Living in a pandemic: changes in mobility routines, social activity and adherence to COVID-19 protective measures

Mixing individual and collective behaviors to predict out-of-routine mobility

Predicting human displacements is crucial for addressing various societal challenges, including urban design, traffic congestion, epidemic management, and migration dynamics. While predictive models like deep learning and Markov models offer insights into individual mobility, they often struggle with out-of-routine behaviors. Our study introduces an approach that dynamically integrates individual and collective mobility behaviors, leveraging collective intelligence to enhance prediction accuracy. Evaluating the model on millions of privacy-preserving trajectories across five US cities, we demonstrate its superior performance in predicting out-of-routine mobility, surpassing even advanced deep learning methods. The spatial analysis highlights the model's effectiveness near urban areas with a high density of points of interest, where collective behaviors strongly influence mobility. During disruptive events like the COVID-19 pandemic, our model retains predictive capabilities, unlike individual-based models. By bridging the gap between individual and collective behaviors, our approach offers transparent and accurate predictions, which are crucial for addressing contemporary mobility challenges.

Nontrivial epidemic dynamics induced by information-driven awareness-activity-resource coevolution

During epidemic outbreaks, information dissemination plays a pivotal role in shaping individual perceptions, which in turn influence contact behavior and resource acquisition, collectively determining infection risk. To capture this intricate interplay, we propose a comprehensive coevolutionary dynamics model that integrates information, awareness, activity, resources, and epidemic within a multiplex network framework. Through the development of a theoretical analysis coupled with extensive numerical verifications, we uncover the nonmonotonic effects of information dissemination on epidemic dynamics. Paradoxically, excessive information flow can intensify resource competition among individuals, leading to inefficient allocation and ultimately exacerbating the epidemic. Our findings highlight the importance of optimized resource allocation, showing that moderately prioritizing aware individuals with resources can effectively reduce infection rates, especially as information dissemination increases. Additionally, we explore the optimal balance between information dissemination and resource allocation, emphasizing its strong dependence on resource availability, while activity frequency experts a comparatively minor impact. This study advances the modeling of epidemic dynamics, providing valuable insights and practical strategies for effective epidemic management and control.

Socioeconomic disparities in mobility behavior during the COVID-19 pandemic in developing countries

Mobile phone data have played a key role in quantifying human mobility during the COVID-19 pandemic. Existing studies on mobility patterns have primarily focused on regional aggregates in high-income countries, obfuscating the accentuated impact of the pandemic on the most vulnerable populations. Leveraging geolocation data from mobile-phone users and population census for 6 middle-income countries across 3 continents between March and December 2020, we uncovered common disparities in the behavioral response to the pandemic across socioeconomic groups. Users living in low-wealth neighborhoods were less likely to respond by self-isolating, relocating to rural areas, or refraining from commuting to work. The gap in the behavioral responses between socioeconomic groups persisted during the entire observation period. Among users living in low-wealth neighborhoods, those who commute to work in high-wealth neighborhoods pre-pandemic were particularly at risk of experiencing economic stress, facing both the reduction in economic activity in the high-wealth neighborhood and being more likely to be affected by public transport closures due to their longer commute distances. While confinement policies were predominantly country-wide, these results suggest that, when data to identify vulnerable individuals are not readily available, GPS-based analytics could help design targeted place-based policies to aid the most vulnerable.

Supplementary Information

The online version contains supplementary material available at 10.1140/epjds/s13688-025-00532-2.

Behaviour-based dependency networks between places shape urban economic resilience

Disruptions, such as closures of businesses during pandemics, not only affect businesses and amenities directly but also influence how people move, spreading the impact to other businesses and increasing the overall economic shock. However, it is unclear how much businesses depend on each other during disruptions. Leveraging human mobility data and same-day visits in five US cities, we quantify dependencies between points of interest encompassing businesses, stores and amenities. We find that dependency networks computed from human mobility exhibit significantly higher rates of long-distance connections and biases towards specific pairs of point-of-interest categories. We show that using behaviour-based dependency relationships improves the predictability of business resilience during shocks by around 40% compared with distance-based models, and that neglecting behaviour-based dependencies can lead to underestimation of the spatial cascades of disruptions. Our findings underscore the importance of measuring complex relationships in patterns of human mobility to foster urban economic resilience to shocks.

Impact of the COVID-19 Pandemic on the Incidence, Etiology, Demographics, and Treatment of Craniomaxillofacial Trauma

OBJECTIVE

To compare the incidence, etiology, demographics, and treatment of craniomaxillofacial (CMF) trauma before, during, and after COVID-19.

STUDY DESIGN

Retrospective cohort.

SETTING

Eighty-three health care organizations across the United States.

METHODS

The TriNetX Research Network identified 77,977,880 patients during 2017 to 2022. CMF fractures and soft tissue injuries during March to August of each year, aligning with the 2020 pandemic lockdown, were analyzed.

