Unlabelled:The COVID-19 pandemic served as an important test case of complementing traditional public health data with nontraditional data, such as mobility traces, social media activity, and wearable data, to inform real-time decision-making. Drawing on an expert workshop and a targeted survey of epidemic modelers in Europe, this study assesses the promise and the persistent limitations of such data in pandemic preparedness and response. We distinguish between "first-mile" challenges (obstacles to accessing and harmonizing data) and "last-mile" challenges (difficulties in translating insights into actionable policy interventions). The expert workshop, convened in March 2024 in Brussels, brought together 50 participants, including public health professionals, data scientists, policymakers, and industry leaders, to reflect on lessons learned and define strategies for better integration of nontraditional data into epidemic modeling and policymaking. The accompanying survey, gathering experiences from 29 modelers, offers empirical evidence of the barriers faced by modelers during the COVID-19 pandemic and highlights areas where key data were unavailable or underused. The experiences collected through the survey and workshop resulted in ten key actions and three overarching recommendations for public entities, data providers, and stakeholders. Our findings reveal ongoing issues with data access, quality, and interoperability, as well as institutional and cognitive barriers to evidence-based decision-making. Approximately 66% of all datasets had at least one access problem, with data sharing reluctance for nontraditional sources being double that of traditional data (30% vs 15%). Only 10% of respondents reported that they could use all the data they needed. These limitations included issues related to timeliness and granularity of data, as well as issues with linkage, comparability, and biases. To overcome these hurdles, we propose a set of enabling mechanisms, including data inventories, standardization protocols, simulation exercises, data stewardship roles, and data collaboratives. For first-mile challenges, solutions focus on technical and legal frameworks for data access. For last-mile challenges, we recommend fusion centers, decision accelerator laboratories, and networks of scientific ambassadors to bridge the gap between analysis and action. We argue that realizing the full value of nontraditional data requires a sustained investment in institutional readiness, cross-sectoral collaboration, and a shift toward a culture of data solidarity. Grounded in the lessons of the COVID-19 pandemic, the study can be used to design a roadmap for using nontraditional data to confront a broader array of public health emergencies, from climate shocks to humanitarian crises.

Wildfires are becoming more frequent and intense, leading to increased evacuation events that disrupt mobility and socioeconomic structures, impacting access to resources, employment, and housing. Understanding the interplay between these factors is crucial for developing effective mitigation and adaptation strategies. We analyse evacuation patterns during the wildfires that occurred in Valparaíso, Chile, on February 2-3, 2024, using high-definition mobile phone records. Applying a causal inference approach combining regression discontinuity and difference-in-differences, we focus on socioeconomic stratification to isolate the wildfire impact on different groups. We find that many people spent nights away from home, with the lowest socioeconomic group staying away the longest. Overall, people reduced their mean and median night-to-night travel distances during the evacuation. Movements initially became irregular but later concentrated in areas of similar socioeconomic status. Finally, we demonstrate a comparability potential of the mobile phone records to the Facebook Disaster Maps, although the latter have a coarse time resolution and are generated only after the wildfire onset. Our results highlight the role of socioeconomic differences in evacuation dynamics, offering valuable insights for response planning.

This study examines behavioral responses to mobile phone evacuation alerts during the February 2024 wildfires in Valparaíso, Chile. Using anonymized mobile network data from 580,000 devices, we analyze population movement following emergency SMS notifications. Results reveal three key patterns: (1) initial alerts trigger immediate evacuation responses with connectivity dropping by 80% within 1.5 hours, while subsequent messages show diminishing effects; (2) substantial evacuation also occurs in non-warned areas, indicating potential transportation congestion; (3) socioeconomic disparities exist in evacuation timing, with high-income areas evacuating faster and showing less differentiation between warned and non-warned locations. Statistical modeling demonstrates socioeconomic variations in both evacuation decision rates and recovery patterns. These findings inform emergency communication strategies for climate-driven disasters, highlighting the need for targeted alerts, socioeconomically calibrated messaging, and staged evacuation procedures to enhance public safety during crises.

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.

