Building capacity for equitable healthcare workforce policy, learning from migrant healthcare workers: A qualitative study with Romanian physicians working in Germany during COVID-19.

BACKGROUND: Attention to the healthcare workforce has increased, yet comprehensive information on migrant healthcare workers is missing. This study focuses on migrant healthcare workers' experiences and mobility patterns in the middle of a global health crisis, aiming to explore the capacity for circular migration and support effective and equitable healthcare workforce policy. METHODS: Romanian physicians working in Germany during the COVID-19 pandemic served as an empirical case study. We applied a qualitative explorative approach; interviews (n = 21) were collected from mid of September to early November 2022 and content analysis was performed. RESULTS AND DISCUSSION: Migrant physicians showed strong resilience during the COVID-19 crisis and rarely complained. Commitment to high professional standards and career development were major pull factors towards Germany, while perceptions of limited career choices, nepotism and corruption in Romania caused strong push mechanisms. We identified two major mobility patterns that may support circular migration policies: well-integrated physicians with a wish to give something back to their home country, and mobile cosmopolitan physicians who flexibly balance career opportunities and personal/family interests. Health policy must establish systematic monitoring of the migrant healthcare workforce including actor-centred approaches, support integration in destination countries as well as health system development in sending countries, and invest in evidence-based circular migration policy.

Cold weather isolation is worse in poor and non-white neighborhoods in the United States.

Social isolation can cause a variety of adverse physical and mental health effects and is central to understanding broader social disparities among marginalized groups in the United States. This study aims to assess whether temperature variation is associated with daily social isolation at the neighborhood level. I test a series of two-way fixed effects models to see if mean daily temperature is associated with individuals spending the entire day at home, as measured using smartphone data, across a sample of 45 million devices in 2019 in the United States. Using interaction terms, I specifically examine heterogeneity in temperature effects by neighborhood racial composition and socioeconomic status. The two-way fixed effects models reveal highly statistically significant negative coefficients for the interaction between temperature and neighborhood proportion Black, temperature and neighborhood proportion Hispanic, and temperature and neighborhood residential disadvantage, in predicting the probability of spending the entire day at home. In marginal terms, the findings indicate the gap in the probability of spending the entire day at home between an all-Black neighborhood and an all-White neighborhood grows by nearly 10 percentage points from the warmest day of the year to the coldest day of the year in some parts of the United States. My models highlight how residents of poor and majority Black and Hispanic neighborhoods experience disproportionate social isolation in the form of a greater propensity to spend the entire day at home.

The role of migration networks in the development of Botswana's generalized HIV epidemic.

The majority of people with HIV live in sub-Saharan Africa, where epidemics are generalized. For these epidemics to develop, populations need to be mobile. However, the role of population-level mobility in the development of generalized HIV epidemics has not been studied. Here we do so by studying historical migration data from Botswana, which has one of the most severe generalized HIV epidemics worldwide; HIV prevalence was 21% in 2021. The country reported its first AIDS case in 1985 when it began to rapidly urbanize. We hypothesize that, during the development of Botswana's epidemic, the population was extremely mobile and the country was highly connected by substantial migratory flows. We test this mobility hypothesis by conducting a network analysis using a historical time series (1981-2011) of micro-census data from Botswana. Our results support our hypothesis. We found complex migration networks with very high rates of rural-to-urban, and urban-to-rural, migration: 10% of the population moved annually. Mining towns (where AIDS cases were first reported, and risk behavior was high) were important in-flow and out-flow migration hubs, suggesting that they functioned as 'core groups' for HIV transmission and dissemination. Migration networks could have dispersed HIV throughout Botswana and generated the current hyperendemic epidemic.

