COVID-19 is spatial: Ensuring that mobile Big Data is used for social good

Analyzing post-COVID-19 demographic and mobility changes in Andalusia using mobile phone data

This work studies changes in the demographics of the different spatial units that make up the Andalusia region in Spain throughout the year 2021, with the aim of seeing the progressive recovery of the population after the COVID-19 pandemic. Mobile phone data from Origin-Destination matrices has been used, due to the ease of obtaining updated information quickly and constantly. A methodology has been developed to transform the number of travelers into an estimated population without biases, and an interpolation function has been used to take into account all the data available in the year 2021. Results show a direct link between the demographic changes in Andalusia and the removal of the mobility restrictions caused by the COVID-19 pandemic, with an increase of non-related work mobility and a decrease of static population. Travel distances between home and work places are also affected, with an increase of long trips after the end of the mobility restrictions. In addition, different patterns have been visualized, such as the concentration of commuting in the metropolitan areas of the region during working days, the population growth in rural areas during weekends, or the population displacement to coastal areas in summer.

Short- and Long-Term Predicted and Witnessed Consequences of Digital Surveillance During the COVID-19 Pandemic: Scoping Review

BACKGROUND

The COVID-19 pandemic has prompted the deployment of digital technologies for public health surveillance globally. The rapid development and use of these technologies have curtailed opportunities to fully consider their potential impacts (eg, for human rights, civil liberties, privacy, and marginalization of vulnerable groups).

OBJECTIVE

We conducted a scoping review of peer-reviewed and gray literature to identify the types and applications of digital technologies used for surveillance during the COVID-19 pandemic and the predicted and witnessed consequences of digital surveillance.

METHODS

Our methodology was informed by the 5-stage methodological framework to guide scoping reviews: identifying the research question; identifying relevant studies; study selection; charting the data; and collating, summarizing, and reporting the findings. We conducted a search of peer-reviewed and gray literature published between December 1, 2019, and December 31, 2020. We focused on the first year of the pandemic to provide a snapshot of the questions, concerns, findings, and discussions emerging from peer-reviewed and gray literature during this pivotal first year of the pandemic. Our review followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) reporting guidelines.

RESULTS

We reviewed a total of 147 peer-reviewed and 79 gray literature publications. Based on our analysis of these publications, we identified a total of 90 countries and regions where digital technologies were used for public health surveillance during the COVID-19 pandemic. Some of the most frequently used technologies included mobile phone apps, location-tracking technologies, drones, temperature-scanning technologies, and wearable devices. We also found that the literature raised concerns regarding the implications of digital surveillance in relation to data security and privacy, function creep and mission creep, private sector involvement in surveillance, human rights, civil liberties, and impacts on marginalized groups. Finally, we identified recommendations for ethical digital technology design and use, including proportionality, transparency, purpose limitation, protecting privacy and security, and accountability.

CONCLUSIONS

A wide range of digital technologies was used worldwide to support public health surveillance during the COVID-19 pandemic. The findings of our analysis highlight the importance of considering short- and long-term consequences of digital surveillance not only during the COVID-19 pandemic but also for future public health crises. These findings also demonstrate the ways in which digital surveillance has rendered visible the shifting and blurred boundaries between public health surveillance and other forms of surveillance, particularly given the ubiquitous nature of digital surveillance.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-https://doi.org/10.1136/bmjopen-2021-053962.

Towards more realistic measures of accessibility to emergency departments in Sweden

BACKGROUND

Assuring that emergency health care (EHC) is accessible is a key objective for health care planners. Conventional accessibility analysis commonly relies on resident population data. However, the allocation of resources based on stationary population data may lead to erroneous assumptions of population accessibility to EHC.

METHOD

Therefore, in this paper, we calculate population accessibility to emergency departments in Sweden with a geographical information system based network analysis. Utilizing static population data and dynamic population data, we investigate spatiotemporal patterns of how static population data over- or underestimates population sizes derived from temporally dynamic population data.

RESULTS

Our findings show that conventional measures of population accessibility tend to underestimate population sizes particularly in rural areas and in smaller ED's catchment areas compared to urban, larger ED's-especially during vacation time in the summer.