RESULTS

In 2020, compared to immediately prepandemic in 2019, there were significant reductions of -17.5% in facial fractures and -19.0% in soft tissue injuries (P < .001). Conversely, in 2021, both injury types increased by +16.7% and +16.3%, respectively, compared to 2020 (P < .001). Changes in injury mechanisms in 2020 included significant decreases in athletic injuries (-57.6%), falls (-16.8%), assaults (-15.5%), motor vehicle collisions (-8.7%), and pedestrian accidents (-6.9%) (P < .01), while off-road vehicle (+48.4%), bicycle (+16.2%), and motorcycle (+8.9%) accidents increased (P < .01). The 10- to 14- and 5- to 9-year-old age groups experienced the most substantial reductions in facial fractures (-39.7% and -29.9%, respectively) and soft tissue injuries (-29.2% and -28.3%, respectively) in 2020 compared to 2019 (P < .001). Operative management of fractures and soft tissue injuries dropped by -20.3% and -12.4%, respectively, in 2020 versus 2019, and then rebounded with +15.8% and +14.6% increases in 2021 compared to 2020 (P < .001). In 2022, compared to prepandemic rates of 2019, there were fewer patients with facial fractures (-2.8%), soft tissue injuries (-4.5%), and operative repairs (-6.9% for fractures, -1.2% for soft tissue injuries) (P < .03).

CONCLUSION

CMF trauma decreased in 2020, with subsequent years showing a rebound to levels slightly below those immediately prior to pandemic onset. Changes in etiology, demographics, and treatment highlight the complex dynamics of traumatic injuries during periods of societal disruption.

Resilience of mobility network to dynamic population response across COVID-19 interventions: Evidences from Chile

The COVID-19 pandemic highlighted the importance of non-traditional data sources, such as mobile phone data, to inform effective public health interventions and monitor adherence to such measures. Previous studies showed how socioeconomic characteristics shaped population response during restrictions and how repeated interventions eroded adherence over time. Less is known about how different population strata changed their response to repeated interventions and how this impacted the resulting mobility network. We study population response during the first and second infection waves of the COVID-19 pandemic in Chile and Spain. Via spatial lag and regression models, we investigate the adherence to mobility interventions at the municipality level in Chile, highlighting the significant role of wealth, labor structure, COVID-19 incidence, and network metrics characterizing business-as-usual municipality connectivity in shaping mobility changes during the two waves. We assess network structural similarities in the two periods by defining mobility hotspots and traveling probabilities in the two countries. As a proof of concept, we simulate and compare outcomes of an epidemic diffusion occurring in the two waves. While differences exist between factors associated with mobility reduction across waves in Chile, underscoring the dynamic nature of population response, our analysis reveals the resilience of the mobility network across the two waves. We test the robustness of our findings recovering similar results for Spain. Finally, epidemic modeling suggests that historical mobility data from past waves can be leveraged to inform future disease spatial invasion models in repeated interventions. This study highlights the value of historical mobile phone data for building pandemic preparedness and lessens the need for real-time data streams for risk assessment and outbreak response. Our work provides valuable insights into the complex interplay of factors driving mobility across repeated interventions, aiding in developing targeted mitigation strategies.

Socioeconomic correlates of urban mobility trends in two Australian cities during transitional periods of the COVID-19 pandemic

During the COVID-19 pandemic, both government-mandated lockdowns and discretionary changes in behaviour combined to produce dramatic and abrupt changes to human mobility patterns. To understand the socioeconomic determinants of intervention compliance and discretionary behavioural responses to epidemic threats, we investigate whether changes in human mobility showed a systematic variation by socioeconomic status during two distinct periods of the COVID-19 pandemic in Australia. We analyse mobility data from two major urban centres and compare the trends during mandated stay-at-home policies and after the full relaxation of nonpharmaceutical interventions, which coincided with a large surge of COVID-19 cases. We analyse data aggregated from de-identified global positioning system trajectories, collated from providers of mobile phone applications and aggregated to small spatial regions. Our results demonstrate systematic decreases in mobility relative to the pre-pandemic baseline with the index of education and occupation, for both pandemic periods. On the other hand, the index of economic resources was not correlated with mobility changes. This result contrasts with observations from other national contexts, where reductions in mobility typically increased strongly with indicators of wealth. Our analysis suggests that economic support policies in place during the initial period of stay-at-home orders in Australia facilitated broad reductions in mobility across the economic spectrum.

Adaptive human behaviour modulates the impact of immune life history and vaccination on long-term epidemic dynamics

The multiple immunity responses exhibited in the population and co-circulating variants documented during pandemics show a high potential to generate diverse long-term epidemiological scenarios. Transmission variability, immune uncertainties and human behaviour are crucial features for the predictability and implementation of effective mitigation strategies. Nonetheless, the effects of individual health incentives on disease dynamics are not well understood. We use a behavioural-immuno-epidemiological model to study the joint evolution of human behaviour and epidemic dynamics for different immunity scenarios. Our results reveal a trade-off between the individuals' immunity levels and the behavioural responses produced. We find that adaptive human behaviour can avoid dynamical resonance by avoiding large outbreaks, producing subsequent uniform outbreaks. Our forward-looking behaviour model shows an optimal planning horizon that minimizes the epidemic burden by balancing the individual risk-benefit trade-off. We find that adaptive human behaviour can compensate for differential immunity levels, equalizing the epidemic dynamics for scenarios with diverse underlying immunity landscapes. Our model can adequately capture complex empirical behavioural dynamics observed during pandemics. We tested our model for different US states during the COVID-19 pandemic. Finally, we explored extensions of our modelling framework that incorporate the effects of lockdowns, the emergence of a novel variant, prosocial attitudes and pandemic fatigue.