Over 25 million people in sub-Saharan Africa live with HIV. After reporting its first AIDS case in 1985, Botswana is one of the most severely affected countries in the region, with one in five adults now living with HIV. Movement of the population is likely to have contributed to a geographically dispersed, and high-prevalence, HIV epidemic in Botswana. Since 1985, urbanization, rapid economic and population growth, and migration have transformed Botswana. Yet, few studies have analyzed the role of population-level movement patterns in the spread of HIV during this time. By studying micro-census data from Botswana between 1981 and 2011, Song et al. found that the country's population was highly mobile during this period. Reconstructions of internal migration patterns show very high rates of rural-to-urban and urban-to-rural migration, with 10% of Botswana's population moving each year. The first reported AIDS cases in Botswana occurred in mining towns and cities where high-risk behavior was prevalent. These areas were also migration hubs during this period and could have contributed to the rapid spread of HIV throughout the country as infected individuals moved back to rural districts. Understanding human migration patterns and how they affect the spread of infectious diseases using current data could help public health authorities in Botswana and additional sub-Saharan African countries design control strategies for HIV and other important infections that occur in the region.

Big data analytics and smart cities: applications, challenges, and opportunities.

Urban environments continuously generate larger and larger volumes of data, whose analysis can provide descriptive and predictive models as valuable support to inspire and develop data-driven Smart City applications. To this aim, Big data analysis and machine learning algorithms can play a fundamental role to bring improvements in city policies and urban issues. This paper introduces how Big Data analysis can be exploited to design and develop data-driven smart city services, and provides an overview on the most important Smart City applications, grouped in several categories. Then, it presents three real-case studies showing how data analysis methodologies can provide innovative solutions to deal with smart city issues. The first one is an approach for spatio-temporal crime forecasting (tested on Chicago crime data), the second one is methodology to discover mobility hotsposts and trajectory patterns from GPS data (tested on Beijing taxi traces), the third one is an approach to discover predictive epidemic patterns from mobility and infection data (tested on real COVID-19 data). The presented real-world cases prove that data analytics models can effectively support city managers in tackling smart city challenges and improving urban applications.

A Systematic Review of Mobile Phone Data in Crime Applications: A Coherent Taxonomy Based on Data Types and Analysis Perspectives, Challenges, and Future Research Directions.

Digital technologies have recently become more advanced, allowing for the development of social networking sites and applications. Despite these advancements, phone calls and text messages still make up the largest proportion of mobile data usage. It is possible to study human communication behaviors and mobility patterns using the useful information that mobile phone data provide. Specifically, the digital traces left by the large number of mobile devices provide important information that facilitates a deeper understanding of human behavior and mobility configurations for researchers in various fields, such as criminology, urban sensing, transportation planning, and healthcare. Mobile phone data record significant spatiotemporal (i.e., geospatial and time-related data) and communication (i.e., call) information. These can be used to achieve different research objectives and form the basis of various practical applications, including human mobility models based on spatiotemporal interactions, real-time identification of criminal activities, inference of friendship interactions, and density distribution estimation. The present research primarily reviews studies that have employed mobile phone data to investigate, assess, and predict human communication and mobility patterns in the context of crime prevention. These investigations have sought, for example, to detect suspicious activities, identify criminal networks, and predict crime, as well as understand human communication and mobility patterns in urban sensing applications. To achieve this, a systematic literature review was conducted on crime research studies that were published between 2014 and 2022 and listed in eight electronic databases. In this review, we evaluated the most advanced methods and techniques used in recent criminology applications based on mobile phone data and the benefits of using this information to predict crime and detect suspected criminals. The results of this literature review contribute to improving the existing understanding of where and how populations live and socialize and how to classify individuals based on their mobility patterns. The results show extraordinary growth in studies that utilized mobile phone data to study human mobility and movement patterns compared to studies that used the data to infer communication behaviors. This observation can be attributed to privacy concerns related to acquiring call detail records (CDRs). Additionally, most of the studies used census and survey data for data validation. The results show that social network analysis tools and techniques have been widely employed to detect criminal networks and urban communities. In addition, correlation analysis has been used to investigate spatial-temporal patterns of crime, and ambient population measures have a significant impact on crime rates.

An analytical tool to support public policies and isolation barriers against SARS-CoV-2 based on mobility patterns and socio-economic aspects.