CONCLUSIONS

Planning based on static population data may thus lead to inequitable distributions of resources. This study is motivated in light of the ongoing centralization of ED's in Sweden, which largely depends on population sizes in ED's catchment areas.

Social media insights into spatio-temporal emotional responses to COVID-19 crisis

The Coronavirus pandemic has presented multifaceted challenges in urban emotional well-being and mental health management. Our study presents a spatio-temporal sentiment mining (STSM) framework to address these challenges, focusing on the space-time geography and environmental psychology. This framework analyzes the distribution and trends of 6 categories of public sentiments in Shanghai during the COVID-19 crisis, considering the potential urban spatial influencing factors. The research specifically draws on social media data temporally coinciding with the spread of COVID-19 and the pre-trained language model RoBERTa-wwm-ext to classify public sentiment, in order to characterize the distribution and trends of dominant urban sentiment under the influence of epidemic at different phases. The interactions between urban geospatial features and sentiments are further modelled and explained using LightGBM algorithm and SHapley Additive exPlanations (SHAP) technique. The experimental findings reveal the subtle yet dynamic impact of the urban environment on the long-term spatial variation and trends of public sentiment under the epidemic, with green spaces and socio-economic status emerging as significant factors. Regions with higher permanent population consumption demonstrated more positive sentiments, underscoring the significance of socio-economic factors in urban planning and public health policy. This research offers the most extensive analysis to date on the influence of urban characteristics on public sentiment during Shanghai's epidemic life cycle also lays the groundwork for applying the STSM framework in future crises beyond COVID-19.

People's political views, perceived social norms, and individualism shape their privacy concerns for and acceptance of pandemic control measures that use individual-level georeferenced data

BACKGROUND

As the COVID-19 pandemic became a major global health crisis, many COVID-19 control measures that use individual-level georeferenced data (e.g., the locations of people's residences and activities) have been used in different countries around the world. Because these measures involve some disclosure risk and have the potential for privacy violations, people's concerns for geoprivacy (locational privacy) have recently heightened as a result, leading to an urgent need to understand and address the geoprivacy issues associated with COVID-19 control measures that use data on people's private locations.

METHODS

We conducted an international cross-sectional survey in six study areas (n = 4260) to examine how people's political views, perceived social norms, and individualism shape their privacy concerns, perceived social benefits, and acceptance of ten COVID-19 control measures that use individual-level georeferenced data. Multilevel linear regression models were used to examine these effects. We also applied multilevel structure equation models (SEMs) to explore the direct, indirect, and mediating effects among the variables.

RESULTS

We observed a tradeoff relationship between people's privacy concerns and the acceptance (and perceived social benefits) of the control measures. People's perceived social tightness and vertical individualism are positively associated with their acceptance and perceived social benefits of the control measures, while horizontal individualism has a negative association. Further, people with conservative political views and high levels of individualism (both vertical and horizontal) have high levels of privacy concerns.

CONCLUSIONS

Our results first suggest that people's privacy concerns significantly affect their perceived social benefits and acceptance of the COVID-19 control measures. Besides, our results also imply that strengthening social norms may increase people's acceptance and perceived social benefits of the control measures but may not reduce people's privacy concerns, which could be an obstacle to the implementation of similar control measures during future pandemics. Lastly, people's privacy concerns tend to increase with their conservatism and individualism.

Estimation of mobility and population in Spain during different phases of the COVID-19 pandemic from mobile phone data

This work aims to find out the effectiveness of sources based on Big Data like mobile phone records to analyze mobility flows and changes in the population of Spain in different scenarios during the period of the pandemic caused by the COVID-19 virus. To this end, we have used mobile phone data provided by the National Institute of Statistics from four days corresponding to different phases of the pandemic. Origin-Destination matrices and population estimation calculations at the spatial level of population cells have been elaborated. The results show different patterns that correspond to the phenomena that have occurred, as the decrease of the population during the periods associated with the confinement measures. The consistency of findings with the reality and the generally good correlation with the population census data indicate that mobile phone records are a useful source of data for the elaboration of demographic and mobility studies during pandemics.