Burden changes in notifiable infectious diseases in Taiwan during the COVID-19 pandemic

Background

This study aimed to assess the impact of the COVID-19 pandemic on the disease burden of Taiwan's notifiable infectious diseases (NIDs). We compared disease burdens between the pandemic and pre-pandemic year of 2020 (with non-pharmaceutical interventions (NPIs)) and 2010 (without NPIs), respectively, to understand the overall pandemic impact on NIDs in Taiwan.

Methods

Forty-three national NIDs were analyzed using the Statistics of Communicable Diseases and Surveillance Report by estimating the premature death and disability via different transmission categories, sex, and age groups. The study evaluated the impact of diseases by assessing the years lost due to death (YLLs), the duration of living with disability (YLDs), and the overall disability-adjusted life years (DALYs) by measuring both the severity of the illness and its duration.

Results

Taiwan recorded 1,577 (2010) and 1,260 (2020) DALYs per million population and lost 43 NIDs, decreasing 317 DALYs per million population. Tuberculosis, HIV/AIDS and acute hepatitis B/D were the leading causes of DALYs, accounting for 89% (2010) and 77% (2020).

Conclusion

Overall, this study provided the first insight of changes in disease burdens in NIDs between pre- and post-COVID-19 based on a nationwide viewpoint for further preventive measures and interventions to be focused on specific diseases by associated health administrations and policies.

The exciting potential and daunting challenge of using GPS human-mobility data for epidemic modeling

Large-scale GPS location datasets hold immense potential for measuring human mobility and interpersonal contact, both of which are essential for data-driven epidemiology. However, despite their potential and widespread adoption during the COVID-19 pandemic, there are several challenges with these data that raise concerns regarding the validity and robustness of its applications. Here we outline two types of challenges-some related to accessing and processing these data, and some related to data quality-and propose several research directions to address them moving forward.

Temporal clustering of social interactions trades-off disease spreading and knowledge diffusion

Non-pharmaceutical measures such as preventive quarantines, remote working, school and workplace closures, lockdowns, etc. have shown effectiveness from an epidemic control perspective; however, they have also significant negative consequences on social life and relationships, work routines and community engagement. In particular, complex ideas, work and school collaborations, innovative discoveries and resilient norms formation and maintenance, which often require face-to-face interactions of two or more parties to be developed and synergically coordinated, are particularly affected. In this study, we propose an alternative hybrid solution that balances the slowdown of epidemic diffusion with the preservation of face-to-face interactions, that we test simulating a disease and a knowledge spreading simultaneously on a network of contacts. Our approach involves a two-step partitioning of the population. First, we tune the level of node clustering, creating 'social bubbles' with increased contacts within each bubble and fewer outside, while maintaining the average number of contacts in each network. Second, we tune the level of temporal clustering by pairing, for a certain time interval, nodes from specific social bubbles. Our results demonstrate that a hybrid approach can achieve better trade-offs between epidemic control and complex knowledge diffusion. The versatility of our model enables tuning and refining clustering levels to optimally achieve the desired trade-off, based on the potentially changing characteristics of a disease or knowledge diffusion process.

Genomic surveillance reveals dynamic shifts in the connectivity of COVID-19 epidemics

The maturation of genomic surveillance in the past decade has enabled tracking of the emergence and spread of epidemics at an unprecedented level. During the COVID-19 pandemic, for example, genomic data revealed that local epidemics varied considerably in the frequency of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) lineage importation and persistence, likely due to a combination of COVID-19 restrictions and changing connectivity. Here, we show that local COVID-19 epidemics are driven by regional transmission, including across international boundaries, but can become increasingly connected to distant locations following the relaxation of public health interventions. By integrating genomic, mobility, and epidemiological data, we find abundant transmission occurring between both adjacent and distant locations, supported by dynamic mobility patterns. We find that changing connectivity significantly influences local COVID-19 incidence. Our findings demonstrate a complex meaning of "local" when investigating connected epidemics and emphasize the importance of collaborative interventions for pandemic prevention and mitigation.