It is crucial to develop spatiotemporal analysis tools to mitigate risks during a pandemic. Many dashboards encountered in the literature do not consider how the geolocation characteristics and travel patterns may influence the spread of the virus. This work brings an interactive tool that is capable of crossing information about mobility patterns, geolocation characteristics and epidemiologic variables. To do so, our system uses a mobility network, generated through anonymized mobile location data, which enables the division of a region into representative clusters. The clusters' aggregated socioeconomic, and epidemiologic indicators can be analyzed through multiple coordinated views. The proposal is to enable users to understand how different locations commute citizens, monitor risk over time, and understand what locations need more assistance, considering different layers of visualization, such as clusters and individual locations. The main novelty is the interactive way to construct the mobility network that defines the social distancing level and the way that risks are managed, since many different geolocation characteristics can be considered and visualized, such as socioeconomic indicators of a location, the economic importance of a set of locations, and the connection of important neighborhoods of a city with other cities. The proposed tool was built and verified by experts assembled to give scientific recommendations to the city administration of Recife, the capital city of Pernambuco. Our analysis shows how a policymaker could use the tool to evaluate different isolation scenarios considering the trade-off between economic activity and contamination risk, where the practical insights can also be used to tighten and relax mitigation measures in other phases of a pandemic.

Mobile Phone Data: A Survey of Techniques, Features, and Applications.

Due to the rapid growth in the use of smartphones, the digital traces (e.g., mobile phone data, call detail records) left by the use of these devices have been widely employed to assess and predict human communication behaviors and mobility patterns in various disciplines and domains, such as urban sensing, epidemiology, public transportation, data protection, and criminology. These digital traces provide significant spatiotemporal (geospatial and time-related) data, revealing people's mobility patterns as well as communication (incoming and outgoing calls) data, revealing people's social networks and interactions. Thus, service providers collect smartphone data by recording the details of every user activity or interaction (e.g., making a phone call, sending a text message, or accessing the internet) done using a smartphone and storing these details on their databases. This paper surveys different methods and approaches for assessing and predicting human communication behaviors and mobility patterns from mobile phone data and differentiates them in terms of their strengths and weaknesses. It also gives information about spatial, temporal, and call characteristics that have been extracted from mobile phone data and used to model how people communicate and move. We survey mobile phone data research published between 2013 and 2021 from eight main databases, namely, the ACM Digital Library, IEEE Xplore, MDPI, SAGE, Science Direct, Scopus, SpringerLink, and Web of Science. Based on our inclusion and exclusion criteria, 148 studies were selected.

Examining Multilevel Correlates of Geographic Mobility in a Sample of US Young Adults Before and During the COVID-19 Pandemic.

Before the COVID-19 pandemic, geographic mobility, previously viewed as an indicator of economic stability, was declining among young adults. Yet, these trends shifted during the COVID-19 pandemic; young adults were more likely to move during COVID-19 for reasons related to reducing disease transmission and fewer educational and job opportunities. Few studies have documented the individual and neighborhood characteristics of young adults who moved before and during the pandemic. We used data from a cohort of young adults aged 18-34 in six metropolitan areas to examine individual- and neighborhood-level predictors of mobility before and during the COVID-19 pandemic. The sample was majority female, white, and educated with a bachelor's degree or more. Residents in neighborhoods they lived in were mostly White, US-born, employed, and lived above the poverty level. Before the pandemic, identifying as a sexual minority was significantly related to mobility. During the pandemic, being younger, single, and non-Hispanic were significantly related to mobility. Higher neighborhood poverty was significantly related to mobility before and during the COVID-19 pandemic. Future studies that examine young adult populations who moved during the pandemic are needed to determine whether COVID-19 related moves increase economic instability and subsequent health-related outcomes.

The Relative Roles of Ambient Temperature and Mobility Patterns in Shaping the Transmission Heterogeneity of SARS-CoV-2 in Japan.

We assess the effects of ambient temperature and mobility patterns on the transmissibility of COVID-19 during the epidemiological years of the pandemic in Japan. The prefecture-specific daily time-series of confirmed coronavirus disease 2019 (COVID-19) cases, meteorological variables, levels of retail and recreation mobility (e.g., activities, going to restaurants, cafes, and shopping centers), and the number of vaccinations were collected for six prefectures in Japan from 1 May 2020 to 31 March 2022. We combined standard time-series generalized additive models (GAMs) with a distributed lag non-linear model (DLNM) to determine the exposure-lag-response association between the time-varying effective reproductive number (Rt), ambient temperature, and retail and recreation mobility, while controlling for a wide range of potential confounders. Utilizing a statistical model, the first distribution of the mean ambient temperature (i.e., -4.9 °C) was associated with an 11.6% (95% confidence interval [CI]: 5.9-17.7%) increase in Rt compared to the optimum ambient temperature (i.e., 18.5 °C). A retail and recreation mobility of 10.0% (99th percentile) was associated with a 19.6% (95% CI: 12.6-27.1%) increase in Rt over the optimal level (i.e., -16.0%). Our findings provide a better understanding of how ambient temperature and mobility patterns shape severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission. These findings provide valuable epidemiological insights for public health policies in controlling disease transmission.

