and poor academic performance had higher nightmare, insomnia, and cognitive deficit scores; those living in the city had higher depression and anxiety scores. Cognitive deficits were positively correlated with nightmares, insomnia, depression, and anxiety. Further, insomnia, depression, and anxiety had a chain mediating effect between nightmares and cognitive deficits in adolescents. Nightmares indirectly affect cognition deficits by affecting insomnia and then depression and anxiety symptoms.

LIMITATIONS

As this was a cross-sectional study, the causal relationship between the variables could not be determined. Moreover, reporting bias and volunteer bias might be present.

CONCLUSIONS

These findings suggest that clinicians should identify adolescents with frequent nightmares early and provide timely treatment to minimize negative outcomes and possibly limit the chronicity of nightmare disorder. It is significant to maintain the physical and mental health development of adolescents to reduce the risk of insomnia, depression, anxiety, and cognitive deficits.

Quantifying COVID-19 recovery process from a human mobility perspective: An intra-city study in Wuhan

The COVID-19 pandemic has brought huge challenges to sustainable urban and community development. Although some recovery signals and patterns have been uncovered, the intra-city recovery process remains underexploited. This study proposes a comprehensive approach to quantify COVID-19 recovery leveraging fine-grained human mobility records. Taking Wuhan, a typical COVID-19 affected megacity in China, as the study area, we identify accurate recovery phases and select appropriate recovery functions in a data-driven manner. We observe that recovery characteristics regarding duration, amplitude, and velocity exhibit notable differences among urban blocks. We also notice that the recovery process under a one-wave outbreak lasts at least 84 days and has an S-shaped form best fitted with four-parameter Logistic functions. More than half of the recovery variance can be well explained and estimated by common variables from auxiliary data, including population, economic level, and built environments. Our study serves as a valuable reference that supports data-driven recovery quantification for COVID-19 and other crises.

Does Pandemic Fatigue Prevent Farmers' Participation in the Rural Tourism Industry: A Comparative Study between Two Chinese Villages

Rural tourism is an important income generation method for farmers post-pandemic. However, few studies have focused on how pandemic fatigue has affected their willingness to participate in rural tourism development. We conducted a quasi-experiment to test these effects using data from two Chinese villages. Shanlian village, which was more severely affected by COVID-19, was the experimental group, while Huashu village was set as the control group. Our results reveal that both physical and mental fatigue hinder farmers' intention to engage in rural tourism. Further, there were significant interaction effects between physical and mental fatigue on the farmers' participation in rural tourism. For farmers with low physical fatigue, the higher their mental fatigue, the less willing they were to participate in rural development. Conversely, for the higher physical fatigue group, farmers with low levels of mental fatigue were still more willing to participate in rural tourism development. These findings reduce the current research gap concerning the relationship between pandemic fatigue and farmers' participation in rural tourism and indicate that practitioners and policymakers should consider farmers' fatigue management as an important factor for the sustainability of rural tourism during the ongoing COVID-19 crisis.

Green spaces, especially nearby forest, may reduce the SARS-CoV-2 infection rate: A nationwide study in the United States

The coronavirus pandemic is an ongoing global crisis that has profoundly harmed public health. Although studies found exposure to green spaces can provide multiple health benefits, the relationship between exposure to green spaces and the SARS-CoV-2 infection rate is unclear. This is a critical knowledge gap for research and practice. In this study, we examined the relationship between total green space, seven types of green space, and a year of SARS-CoV-2 infection data across 3,108 counties in the contiguous United States, after controlling for spatial autocorrelation and multiple types of covariates. First, we examined the association between total green space and SARS-CoV-2 infection rate. Next, we examined the association between different types of green space and SARS-CoV-2 infection rate. Then, we examined forest-infection rate association across five time periods and five urbanicity levels. Lastly, we examined the association between infection rate and population-weighted exposure to forest at varying buffer distances (100 m to 4 km). We found that total green space was negative associated with the SARS-CoV-2 infection rate. Furthermore, two forest variables (forest outside park and forest inside park) had the strongest negative association with the infection rate, while open space variables had mixed associations with the infection rate. Forest outside park was more effective than forest inside park. The optimal buffer distances associated with lowest infection rate are within 1,200 m for forest outside park and within 600 m for forest inside park. Altogether, the findings suggest that green spaces, especially nearby forest, may significantly mitigate risk of SARS-CoV-2 infection.

Quantifying diurnal changes in NO2 due to COVID-19 stay-at-home orders in New York City

Introduction

Policy responses to the COVID-19 pandemic, such as the NY on Pause stay-at-home order (March 22 - June 8, 2020), substantially reduced traffic and traffic-related air pollution (TRAP) in New York City (NYC). We evaluated the magnitude of TRAP decreases and examined the role of modifying factors such as weekend/weekday, road proximity, location, and time-of-day.

Methods

Hourly nitrogen dioxide (NO₂) concentrations from January 1, 2018 through June 8, 2020 were obtained from the Environmental Protection Agency's Air Quality System for all six hourly monitors in the NYC area. We used an interrupted time series design to determine the impact of NY on Pause on NO₂ concentrations, using a mixed effects model with random intercepts for monitor location, adjusted for meteorology and long-term trends. We evaluated effect modification through stratification.

Results

NO₂ concentrations decreased during NY on Pause by 19% (-3.2 ppb, 95% confidence interval [CI]: -3.5, -3.0), on average, compared to pre-Pause time trends. We found no evidence for modification by weekend/weekday, but greater decreases in NO₂ at non-roadside monitors and weak evidence for modification by location. For time-of-day, we found the largest decreases for 5 am (27%, -4.5 ppb, 95% CI: -5.7, -3.3) through 7 am (24%, -4.0 ppb, 95% CI: -5.2, -2.8), followed by 6 pm and 7 pm (22%, -3.7 ppb, 95% CI: -4.8, -2.6 and 22%, -4.8, -2.5, respectively), while the smallest decreases occurred at 11 pm and 1 am (both: 11%, -1.9 ppb, 95% CI: -3.1, -0.7).