What data counts in policymaking and programming evaluation - Relevant data sources for triangulation according to main epistemologies and philosophies within social science

Policy analysis and program evaluation quality guides how impact is measured, revisions are made, and allocations of resources is deployed. As interdisciplinary research grows in contemporary policy science, the importance of defining truth can be contentious among different social scientists. This research traces the history of triangulation to its contemporary version within the social sciences. The study examines the philosophical evolution influencing the environment in which triangulation develops including: consilience and the comparative linguistic structure of the traditions of thought; and the historical development of methods and emergence of triangulation in research. It also offers contrasting interpretations of triangulation within the various epistemologies and philosophies of science that have arisen in recent movements within social science. As research strives to address novel 21st century issues including big data, pandemics, misinformation, and globalization, there is a need for rigorous social science and policy-based research to be aware of different interpretations of empirical data and valid research methods across disciplines, examine new and old phenomena using multi-method approaches, and validate data informing policy through methods of triangulation.

Use of digital technologies for public health surveillance during the COVID-19 pandemic: A scoping review

Throughout the COVID-19 pandemic, a variety of digital technologies have been leveraged for public health surveillance worldwide. However, concerns remain around the rapid development and deployment of digital technologies, how these technologies have been used, and their efficacy in supporting public health goals. Following the five-stage scoping review framework, we conducted a scoping review of the peer-reviewed and grey literature to identify the types and nature of digital technologies used for surveillance during the COVID-19 pandemic and the success of these measures. We conducted a search of the peer-reviewed and grey literature published between 1 December 2019 and 31 December 2020 to provide a snapshot of questions, concerns, discussions, and findings emerging at this pivotal time. A total of 147 peer-reviewed and 79 grey literature publications reporting on digital technology use for surveillance across 90 countries and regions were retained for analysis. The most frequently used technologies included mobile phone devices and applications, location tracking technologies, drones, temperature scanning technologies, and wearable devices. The utility of digital technologies for public health surveillance was impacted by factors including uptake of digital technologies across targeted populations, technological capacity and errors, scope, validity and accuracy of data, guiding legal frameworks, and infrastructure to support technology use. Our findings raise important questions around the value of digital surveillance for public health and how to ensure successful use of technologies while mitigating potential harms not only in the context of the COVID-19 pandemic, but also during other infectious disease outbreaks, epidemics, and pandemics.

Determination of Population Mobility Dynamics in Popayán-Colombia during the COVID-19 Pandemic Using Open Datasets

The COVID-19 pandemic is a catastrophic event that marked the history of humanity. The virus's transmissibility has primarily prevented the control of the pandemic, so it has become vital to determine and control the dynamics of the population mobility to reduce the epidemiological impact. Considering the above, this paper uses an exposure indicator based on the movement ranges provided by Facebook to determine the dynamics of population mobility in Popayán city for the period after the appearance of COVID-19. Using statistical analysis techniques, it then contrasts the data obtained with the public circulation reports provided by Google and Apple. The results suggest that the exposure indicator is reliable and presents moderate to strong linear relationships for the public data, which implies that it can be an additional resource for decision-making to curb the spread of the virus.

Data science for pedestrian and high street retailing as a framework for advancing urban informatics to individual scales

BACKGROUND

In this paper, we consider the applicability of the customer journey framework from retailing as a driver for urban informatics at individual scales within urban science. The customer journey considers shopper experiences in the context of shopping paths, retail service spaces, and touch-points that draw them into contact. Around this framework, retailers have developed sophisticated data science for observation, identification, and measurement of customers in the context of their shopping behavior. This knowledge supports broad data-driven understanding of customer experiences in physical spaces, economic spaces of decision and choice, persuasive spaces of advertising and branding, and inter-personal spaces of customer-staff interaction.

METHOD

We review the literature on pedestrian and high street retailing, and on urban informatics. We investigate whether the customer journey could be usefully repurposed for urban applications. Specifically, we explore the potential use of the customer journey framework for producing new insight into pedestrian behavior, where a sort of empirical hyperopia has long abounded because data are always in short supply.