Urban mobility patterns and the spatial distribution of infections in Santiago de Chile.

The process of a virus spread is inherently spatial. Even though Latin America became the epicenter of the COVID-19 pandemic in May 2020, there is still little evidence of the relationship between urban mobility and virus propagation in the region. This paper combines network analysis of mobility patterns in public transportation with a spatial error correction model for Santiago de Chile. Results indicate that a 10% higher number of daily public transportation trips received by an administrative unit in the city was associated with a 1.3% higher number of confirmed COVID-19 cases per 100,000 inhabitants. Following these findings, we propose an empirical method to identify and classify neighborhoods according to the level and type of risk for COVID-19-like disease propagation, helping policymakers manage mobility during the initial stages of an epidemic outbreak.

The response of geophytes to continuous human foraging on the Cape south coast, South Africa and its implications for early hunter-gatherer mobility patterns.

Current ecological understanding of plants with underground storage organs (USOs) suggests they have, in general, low rates of recruitment and thus as a resource it should be rapidly exhausted, which likely had implications for hunter-gatherer mobility patterns. We focus on the resilience (defined here as the ability of species to persist after being harvested) of USOs to human foraging. Human foragers harvested all visible USO material from 19 plots spread across six Cape south coast (South Africa) vegetation types for three consecutive years (2015-2017) during the period of peak USO apparency (September-October). We expected the plots to be depleted after the first year of harvesting since the entire storage organ of the USO is removed during foraging, i.e. immediate and substantial declines from the first to the second harvest. However, over 50% of the total weight harvested in 2015 was harvested in 2016 and 2017; only after two consecutive years of harvesting, was there evidence of significantly lower yield (p = 0.034) than the first (2015) harvest. Novel emergence of new species and new individuals in year two and three buffered the decline of harvested USOs. We use our findings to make predictions on hunter-gatherer mobility patterns in this region compared to the Hadza in East Africa and the Alyawara in North Australia.

Managing Perceived Loneliness and Social-Isolation Levels for Older Adults: A Survey with Focus on Wearables-Based Solutions.

As an inevitable process, the number of older adults is increasing in many countries worldwide. Two of the main problems that society is being confronted with more and more, in this respect, are the inter-related aspects of feelings of loneliness and social isolation among older adults. In particular, the ongoing COVID-19 crisis and its associated restrictions have exacerbated the loneliness and social-isolation problems. This paper is first and foremost a comprehensive survey of loneliness monitoring and management solutions, from the multidisciplinary perspective of technology, gerontology, socio-psychology, and urban built environment. In addition, our paper also investigates machine learning-based technological solutions with wearable-sensor data, suitable to measure, monitor, manage, and/or diminish the levels of loneliness and social isolation, when one also considers the constraints and characteristics coming from social science, gerontology, and architecture/urban built environments points of view. Compared to the existing state of the art, our work is unique from the cross-disciplinary point of view, because our authors' team combines the expertise from four distinct domains, i.e., gerontology, social psychology, architecture, and wireless technology in addressing the two inter-related problems of loneliness and social isolation in older adults. This work combines a cross-disciplinary survey of the literature in the four aforementioned domains with a proposed wearable-based technological solution, introduced first as a generic framework and, then, exemplified through a simple proof of concept with dummy data. As the main findings, we provide a comprehensive view on challenges and solutions in utilizing various technologies, particularly those carried by users, also known as wearables, to measure, manage, and/or diminish the social isolation and the perceived loneliness among older adults. In addition, we also summarize the identified solutions which can be used for measuring and monitoring various loneliness- and social isolation-related metrics, and we present and validate, through a simple proof-of-concept mechanism, an approach based on machine learning for predicting and estimating loneliness levels. Open research issues in this field are also discussed.