A Process Evaluation of Intervention Delivery for a Cancer Survivorship Rehabilitation Clinical Trial Conducted during the COVID-19 Pandemic

The purpose of the present study was to conduct a process evaluation of intervention delivery for a randomized controlled trial (RCT) conducted during the COVID-19 pandemic (NCT03915548). The RCT tested the effects of a telephone-delivered behavioral intervention on changes in breast cancer survivors' satisfaction with social roles and activities, as compared to an attention control condition. This process evaluation examined (a) fidelity monitoring scores; (b) participants' perceived benefit ratings for gaining confidence, reducing distress, adjusting habits and routines, setting goals, and increasing exercise; and (c) field notes, email communications, and transcripts of coach supervision and debriefing sessions. The behavioral and attention control conditions were delivered with a high degree of fidelity (global quality rating score for the BA/PS condition was M = 4.6 (SD = 0.6) and M = 4.9 (SD = 0.3) for the attention control condition, where "5" is the highest rating). The behavioral intervention participants perceived greater benefits than the control participants pertaining to goal setting, t(248) = 5.73, p = <0.0001, adjusting habits and routines, t(248) = 2.94, p = 0.0036, and increasing exercise, t(248) = 4.66, p = <0.0001. Moreover, coaches' perceptions regarding the behavioral intervention's therapeutic aspects aligned with the study's a priori conceptual model including the use of a structured process to set small, observable goals and facilitate the independent use of problem-solving skills. However, coaches also noted that aspects of the attention control condition, including the perceived relevance of the educational content and opportunities for social support, may have made it more therapeutically potent than intended. The pandemic may have affected the activity goals behavioral intervention participants could set as well as augmented the relevancy of social support provided in both conditions.

[Factors associated with adherence to COVID-19 preventive behaviors in ELSA-Brasil participants]

The aim of the study is to assess the adherence to recommended prevention measures during the COVID-19 pandemic and to investigate the factors associated with this adherence in the adult population. This study has a cross-sectional design and used data from the complementary study Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) - COVID, conducted from 2020 to 2021. Data were analyzed using the chi-square test and multinomial logistic regression. The sample consisted of 5,440 participants. The preventive measure with the highest adherence was the use of a face mask (95.5%). There was greater adherence by females and lower chance of adherence by white people, by those who consume alcoholic beverages, people who were retired, as well as for those who live alone or who have family members who did not follow the recommendations to stay at home. Greater adherence to preventive behaviors was observed in only one third of the participating population, which demonstrates that there was a need for greater awareness of the risks in specific populations. The findings contribute to improving our understanding about health promotion and COVID-19 prevention.

The limits of human mobility traces to predict the spread of COVID-19: A transfer entropy approach

Mobile phone data have been widely used to model the spread of COVID-19; however, quantifying and comparing their predictive value across different settings is challenging. Their quality is affected by various factors and their relationship with epidemiological indicators varies over time. Here, we adopt a model-free approach based on transfer entropy to quantify the relationship between mobile phone-derived mobility metrics and COVID-19 cases and deaths in more than 200 European subnational regions. Using multiple data sources over a one-year period, we found that past knowledge of mobility does not systematically provide statistically significant information on COVID-19 spread. Our approach allows us to determine the best metric for predicting disease incidence in a particular location, at different spatial scales. Additionally, we identify geographic and demographic factors, such as users' coverage and commuting patterns, that explain the (non)observed relationship between mobility and epidemic patterns. Our work provides epidemiologists and public health officials with a general-not limited to COVID-19-framework to evaluate the usefulness of human mobility data in responding to epidemics.

A Global Feature-Rich Network Dataset of Cities and Dashboard for Comprehensive Urban Analyses

Urban network analytics has become an essential tool for understanding and modeling the intricate complexity of cities. We introduce the Urbanity data repository to nurture this growing research field, offering a comprehensive, open spatial network resource spanning 50 major cities in 29 countries worldwide. Our workflow enhances OpenStreetMap networks with 40 + high-resolution indicators from open global sources such as street view imagery, building morphology, urban population, and points of interest, catering to a diverse range of applications across multiple fields. We extract streetscape semantic features from more than four million street view images using computer vision. The dataset's strength lies in its thorough processing and validation at every stage, ensuring data quality and consistency through automated and manual checks. Accompanying the dataset is an interactive, web-based dashboard we developed which facilitates data access to even non-technical stakeholders. Urbanity aids various GeoAI and city comparative analyses, underscoring the growing importance of urban network analytics research.

Investigating the effects of vaccine on COVID-19 disease propagation using a Bayesian approach

The causal impact of COVID-19 vaccine coverage on effective reproduction number R(t) under the disease control measures in the real-world scenario is understudied, making the optimal reopening strategy (e.g., when and which control measures are supposed to be conducted) during the recovery phase difficult to design. In this study, we examine the demographic heterogeneity and time variation of the vaccine effect on disease propagation based on the Bayesian structural time series analysis. Furthermore, we explore the role of non-pharmaceutical interventions (NPIs) and the entrance of the Delta variant of COVID-19 in the vaccine effect for U.S. counties. The analysis highlights several important findings: First, vaccine effects vary among the age-specific population and population densities. The vaccine effect for areas with high population density or core airport hubs is 2 times higher than for areas with low population density. Besides, areas with more older people need a high vaccine coverage to help them against the more contagious variants (e.g., the Delta variant). Second, the business restriction policy and mask requirement are more effective in preventing COVID-19 infections than other NPI measures (e.g., bar closure, gather ban, and restaurant restrictions) for areas with high population density and core airport hubs. Furthermore, the mask requirement consistently amplifies the vaccine effects against disease propagation after the presence of contagious variants. Third, areas with a high percentage of older people are suggested to postpone relaxing the restaurant restriction or gather ban since they amplify the vaccine effect against disease infections. Such empirical insights assist recovery phases of the pandemic in designing more efficient reopening strategies, vaccine prioritization, and allocation policies.