RESULTS

Our review addresses how the customer journey might be used as a structure for examining how urban walkers come into contact with the built environment, how people actively and passively sense and perceive ambient city life as they move, how pedestrians make sense of urban context, and how they use this knowledge to build cognition of city streetscapes. Each of these topics has relevance to walking studies specifically, but also to urban science more generally. We consider how retailing might reciprocally benefit from urban science perspectives, especially in extending the reach of retailers' insight beyond store walls, into the retail high streets from which they draw custom.

CONCLUSION

We conclude that a broad set of theoretical frameworks, data collection schemes, and analytical methodologies that have advanced retail data science closer and closer to individual-level acumen might be usefully applied to accomplish the same in urban informatics. However, we caution that differences between retailers' and urban scientists' viewpoints on privacy presents potential controversy.

Analysis of pandemic outdoor recreation and green infrastructure in Nordic cities to enhance urban resilience

Recent empirical research has confirmed the importance of green infrastructure and outdoor recreation to urban people's well-being during the COVID-19 pandemic. However, only a few studies provide cross-city analyses. We analyse outdoor recreation behaviour across four Nordic cities ranging from metropolitan areas to a middle-sized city. We collected map-based survey data from residents (n = 469-4992) in spring 2020 and spatially analyse green infrastructure near mapped outdoor recreation sites and respondents' places of residence. Our statistical examination reveals how the interplay among access to green infrastructure across cities and at respondents' residential location, together with respondents' socio-demographic profiles and lockdown policies or pandemic restrictions, affects outdoor recreation behaviour. The results highlight that for pandemic resilience, the history of Nordic spatial planning is important. To support well-being in exceptional situations as well as in the long term, green infrastructure planning should prioritise nature wedges in and close to cities and support small-scale green infrastructure.

Spatial Syndromic Surveillance and COVID-19 in the U.S.: Local Cluster Mapping for Pandemic Preparedness

Maps have become the de facto primary mode of visualizing the COVID-19 pandemic, from identifying local disease and vaccination patterns to understanding global trends. In addition to their widespread utilization for public communication, there have been a variety of advances in spatial methods created for localized operational needs. While broader dissemination of this more granular work is not commonplace due to the protections under Health Insurance Portability and Accountability Act (HIPAA), its role has been foundational to pandemic response for health systems, hospitals, and government agencies. In contrast to the retrospective views provided by the aggregated geographies found in the public domain, or those often utilized for academic research, operational response requires near real-time mapping based on continuously flowing address level data. This paper describes the opportunities and challenges presented in emergent disease mapping using dynamic patient data in the response to COVID-19 for northeast Ohio for the period 2020 to 2022. More specifically it shows how a new clustering tool developed by geographers in the initial phases of the pandemic to handle operational mapping continues to evolve with shifting pandemic needs, including new variant surges, vaccine targeting, and most recently, testing data shortfalls. This paper also demonstrates how the geographic approach applied provides the framework needed for future pandemic preparedness.

“Small” analysis of Big Data: An evaluation of the effects of social distancing in the United States

This paper proposes a “small” contextual analysis approach to big data and reports our experimental application of this approach in evaluating the effects of social distancing on focused subpopulations in U.S. society. We recognize the common and critical limitations of big data, especially the unrepresentativeness and the unpublished methodology of accessible datasets. Our proposed methodological approach is built upon recent works on data ontology, especially the recognition that big data are essentially remaining digital footprints of human life in need of additional data of contextual factors for valid and meaningful interpretation. It guides the selection and processing of big data to make big data small and structured and thus articulable with traditional social sciences data and usable to conventional social sciences methods. In our experimental case study, we apply our sampling strategy developed from traditional social science data to Google’s mobility dataset for our analysis using primarily a Difference In Difference (DID) model. The results of this case study are of timely value to policy evaluation and public decision-making in the pandemic. We call for more proactive methodological innovations that confront the critical limitations of accessible big data especially in times of urgent needs.