Strategizing COVID-19 lockdowns using mobility patterns.

During the COVID-19 pandemic, governments have attempted to control infections within their territories by implementing border controls and lockdowns. While large-scale quarantine has been the most successful short-term policy, the enormous costs exerted by lockdowns over long periods are unsustainable. As such, developing more flexible policies that limit transmission without requiring large-scale quarantine is an urgent priority. Here, the dynamics of dismantled community mobility structures within US society during the COVID-19 outbreak are analysed by applying the Louvain method with modularity optimization to weekly datasets of mobile device locations. Our networks are built based on individuals' movements from February to May 2020. In a multi-scale community detection process using the locations of confirmed cases, natural break points from mobility patterns as well as high risk areas for contagion are identified at three scales. Deviations from administrative boundaries were observed in detected communities, indicating that policies informed by assumptions of disease containment within administrative boundaries do not account for high risk patterns of movement across and through these boundaries. We have designed a multi-level quarantine process that takes these deviations into account based on the heterogeneity in mobility patterns. For communities with high numbers of confirmed cases, contact tracing and associated quarantine policies informed by underlying dismantled community mobility structures is of increasing importance.

Change in GPS-assessed walking locations following a cluster-randomized controlled physical activity trial in older adults, results from the MIPARC trial.

This study employed novel GPS methods to assess the effect of a multilevel physical activity (PA) intervention on device-measured walking locations in 305 community dwelling older adults, ages 65+ (mean age = 83, 73% women). Retirement communities were randomized to a 1-year PA intervention that encouraged neighborhood walking, or to a healthy aging control condition. Total time and time spent walking in four life-space domains were assessed using GPS and accelerometer devices. The intervention increased the time spent walking as a proportion of total time spent in the Campus, Neighborhood and Beyond Neighborhood domains. Intervention effects on walking location were observed in both genders and across physical and cognitive functioning groups. Results demonstrate that an intervention providing individual, social and environmental support for walking can increase PA in larger life-space domains for a broad spectrum of older adults.

Technology to Improve Autonomy and Inform Housing Decisions for Older Adults With Memory Problems Who Live at Home in Canada, Sweden, and the Netherlands: Protocol for a Multipronged Mixed Methods Study.

BACKGROUND: Understanding the mobility patterns and experiences of older adults with memory problems living at home has the potential to improve autonomy and inform shared decision making (SDM) about their housing options. OBJECTIVE: We aim to (1) assess the mobility patterns and experiences of older adults with memory problems, (2) co-design an electronic decision support intervention (e-DSI) that integrates users' mobility patterns and experiences, (3) explore their intention to use an e-DSI to support autonomy at home, and (4) inform future SDM processes about housing options. METHODS: Informed by the Good Reporting of A Mixed Methods Study (GRAMMS) reporting guidelines, we will conduct a 3-year, multipronged mixed methods study in Canada, Sweden, and the Netherlands. For Phase 1, we will recruit a convenience sample of 20 older adults living at home with memory problems from clinical and community settings in each country, for a total of 60 participants. We will ask participants to record their mobility patterns outside their home for 14 days using a GPS tracker and a travel diary; in addition, we will conduct a walking interview and a final debrief interview after 14 days. For Phase 2, referring to results from the first phase, we will conduct one user-centered co-design process per country with older adults with memory issues, caregivers, health care professionals, and information technology representatives informed by the Double Diamond method. We will ask participants how personalized information about mobility patterns and experiences could be added to an existing e-DSI and how this information could inform SDM about housing options. For Phase 3, using online web-based surveys, we will invite 210 older adults with memory problems and/or their caregivers, split equally across the three countries, to use the e-DSI and provide feedback on its strengths and limitations. Finally, in Phase 4, we will triangulate and compare data from all phases and countries to inform a stakeholder meeting where an action plan will be developed. RESULTS: The study opened for recruitment in the Netherlands in November 2018 and in Canada and Sweden in December 2019. Data collection will be completed by April 2021. CONCLUSIONS: This project will explore how e-DSIs can integrate the mobility patterns and mobility experiences of older adults with memory problems in three countries, improve older adults' autonomy, and, ultimately, inform SDM about housing options. TRIAL REGISTRATION: ClinicalTrials.gov NCT04267484; https://clinicaltrials.gov/ct2/show/NCT04267484. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/19244.