Future directions in human mobility science

We provide a brief review of human mobility science and present three key areas where we expect to see substantial advancements. We start from the mind and discuss the need to better understand how spatial cognition shapes mobility patterns. We then move to societies and argue the importance of better understanding new forms of transportation. We conclude by discussing how algorithms shape mobility behavior and provide useful tools for modelers. Finally, we discuss how progress on these research directions may help us address some of the challenges our society faces today.

Who changed and who maintained their urban bike-sharing mobility after the COVID-19 outbreak? A within-subjects study

The COVID-19 pandemic has remarkably impacted urban mobility. All non-essential movements were restricted in Valencia (Spain) to contain the virus. Thus, the transport usage patterns of Valencia's bike-sharing system (BSS) users changed during this emergency situation. The primary objective of this study was to analyse the behaviour patterns of BSS users in Valencia before and after the COVID-19 outbreak, specifically those who maintained or changed their transport routines. A within-subjects comparison design was developed using a group of BSS users before and after the onset of the pandemic. Data mining techniques were used on a sample of 4355 regular users and 25 variables were calculated to classify users by self-organising maps analysis. The results show a significant reduction (40 %) in BSS movements after the outbreak during the entire post-outbreak year. There was some recovery during the rest of 2020; however, this has yet to reach the pre-pandemic levels, with variations observed based on the activities performed in different areas of the city. Of the users, 63 % changed their BSS use patterns after the onset of the pandemic (LEAVE group), while 37 % maintained their patterns (REMAIN group). The user profile of the REMAIN group was characterised by a general reduction of approximately 35 % of journeys during 2020, with a slight increase in morning movements compared to those made in the evening. These users also presented an equivalent number of cycling days to those of the previous year, reduced the number of connections and increased the network's density and the travelling speed. These results can be useful in estimating the percentage of people who do not vary their usual behaviour during emergencies. Finally, several policy implications are outlined based on the findings.

Crime, inequality and public health: a survey of emerging trends in urban data science

Urban agglomerations are constantly and rapidly evolving ecosystems, with globalization and increasing urbanization posing new challenges in sustainable urban development well summarized in the United Nations' Sustainable Development Goals (SDGs). The advent of the digital age generated by modern alternative data sources provides new tools to tackle these challenges with spatio-temporal scales that were previously unavailable with census statistics. In this review, we present how new digital data sources are employed to provide data-driven insights to study and track (i) urban crime and public safety; (ii) socioeconomic inequalities and segregation; and (iii) public health, with a particular focus on the city scale.

Measuring human mobility in times of trouble: an investigation of the mobility of European populations during COVID-19 using big data

Spatial mobility is a distinctive feature of human history and has important repercussions in many aspects of societies. Spatial mobility has always been a subject of interest in many disciplines, even if only mobility observable from traditional sources, namely migration (internal and international) and more recently commuting, is generally studied. However, it is the other forms of mobility, that is, the temporary forms of mobility, that most interest today's societies and, thanks to new data sources, can now be observed and measured. This contribution provides an empirical and data-driven reflection on human mobility during the COVID pandemic crisis. The paper has two main aims: (a) to develop a new index for measuring the attrition in mobility due to the restrictions adopted by governments in order to contain the spread of COVID-19. The robustness of the proposed index is checked by comparing it with the Oxford Stringency Index. The second goal is (b) to test if and how digital footprints (Google data in our case) can be used to measure human mobility. The study considers Italy and all the other European countries. The results show, on the one hand, that the Mobility Restriction Index (MRI) works quite well and, on the other, the sensitivity, in the short term, of human mobility to exogenous shocks and intervention policies; however, the results also show an inner tendency, in the middle term, to return to previous behaviours.

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.

Behavioral changes during the COVID-19 pandemic decreased income diversity of urban encounters

Diversity of physical encounters in urban environments is known to spur economic productivity while also fostering social capital. However, mobility restrictions during the pandemic have forced people to reduce urban encounters, raising questions about the social implications of behavioral changes. In this paper, we study how individual income diversity of urban encounters changed during the pandemic, using a large-scale, privacy-enhanced mobility dataset of more than one million anonymized mobile phone users in Boston, Dallas, Los Angeles, and Seattle, across three years spanning before and during the pandemic. We find that the diversity of urban encounters has substantially decreased (by 15% to 30%) during the pandemic and has persisted through late 2021, even though aggregated mobility metrics have recovered to pre-pandemic levels. Counterfactual analyses show that behavioral changes including lower willingness to explore new places further decreased the diversity of encounters in the long term. Our findings provide implications for managing the trade-off between the stringency of COVID-19 policies and the diversity of urban encounters as we move beyond the pandemic.