A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland

In this article, we present temporally dynamic population distribution data from the Helsinki Metropolitan Area, Finland, at the level of 250 m by 250 m statistical grid cells. An hourly population distribution dataset is provided for regular workdays (Mon – Thu), Saturdays and Sundays. The data are based on aggregated mobile phone data collected by the biggest mobile network operator in Finland. Mobile phone data are assigned to statistical grid cells using an advanced dasymetric interpolation method based on ancillary data about land cover, buildings and a time use survey. The dataset is validated by comparing population register data from Statistics Finland for night hours and a daytime workplace registry. The resulting 24-hour population data can be used to reveal the temporal dynamics of the city, and examine population variations relevant to spatial accessibility analyses, crisis management, planning and beyond.

Measurement(s)	population distribution
Technology Type(s)	mobile phone • digital curation
Factor Type(s)	geographic location • hour of the day • day of the week
Sample Characteristic - Environment	city
Sample Characteristic - Location	Capital Region • Helsinki

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.17168978

Delineating the geographic context of physical activities: A systematic search and scoping review of the methodological approaches used in social ecological research over two decades

BACKGROUND

The social ecological approach suggests that the spatial context among other factors influence physical activity behavior. Ample research documents physical environmental effects on physical activity. Yet, to date inconsistent associations remain, which might be explained by conceptual and methodological challenges in measuring the spatial dimensions of health behavior. We review methods applied to measure the spatial contexts in the social ecological physical activity literature.

METHODS

Online databases and selected reviews were used to identify papers published between 1990 and 2020. A total of 2167 records were retrieved, from which 412 studies that used physical activity as a primary outcome variable, included measures of the physical environment and applied the main principles of the social ecological approach, were included.

RESULTS

Subjective approaches were the dominant method to capture the spatial context of physical activities. These approaches were applied in 67% (n=279) of the studies. From the objective approaches an administrative unit was most prevalent and was applied in 29% (n=118) of the studies. The most comprehensive objective spatial methods that capture the true environmental exposure, were used only in 2% (n=10) of the studies.

CONCLUSIONS

Current social ecological physical activity research applies simple conceptualizations and methods of the spatial context. While conceptual and methodological concerns have been repeatedly expressed, no substantive progress has been made in the use of spatial approaches. To further our understanding on place effects on health, future studies should carefully consider the choice of spatial approaches, and their effect on study results.

Enclave-Reinforced Inequality during the COVID-19 Pandemic: Evidence from University Campus Lockdowns in Wuhan, China

The COVID-19 pandemic has impacted urban life and created spatial and social inequalities in cities. The impacts of lifting full lockdown restrictions once fast-spreading and community-acquired infection waves were under control are still not fully understood. This study aims to explore spatial inequality reinforced in the intervals between the waves of infection during the COVID-19 pandemic. Enclave-reinforced inequality resulting from enclave-based lockdown policies in Chinese cities was investigated through an analysis of the impacts of university campus enclave closures on the accessibility and crowdedness of urban green spaces. Using a modified two-step floating catchment area (2SFCA) and inversed 2SFCA (i2SFCA) method, accessibility and crowdedness were calculated and compared under two different scenarios. Additionally, the Lorenz curve, Gini coefficient, and Theil index were used to measure and compare intra-city global and local inequalities under each scenario. The results indicate that the lockdown of university campus enclaves decreased the supply of urban green spaces. Campus closures not only exacerbated the unequal distribution of urban green space, but also reduced the inequality of crowdedness in urban parks due to increased crowdedness in parks near the closed enclaves. Moreover, both accessibility and crowdedness worsened when the calculations were weighted for population size and the total supply of green space. Enclave-based lockdown in cities reinforced spatial inequality, and it is highly complex and has multidimensional impacts on urban inequalities and environmental injustice which should be considered by urban planners and decision-makers hoping to create healthy, inclusive, resilient, and sustainable cities in the “new normal” of the COVID-19 pandemic.

Applying contextual integrity to digital contact tracing and automated triage for hospitals during COVID-19

To control and minimise the spread of COVID-19, various technological solutions have been proposed. In this research, we focus on digital contact tracing and automated triage for hospitals. We conducted an online survey in Flanders (N = 1708) to investigate the perceived appropriateness of these systems based on the Contextual Integrity framework, as developed by Nissenbaum [1]. For digital contact tracing, significant differences were found between the appropriateness of using various types of data for different goals. Precise individual location data (i.e. GPS) was considered to be least appropriate and much less appropriate than proximity data (i.e. Bluetooth) or coarser location data (i.e. GSM). Goals for digital contact tracing with a high individual impact were considered to be less appropriate than goals with a low individual or societal impact. In addition, the data showed that respondents would find the usage of digital contact tracing to be less appropriate after the pandemic, underlining the temporality of this technological solution. For automated triage, the results indicated that gender is perceived to be significantly less appropriate than the other types of data, including age, to determine the priority of treatment.