A Smartphone App to Support Sedentary Behavior Change by Visualizing Personal Mobility Patterns and Action Planning (SedVis): Development and Pilot Study.

BACKGROUND: Prolonged sedentary behavior is related to a number of risk factors for chronic diseases. Given the high prevalence of sedentary behavior in daily life, simple yet practical solutions for behavior change are needed to avoid detrimental health effects. OBJECTIVE: The mobile app SedVis was developed based on the health action process approach. The app provides personal mobility pattern visualization (for both physical activity and sedentary behavior) and action planning for sedentary behavior change. The primary aim of the study is to investigate the effect of mobility pattern visualization on users' action planning for changing their sedentary behavior. The secondary aim is to evaluate user engagement with the visualization and user experience of the app. METHODS: A 3-week user study was conducted with 16 participants who had the motivation to reduce their sedentary behavior. Participants were allocated to either an active control group (n=8) or an intervention group (n=8). In the 1-week baseline period, none of the participants had access to the functions in the app. In the following 2-week intervention period, only the intervention group was given access to the visualizations, whereas both groups were asked to make action plans every day and reduce their sedentary behavior. Participants' sedentary behavior was estimated based on the sensor data of their smartphones, and their action plans and interaction with the app were also recorded by the app. Participants' intention to change their sedentary behavior and user experience of the app were assessed using questionnaires. RESULTS: The data were analyzed using both traditional null hypothesis significance testing (NHST) and Bayesian statistics. The results suggested that the visualizations in SedVis had no effect on the participants' action planning according to both the NHST and Bayesian statistics. The intervention involving visualizations and action planning in SedVis had a positive effect on reducing participants' sedentary hours, with weak evidence according to Bayesian statistics (Bayes factor, BF+0=1.92; median 0.52; 95% CI 0.04-1.25), whereas no change in sedentary time was more likely in the active control condition (BF+0=0.28; median 0.18; 95% CI 0.01-0.64). Furthermore, Bayesian analysis weakly suggested that the more frequently the users checked the app, the more likely they were to reduce their sedentary behavior (BF-0=1.49; r=-0.50). CONCLUSIONS: Using a smartphone app to collect data on users' mobility patterns and provide real-time feedback using visualizations may be a promising method to induce changes in sedentary behavior and may be more effective than action planning alone. Replications with larger samples are needed to confirm these findings.

Lockdowned: Everyday mobility changes in response to COVID-19.

Background: This paper looks into the impact of the recent COVID-19 epidemic on the daily mobility of people. Existing research into the epidemic travel patterns points at transport as a channel for disease spreading with especially long-distance travel in the centre of interest. We adopt a different approach looking into the effects that epidemic has on the transport system and specifically in relation to short-distance daily mobility activities. We go beyond simple travel avoidance behaviours and look into factors influencing change in travel times and in modal split under epidemic. This leads to the research problems we posit in this paper. We look into the overall reduction of daily travel and into the factors impacting peoples' decisions to refrain from daily traveling. This paper focuses on modes affected and explores differences between various societal groups. Methods: We use a CATI survey with a representative sample size of 1069 respondents from Poland. The survey was carried out between March, 24th and April, 6th2020, with a start date one week after the Polish government introduced administrative measures aimed at slowing down the COVID-19 epidemic. For data analysis, we propose using the GLM (general linear model), allowing us to include all the qualitative and quantitative variables which depict our sample. Results: We observe significant drops in travel times under epidemic conditions. Those drops are similar regardless of the age group and gender. The time decrease depended on the purpose of travels, means of transport, traveller's household size, fear of coronavirus, main occupation, and change in it caused by the epidemic. The more the respondent was afraid of coronavirus, the more she or he shortened the travel time.

Health equity considerations in COVID-19: geospatial network analysis of the COVID-19 outbreak in the migrant population in Singapore.