A dataset to assess mobility changes in Chile following local quarantines

Fighting the COVID-19 pandemic, most countries have implemented non-pharmaceutical interventions like wearing masks, physical distancing, lockdown, and travel restrictions. Because of their economic and logistical effects, tracking mobility changes during quarantines is crucial in assessing their efficacy and predicting the virus spread. Unlike many other heavily affected countries, Chile implemented quarantines at a more localized level, shutting down small administrative zones, rather than the whole country or large regions. Given the non-obvious effects of these localized quarantines, tracking mobility becomes even more critical in Chile. To assess the impact on human mobility of the localized quarantines, we analyze a mobile phone dataset made available by Telefónica Chile, which comprises 31 billion eXtended Detail Records and 5.4 million users covering the period February 26th to September 20th, 2020. From these records, we derive three epidemiologically relevant metrics describing the mobility within and between comunas. The datasets made available may be useful to understand the effect of localized quarantines in containing the COVID-19 pandemic.

Generating mobility networks with generative adversarial networks

The increasingly crucial role of human displacements in complex societal phenomena, such as traffic congestion, segregation, and the diffusion of epidemics, is attracting the interest of scientists from several disciplines. In this article, we address mobility network generation, i.e., generating a city's entire mobility network, a weighted directed graph in which nodes are geographic locations and weighted edges represent people's movements between those locations, thus describing the entire mobility set flows within a city. Our solution is MoGAN, a model based on Generative Adversarial Networks (GANs) to generate realistic mobility networks. We conduct extensive experiments on public datasets of bike and taxi rides to show that MoGAN outperforms the classical Gravity and Radiation models regarding the realism of the generated networks. Our model can be used for data augmentation and performing simulations and what-if analysis.

Non-pharmaceutical interventions and urban vehicle mobility in Seoul during the COVID-19 pandemic

Non-pharmaceutical interventions to control human mobility are important in preventing COVID-19 transmission. These interventions must also help effectively control the urban mobility of vehicles, which can be a safer travel mode during the pandemic, at any time and place. However, few studies have identified the effectiveness of vehicle mobility in terms of time and place. This study demonstrates the effectiveness of non-pharmaceutical interventions at both local and national levels on intra- and inter-urban vehicle mobility by time of day in Seoul, South Korea, by applying the autoregressive integrated moving average with exogenous variables. The study found that social distancing measures at the national level were effective for intra-urban vehicle mobility, especially at night-time, but not for inter-urban mobility. Information provision with emergency text messages by cell phone was effective in reducing vehicle mobility in daytime and night-time, but not during morning peak hours. At the local level, both restrictions on late-night transit operations and stricter social distancing measures were mostly significant in reducing night-time mobility only in intra-urban areas. The study also indicates when (what time of the day), where (which area within the city), and which combination strategy could be more effective in containing urban vehicle mobility. This study recommends that restrictions on human mobility should also be extended to vehicle mobility, especially in inter-urban areas and during morning peak hours, by systematically designing diverse non-pharmaceutical interventions.

Human mobility variations in response to restriction policies during the COVID-19 pandemic: An analysis from the Virus Watch community cohort in England, UK

Objective

Since the outbreak of COVID-19, public health and social measures to contain its transmission (e.g., social distancing and lockdowns) have dramatically changed people's lives in rural and urban areas globally. To facilitate future management of the pandemic, it is important to understand how different socio-demographic groups adhere to such demands. This study aims to evaluate the influences of restriction policies on human mobility variations associated with socio-demographic groups in England, UK.

Methods

Using mobile phone global positioning system (GPS) trajectory data, we measured variations in human mobility across socio-demographic groups during different restriction periods from Oct 14, 2020 to Sep 15, 2021. The six restriction periods which varied in degree of mobility restriction policies, denoted as "Three-tier Restriction," "Second National Lockdown," "Four-tier Restriction," "Third National Lockdown," "Steps out of Lockdown," and "Post-restriction," respectively. Individual human mobility was measured with respect to the time period people stayed at home, visited places outside the home, and traveled long distances. We compared these indicators across the six restriction periods and across socio-demographic groups.

Results

All human mobility indicators significantly differed across the six restriction periods, and the influences of restriction policies on individual mobility behaviors are correlated with socio-demographic groups. In particular, influences relating to mobility behaviors are stronger in younger and low-income groups in the second and third national lockdowns.

Conclusions

This study enhances our understanding of the influences of COVID-19 pandemic restriction policies on human mobility behaviors within different social groups in England. The findings can be usefully extended to support policy-making by investigating human mobility and differences in policy effects across not only age and income groups, but also across geographical regions.