Mobile Apps to Fight the COVID-19 Crisis

The COVID-19 pandemic led to a multi-faceted global crisis, which triggered the diverse and quickly emerging use of old and new digital tools. We have developed a multi-channel approach for the monitoring and analysis of a subset of such tools, the COVID-19 related mobile applications (apps). Our approach builds on the information available in the two most prominent app stores (i.e., Google Play for Android-powered devices and Apple’s App Store for iOS-powered devices), as well as on relevant tweets and digital media outlets. The dataset presented here is one of the outcomes of this approach, uses the content of the app stores and enriches it, providing aggregated information about 837 mobile apps published across the world to fight the COVID-19 crisis. This information includes: (a) information available in the mobile app stores between 20 April 2020 and 2 August 2020; (b) complementary information obtained from manual analysis performed until mid-September 2020; and (c) status information about app availability on 28 February 2021, when we last collected data from the mobile app stores. We highlight our findings with a series of descriptives, which depict both the activities in the app stores and the qualitative information that was revealed by the manual analysis.

Outdoor recreation and nature's contribution to well-being in a pandemic situation - Case Turku, Finland

Urban green infrastructure provides a range of experiences for people and various health benefits that support human well-being. To increase urban resilience, exceptional situations, such as the COVID-19 pandemic, are important to learn from. This study aims to understand how the residents in Turku, a middle-sized city in Finland, perceived their outdoor recreation changed and how nature contributed to their subjective well-being during the early phases of the COVID-19. Sites of outdoor recreation and associated ecosystem service benefits were gathered through a map-based survey. In addition, the contribution of nature on subjective well-being was measured through Likert scale statements and the perceived changes in outdoor recreation behaviour were measured through self-reported number of days and from responses to open survey questions. Data was analysed through quantitative, qualitative and spatial methods. The results show that nearly half of the respondents increased outdoor recreation and the majority of outdoor recreation sites were visited more or as often as before the pandemic. The spatial analysis revealed that the most often visited recreation sites were near forests, semi-natural areas and housing areas as well as relatively close to respondent's residence. Respondents had various reasons for changes in outdoor recreation behaviour. For some a shift to working remotely and changes in everyday routines led to spending time outdoors more often and for some spending less while others avoided recreation in crowded areas due to social distancing. The results also indicate that people's opportunities to adapt to the pandemic conditions differ greatly. The nature's contribution to subjective well-being during COVID-19 was important regardless of respondent's outdoor recreation behaviour. Our study highlights that urban planning should respond to different needs for outdoor recreation in order to widely, and in a just way, promote the well-being benefits of urban nature during a pandemic, and to increase the resilience of the city and its residents. Participatory mapping can capture the variety in resident's values and identify key recreation sites of multiple ecosystem service benefits.

Citizen Science Data to Measure Human Use of Green Areas and Forests in European Cities

Understanding and explaining the use of green spaces and forests is challenging for sustainable urban planning. In recent years there has been increasing demand for novel approaches to investigate urban green infrastructure by capitalizing on large databases from existing citizen science tools. In this study, we analyzed iNaturalist data to perform an assessment of the intentional use of these urban spaces for their value and to understand the main drivers. We retrieved the total number of observations obtained across a set of 672 European cities and focused on reporting from mapped green areas and forests. We used two separate multivariate explanatory models to investigate which factors explained variations in the number of observations for green areas and forests. We found a relatively heterogeneous use of these two urban green spaces. Gross domestic product was important in explaining the number of visits. Availability and accessibility also had positive relationships with the use of green areas and forests in cities, respectively. This study paves the way for better integration of citizen science data in assessing cultural services provided by urban green infrastructure and therefore in supporting the evaluation of spatial planning policies for the sustainable development of urban areas.