BACKGROUND: Low-wage dormitory-dwelling migrant workers in Singapore were disproportionately affected by coronavirus disease 2019 (COVID-19) infection. This was attributed to communal living in high-density and unhygienic dormitory settings and a lack of inclusive protection systems. However, little is known about the roles of social and geospatial networks in COVID-19 transmission. The study examined the networks of non-work-related activities among migrant workers to inform the development of lockdown exit strategies and future pandemic preparedness. METHODS: A population-based survey was conducted with 509 migrant workers across the nation, and it assessed dormitory attributes, social ties, physical and mental health status, COVID-19-related variables and mobility patterns using a grid-based network questionnaire. Mobility paths from dormitories were presented based on purposes of visit. Two-mode social networks examined the structures and positions of networks between workers and visit areas with individual attributes. RESULTS: COVID-19 risk exposure was associated with the density of dormitory, social ties and visit areas. The migrant worker hub in the city centre was the most frequently visited for essential services of grocery shopping and remittance, followed by south central areas mainly for social gathering. The hub was positioned as the core with the highest degree of centrality with a cluster of workers exposed to COVID-19. CONCLUSIONS: Social and geospatial networks of migrant workers should be considered in the implementation of lockdown exit strategies while addressing the improvement of living conditions and monitoring systems. Essential services, like remittance and grocery shopping at affordable prices, need to be provided near to dormitories to minimize excess gatherings.

Mapping travel behavior changes during the COVID-19 lock-down: a socioeconomic analysis in Greece.

Background: COVID-19 pandemic is a challenge that the world had never encountered in the last 100 years. In order to mitigate its negative effects, governments worldwide took action by prohibiting at first certain activities and in some cases by a countrywide lockdown. Greece was among the countries that were struck by the pandemic. Governmental authorities took action in limiting the spread of the pandemic through a series of countermeasures, which built up to a countrywide lockdown that lasted 42 days. Methodology: This research aims at identifying the effect of certain socioeconomic factors on the travel behaviour of Greek citizens and at investigating whether any social groups were comparatively less privileged or suffered more from the lockdown. To this end, a dynamic online questionnaire survey on mobility characteristics was designed and distributed to Greek citizens during the lockdown period, which resulted in 1,259 valid responses. Collected data were analysed through descriptive and inferential statistical tests, in order to identify mobility patterns and correlations with certain socioeconomic characteristics. Additionally, a Generalised Linear Model (GLM) was developed in order to examine the potential influence of socioeconomic characteristics to trip frequency before and during the lockdown period. Results: Outcomes indicate a decisive decrease in trip frequencies due to the lockdown. Furthermore, the model's results indicate significant correlations between gender, income and trip frequencies during the lockdown, something that is not evident in the pre-pandemic era.

Estimating the impact of mobility patterns on COVID-19 infection rates in 11 European countries.

BACKGROUND: As governments across Europe have issued non-pharmaceutical interventions (NPIs) such as social distancing and school closing, the mobility patterns in these countries have changed. Most states have implemented similar NPIs at similar time points. However, it is likely different countries and populations respond differently to the NPIs and that these differences cause mobility patterns and thereby the epidemic development to change. METHODS: We build a Bayesian model that estimates the number of deaths on a given day dependent on changes in the basic reproductive number, R 0, due to differences in mobility patterns. We utilise mobility data from Google mobility reports using five different categories: retail and recreation, grocery and pharmacy, transit stations, workplace and residential. The importance of each mobility category for predicting changes in R 0 is estimated through the model. FINDINGS: The changes in mobility have a considerable overlap with the introduction of governmental NPIs, highlighting the importance of government action for population behavioural change. The shift in mobility in all categories shows high correlations with the death rates 1 month later. Reduction of movement within the grocery and pharmacy sector is estimated to account for most of the decrease in R 0. INTERPRETATION: Our model predicts 3-week epidemic forecasts, using real-time observations of changes in mobility patterns, which can provide governments with direct feedback on the effects of their NPIs. The model predicts the changes in a majority of the countries accurately but overestimates the impact of NPIs in Sweden and Denmark and underestimates them in France and Belgium. We also note that the exponential nature of all epidemiological models based on the basic reproductive number, R 0 cause small errors to have extensive effects on the predicted outcome.