Differential personality change earlier and later in the coronavirus pandemic in a longitudinal sample of adults in the United States

Five-factor model personality traits (neuroticism, extraversion, openness, agreeableness, conscientiousness) are thought to be relatively impervious to environmental demands in adulthood. The coronavirus pandemic is an unprecedented opportunity to examine whether personality changed during a stressful global event. Surprisingly, two previous studies found that neuroticism decreased early in the pandemic, whereas there was less evidence for change in the other four traits during this period. The present research used longitudinal assessments of personality from the Understanding America Study (N = 7,109; 18,623 assessments) to examine personality changes relatively earlier (2020) and later (2021–2022) in the pandemic compared to pre-pandemic levels. Replicating the two previous studies, neuroticism declined very slightly in 2020 compared to pre-pandemic levels; there were no changes in the other four traits. When personality was measured in 2021–2022, however, there was no significant change in neuroticism compared to pre-pandemic levels, but there were significant small declines in extraversion, openness, agreeableness, and conscientiousness. The changes were about one-tenth of a standard deviation, which is equivalent to about one decade of normative personality change. These changes were moderated by age and Hispanic/Latino ethnicity, but not race or education. Strikingly, younger adults showed disrupted maturity in that they increased in neuroticism and declined in agreeableness and conscientiousness. Current evidence suggests the slight decrease in neuroticism early in the pandemic was short-lived and detrimental changes in the other traits emerged over time. If these changes are enduring, this evidence suggests population-wide stressful events can slightly bend the trajectory of personality, especially in younger adults.

Autistic children and adolescents with frequent restricted interest and repetitive behavior showed more difficulty in social cognition during mask-wearing during the COVID-19 pandemic: a multisite survey

BACKGROUND

The public health measures enacted in order to control the coronavirus disease (COVID-19) pandemic have caused considerable changes to daily life. For autistic children and adolescents, adapting to the "new normal," including mask-wearing, may be difficult because of their restricted interest and repetitive behavior (RRB) characteristics. We aimed to examine the relationships between RRB characteristics and the impact of mask-wearing on their social communications during the pandemic.

METHODS

We recruited participants with a clinical diagnosis of autism spectrum disorder based on DSM-5 diagnostic criteria from two outpatient clinics in Tokyo, Japan, between November 2020 and April 2021 using a convenience sampling methodology. As a result, the participants consisted of 102 children and adolescents (mean (SD) age = 11.6 (5.3)). We collected data on RRB characteristics frequency before and during the pandemic using the CoRonavIruS Health Impact Survey (CRISIS) - Adapted for Autism and Related Neurodevelopmental conditions (AFAR). We then conducted factor analyses to compute the RRB severity composite scores, which are divided into lower- (e.g., sensory seeking), and higher-order (e.g., restricted interest). We also investigated mask-wearing culture using a bespoke questionnaire, and using Spearman's rank correlation analyses, we examined the relationships between before pandemic RRB characteristics, and the impact of mask-wearing on social communications during the pandemic.

RESULTS

We found that children and adolescents who exhibited lower-order RRB before the pandemic had difficulties in going-out with mask-wearing (rho = -0.25, q = .031), more challenges with mask-wearing (rho =  - 0.34, q = .0018), and difficulty in referring to others' emotions while wearing masks (rho =  - 0.36, q = .0016). We also found an association between higher-order RRB before the pandemic and an uncomfortable sensation (rho =  - 0.42, q = .0002) and difficulties in referring to other's emotions while wearing masks (rho =  - 0.25, q = .031).

CONCLUSIONS

We revealed that various behaviors, such as sensory seeking, repetitive motor mannerisms and movements, and rituals and routines, undertaken before the pandemic could be important predictors of difficulties with mask-wearing and social communication for autistic children and adolescents during the pandemic. Caregivers and teachers wearing masks may need to provide extra support for social communication to autistic children and adolescents showing RRB characteristics frequently.

COVID-19 health practices and attitudes in the United States: the role of trust in healthcare

Aim

Individual-level COVID-19 vaccination and related preventive health behaviors is politically polarized in the United States. We examined whether the current polarization in COVID-19 health behavior may be explained by differences in trust in healthcare, locus of control, or insurance status.

Subject and methods

Our sample includes 553 US adults recruited on Amazon MTurk. We assessed odds ratios of currently vaccinated, or willing to be vaccinated if unvaccinated using logistic regression. We assessed count of routine changes and positive attitudes toward facemasks using negative binomial regression.

Results

Trust in healthcare was found to be an important determinant of all COVID-19 related health behavior measured in our study. Further, the effects on COVID-related attitudes/behavior from trust in healthcare are large in magnitude. For instance, our results suggest that individuals at or above the upper quartile of trust in healthcare are around 20 percentage points more likely to be vaccinated than those at or below the lower quartile. Further, we find that the effect of trust in healthcare on adherence or endorsement of COVID-19 mitigation strategies is distinct from political affiliation, i.e., the effect on COVID-19 related health behavior is independent of the polarization across political party lines. Locus of control was not associated with adherence/attitude toward COVID-19 mitigation strategies. Insurance status was only found to be positively associated with odds of being vaccinated.

Conclusion

Our study highlights the importance of increasing trust in healthcare as a means to protect public health in the wake of major public health crises.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10389-022-01737-9.