Conclusion

NY on Pause's impact on TRAP varied greatly diurnally. Decreases during early morning and evening time periods are likely due to decreases in traffic. Our results may be useful for planning traffic policies that vary by time of day, such as congestion tolling policies.

Assessing Neighborhood-scale Traffic from Crowd-sensed Traffic Data: Findings from an Environmental Justice Community in New York City

BACKGROUND

The waterfront in the South Bronx in New York City is used industrially and harbors the Harlem River Yards (HRY). The HRY borders an environmental justice area, which includes a mixed-use area that is separated from a densely populated residential area by interstates. Recently, development of the HRY has expanded including the 2018 opening of a large online store warehouse.

OBJECTIVE

The goal of this study was to evaluate trends in traffic congestion nearby the HRY between 2017 to 2019.

METHODS

We analyzed one-hourly time series of crowd-sensed traffic congestion maps, both at the neighborhood scale and the road stretch level. Traffic radar measurements at two locations did not indicate bias in the crowd-sensed data over the study period, i.e., changed mappings between vehicle speed and the reported congestion.

RESULTS

In the mixed-use areas, traffic congestion increased significantly during all hours of the day, with greatest increases at night and in the morning. Congestion increased close to the entrances of the HRY and along routes used by pedestrians and bicyclists to access a nearby recreational area. In the residential area, congestion increased significantly from midnight to morning and was unchanged for the remainder of the day. On the interstates, congestion decreased during the daytime but increased at night.

CONCLUSIONS

Neighborhood-scale traffic congestion increased in mixed-use and residential areas in an environmental justice community. Our methods can be applied globally as long as crowd-sensed traffic data can be acquired. The data enable communities to advocate for mitigating measures.

Patterns of compliance with COVID-19 preventive behaviours: a latent class analysis of 20 000 UK adults

BACKGROUND

Governments have implemented a range of measures to tackle COVID-19, primarily focusing on changing citizens' behaviours in order to lower the transmission of the virus. Few studies have looked at the patterns of compliance with different measures within individuals: whether people comply with all measures or selectively choose some but not others. Such research is important for designing interventions to increase compliance.

METHODS

We used cross-sectional data from 20 947 UK adults in the COVID-19 Social Study collected from 17 November to 23 December 2020. Self-report compliance was assessed with six behaviours: mask wearing, hand washing, indoor household mixing, outdoor household mixing, social distancing and compliance with other guidelines. Patterns of compliance behaviour were identified using latent class analysis, and multinomial logistic regression was used to assess demographic, socioeconomic and personality predictors of behaviour patterns.

RESULTS

We selected a four-latent class solution. Most individuals reported similar levels of compliance across the six behaviour measures. High level of compliance was the modal response. Lower self-reported compliance was related to young age, high risk-taking behaviour, low confidence in government and low empathy, among other factors. Looking at individual behaviours, mask wearing had the highest level of compliance while compliance with social distancing was relatively low.

CONCLUSION

Results suggest that individuals choose to comply with all guidelines, rather than some but not others. Strategies to increase compliance should focus on increasing general motivations to comply alongside specifically encouraging social distancing.

The dynamical formation of ephemeral groups on networks and their effects on epidemics spreading

In network models of propagation processes, the individual, microscopic level perspective is the norm, with aggregations studied as possible outcomes. On the contrary, we adopted a mesoscale perspective with groups as the core element and in this sense we present a novel agent-group dynamic model of propagation in networks. In particular, we focus on ephemeral groups that dynamically form, create new links, and dissolve. The experiments simulated 160 model configurations and produced results describing cases of consecutive and non-consecutive dynamic grouping, bounded or unbounded in the number of repetitions. Results revealed the existence of complex dynamics and multiple behaviors. An efficiency metric is introduced to compare the different cases. A Null Model analysis disclosed a pattern in the difference between the group and random models, varying with the size of groups. Our findings indicate that a mesoscopic construct like the ephemeral group, based on assumptions about social behavior and absent any microscopic level change, could produce and describe complex propagation dynamics. A conclusion is that agent-group dynamic models may represent a powerful approach for modelers and a promising new direction for future research in models of coevolution between propagation and behavior in society.

Method for Identifying the Traffic Congestion Situation of the Main Road in Cold-Climate Cities Based on the Clustering Analysis Algorithm

Congestion has become a common urban disease in countries worldwide, with the acceleration of urbanization. The connotation of the congestion situation is expanded to describe, in detail, the traffic operation status and change characteristics of the main road in cold-climate cities and to provide more comprehensive identification methods and theoretical basis for cold-climate cities. It includes two aspects: the state and trend. A method to distinguish the traffic congestion state level and trend type of the main road in cold-climate cities is proposed on the basis of density clustering, hierarchical clustering, and fuzzy C-means clustering, and the temporal and spatial congestion characteristics of the main roads of cold-climate cities are explored. Research results show that we can divide the traffic congestion state into three levels: unblocked, slow, and congested. We can also divide the congestion trend into three types: aggravation, relief, and stability. This method is suitable for the identification of the main road’s congestion situation in cold-climate cities and can satisfy the spatiotemporal self-correlation and difference test. The temporal and spatial distribution rules of congestion are different under different road conditions, the volatility of the congestion degree and change speed on snowy and icy pavements, and the instability of congestion spatial aggregation are more serious than that on non-snowy and non-icy pavements. The research results are more comprehensive and objective than the existing methods.