Socioeconomic Conditioning of the Development of the COVID-19 Pandemic and Its Global Spatial Differentiation

The COVID pandemic very quickly became the world's most serious social and economic problem. This paper's focus is on the spatial aspect of its spread, with the aims being to point to spatial conditioning underpinning development of the pandemic, and to identify and assess possible socio-economic features exerting an impact on that. Particular attention has been paid to the percentage of positive tests for the presence of the coronavirus, as well as mortality due to the disease it causes. The statistics used relate to 102 countries, with the research for each extending from the time first cases of COVID-19 were reported through to 18 November 2020. The focus of investigation has been the stochastic co-occurrence of both a morbidity index and a mortality index, with intentionally selected socio-economic variables. Results have then been summarized through the classification of countries in relation to the two indices. Highest values relate to Latin America. A significant co-occurrence of morbidity and mortality with GDP per capita has been identified, as values for the indices are found to be lower in wealthier countries. The basic conclusion is that the dependency of the pandemic on environmental and socio-economic conditioning became more complex and ambiguous, while also being displaced gradually as concrete political decisions came to be taken.

Exploring the Roles of Local Mobility Patterns, Socioeconomic Conditions, and Lockdown Policies in Shaping the Patterns of COVID-19 Spread

The COVID-19 crisis has shown that we can only prevent the risk of mass contagion through timely, large-scale, coordinated, and decisive actions. This pandemic has also highlighted the critical importance of generating rigorous evidence for decision-making, and actionable insights from data, considering further the intricate web of causes and drivers behind observed patterns of contagion diffusion. Using mobility, socioeconomic, and epidemiological data recorded throughout the pandemic development in the Santiago Metropolitan Region, we seek to understand the observed patterns of contagion. We characterize human mobility patterns during the pandemic through different mobility indices and correlate such patterns with the observed contagion diffusion, providing data-driven models for insights, analysis, and inferences. Through these models, we examine some effects of the late application of mobility restrictions in high-income urban regions that were affected by high contagion rates at the beginning of the pandemic. Using augmented synthesis control methods, we study the consequences of the early lifting of mobility restrictions in low-income sectors connected by public transport to high-risk and high-income communes. The Santiago Metropolitan Region is one of the largest Latin American metropolises with features that are common to large cities. Therefore, it can be used as a relevant case study to unravel complex patterns of the spread of COVID-19.

The association between socioeconomic status and mobility reductions in the early stage of England's COVID-19 epidemic

This study uses mobile phone data to examine how socioeconomic status was associated with the extent of mobility reduction during the spring 2020 lockdown in England in a manner that considers both potentially confounding effects and spatial dependency and heterogeneity. It shows that socioeconomic status as approximated through income and occupation was strongly correlated with the extent of mobility reduction. It also demonstrates that the specific nature of the association of socioeconomic status with mobility reduction varied markedly across England. Finally, the analysis suggests that the spatial differentiation in the ability to restrict everyday mobility in response to a national lockdown is an important topic for future research.

Escaping from Cities during the COVID-19 Crisis: Using Mobile Phone Data to Trace Mobility in Finland

The coronavirus disease 2019 (COVID-19) crisis resulted in unprecedented changes in the spatial mobility of people across societies due to the restrictions imposed. This also resulted in unexpected mobility and population dynamics that created a challenge for crisis preparedness, including the mobility from cities during the crisis due to the underlying phenomenon of multi-local living. People changing their residences can spread the virus between regions and create situations in which health and emergency services are not prepared for the population increase. Here, our focus is on urban–rural mobility and the influence of multi-local living on population dynamics in Finland during the COVID-19 crisis in 2020. Results, based on three mobile phone datasets, showed a significant drop in inter-municipal mobility and a shift in the presence of people—a population decline in urban centres and an increase in rural areas, which is strongly correlated to secondary housing. This study highlights the need to improve crisis preparedness by: (1) acknowledging the growing importance of multi-local living, and (2) improving the use of novel data sources for monitoring population dynamics and mobility. Mobile phone data products have enormous potential, but attention should be paid to the varying methodologies and their possible impact on analysis.