COVID-19 lockdown introduces human mobility pattern changes for both Guangdong-Hong Kong-Macao greater bay area and the San Francisco bay area

In response to the coronavirus disease 2019 (COVID-19) pandemic, various countries have sought to control COVID-19 transmission by introducing non-pharmaceutical interventions. Restricting population mobility, by introducing social distancing, is one of the most widely used non-pharmaceutical interventions. Although similar population mobility restriction interventions were introduced, their impacts on COVID-19 transmission are often inconsistent across different regions and different time periods. These differences may provide critical information for tailoring COVID-19 control strategies. In this paper, anonymized high spatiotemporal resolution mobile-phone location data were employed to empirically analyze and quantify the impact of lockdowns on population mobility. Both the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) in China and the San Francisco Bay Area (SBA) in the United States were studied. In response to the lockdowns, a general reduction in population mobility was observed, but the structural changes in mobility are very different between the two bays: 1) GBA mobility decreased by approximately 74.0-80.1% while the decrease of SBA was about 25.0-42.1%; 2) compared to SBA, the GBA had smoother volatility in daily volume during the lockdown. The volatility change indexes for GBA and SBA were 2.55% and 7.52%, respectively; 3) the effect of lockdown on short- to long-distance mobility was similar in GBA while the medium- and long-distance impact was more pronounced in SBA.

Awakening City: Traces of the Circadian Rhythm within the Mobile Phone Network Data

In this study, call detail records (CDR), covering Budapest, Hungary, are processed to analyze the circadian rhythm of the subscribers. An indicator, called wake-up time, is introduced to describe the behavior of a group of subscribers. It is defined as the time when the mobile phone activity of a group rises in the morning. Its counterpart is the time when the activity falls in the evening. Inhabitant and area-based aggregation are also presented. The former is to consider the people who live in an area, while the latter uses the transit activity in an area to describe the behavior of a part of the city. The opening hours of the malls and the nightlife of the party district are used to demonstrate this application as real-life examples. The proposed approach is also used to estimate the working hours of the workplaces. The findings are in a good agreement with the practice in Hungary, and also support the workplace detection method. A negative correlation is found between the wake-up time and mobility indicators (entropy, radius of gyration): on workdays, people wake up earlier and travel more, while on holidays, it is quite the contrary. The wake-up time is evaluated in different socioeconomic classes, using housing prices and mobile phones prices, as well. It is found that lower socioeconomic groups tend to wake up earlier.

The impact of control and mitigation strategies during the second wave of coronavirus infections in Spain and Italy

European countries struggled to fight against the second and the third waves of the COVID-19 pandemic, as the Test-Trace-Isolate (TTI) strategy widely adopted over the summer and early fall 2020 failed to contain the spread of the disease effectively. This paper sheds light on the effectiveness of such a strategy in two European countries (Spain and Italy) by analysing data from June to December 2020, collected via a large-scale online citizen survey with 95,251 and 43,393 answers in Spain and Italy, respectively. Our analysis describes several weaknesses in each of the three pillars of the TTI strategy: Test, Trace, and Isolate. We find that 40% of respondents had to wait more than 48 hours to obtain coronavirus tests results, while literature has shown that a delay of more than one day might make tracing all cases inefficient. We also identify limitations in the manual contact tracing capabilities in both countries, as only 29% of respondents in close contact with a confirmed infected individual reported having been contact traced. Moreover, our analysis shows that more than 45% of respondents report being unable to self-isolate if needed. We also analyse the mitigation strategies deployed to contain the second wave of coronavirus. We find that these interventions were particularly effective in Italy, where close contacts were reduced by more than 20% in the general population. Finally, we analyse the participants’ perceptions about the coronavirus risk associated with different daily activities. We observe that they are often gender- and age-dependent, and not aligned with the actual risk identified by the literature. This finding emphasises the importance of deploying public-health communication campaigns to debunk misconceptions about SARS-CoV-2. Overall, our work illustrates the value of online citizen surveys to quickly and efficiently collect large-scale population data to support and evaluate policy decisions to combat the spread of infectious diseases, such as coronavirus.

Modeling international mobility using roaming cell phone traces during COVID-19 pandemic

Most of the studies related to human mobility are focused on intra-country mobility. However, there are many scenarios (e.g., spreading diseases, migration) in which timely data on international commuters are vital. Mobile phones represent a unique opportunity to monitor international mobility flows in a timely manner and with proper spatial aggregation. This work proposes using roaming data generated by mobile phones to model incoming and outgoing international mobility. We use the gravity and radiation models to capture mobility flows before and during the introduction of non-pharmaceutical interventions. However, traditional models have some limitations: for instance, mobility restrictions are not explicitly captured and may play a crucial role. To overtake such limitations, we propose the COVID Gravity Model (CGM), namely an extension of the traditional gravity model that is tailored for the pandemic scenario. This proposed approach overtakes, in terms of accuracy, the traditional models by 126.9% for incoming mobility and by 63.9% when modeling outgoing mobility flows.