Understanding mobile-user behavior requires joint modeling of mobility and traffic, as data consumption is shaped by where, when, and how users travel. Despite this clear intuition, most studies still treat the two in isolation, missing the intricate dependencies between them at the individual level. This paper propose a novel approach that explicitly captures the interplay between traffic and mobility behaviors using fine-grained mobile datasets. Using week-long eXtended Data Records (XDRs), we identify 13 interpretable features and pinpoint the mobility traits that truly drive traffic variation. These insights support a privacy-preserving user abstraction that represents each timeline as a sequence of discrete mobility-traffic states, capturing temporal dynamics and heterogeneity while generalizing across regions. We then introduce a probabilistic likelihood model that scores any mobility-traffic pairing, enabling cross-modality prediction and statistically sound fusion of fragmented logs. Experiments on four provincial datasets covering 1.3 million Chilean users show that the model reliably separates plausible from implausible behavior and generalizes from dense urban cores to mixed rural-urban contexts. The framework is descriptive, generative, and transferable, paving the way for anomaly detection, personalized QoE adaptation, and realistic network simulation.

Non-pharmaceutical interventions (NPIs) are essential for controlling infectious diseases during pre-vaccine periods, yet their success hinges on sustained public adherence. This study investigates adherence dynamics to tiered restriction systems implemented during COVID-19 in six geographical regions across Europe, North America, Africa, and South America. Using daily mobility data and linear-mixed models, we assessed three types of fatigue: overall fatigue (linked to cumulative time under restrictions), tier fatigue (linked to time spent under a specific tier), and iteration fatigue (linked to repeated implementation of the same tier). Tier fatigue caused the most rapid adherence loss, producing effects within days that overall fatigue required months of restrictions to achieve. Iterative application of shorter NPIs, interspersed with temporary relaxation, helped reset adherence, mitigating fatigue and sometimes even improving compliance. Psychological relief and a sense of regained autonomy during relaxation periods may renew public willingness to comply when restrictions are reintroduced. These findings emphasize the dual benefits of short, strategic NPIs for epidemic control and public resilience, offering actionable insights for designing more sustainable pandemic interventions. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement The authors thank Shweta Bansal for useful discussions on this study. The research was partially supported by: ANR grant DATAREDUX (ANR-19-CE46-0008-03) to LDD, CES, VC; EU Horizon 2020 grant MOOD (H2020-874850, publication cataloged as MOOD 124) to AR, CES, VC; EU Horizon Europe grant VERDI (101045989) to VC; EU Horizon Europe grant ESCAPE (101095619) to AR, VC; Telefonica R&D Chile and CISCO Chile to LF; FONDECYT Grant N1221315 to LF; Lagrange project of the ISI Foundation funded by Fondazione CRT to LF. The contents of this publication are the sole responsibility of the authors and don't necessarily reflect the views of the European Commission. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The details of the IRB/oversight body that provided approval or exemption for the research described are given below: This study utilises anonymized, unidentifiable data collected from individual mobile phone devices and publicly available aggregated case count data in Chile. It does not use or access individual personal information, and has therefore been waived for an ethical approval. This decision was made by the Institutional Ethics Committee at Universidad del Desarrollo, Santiago de Chile, Chile, communicated to the authors on March 30, 2022. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data from Google mobility reports are publicly available. Mobility data for Chile produced in the present study are available upon reasonable request to the authors.

Mobile devices have become essential for capturing human activity, and eXtended Data Records (XDRs) offer rich opportunities for detailed user behavior modeling, which is useful for designing personalized digital services. Previous studies have primarily focused on aggregated mobile traffic and mobility analyses, often neglecting individual-level insights. This paper introduces a novel approach that explores the dependency between traffic and mobility behaviors at the user level. By analyzing 13 individual features that encompass traffic patterns and various mobility aspects, we enhance the understanding of how these behaviors interact. Our advanced user modeling framework integrates traffic and mobility behaviors over time, allowing for fine-grained dependencies while maintaining population heterogeneity through user-specific signatures. Furthermore, we develop a Markov model that infers traffic behavior from mobility and vice versa, prioritizing significant dependencies while addressing privacy concerns. Using a week-long XDR dataset from 1,337,719 users across several provinces in Chile, we validate our approach, demonstrating its robustness and applicability in accurately inferring user behavior and matching mobility and traffic profiles across diverse urban contexts.

Crime and violence shape psychological and sociological perceptions, fostering a sense of insecurity, especially in urban settings. This perception significantly alters lifestyles, routines, and social interactions. In this study, it is conducted an empirical analysis of the relationship between personal feelings of insecurity and the way individuals move in their daily lives, with a particular focus on differences between genders. The methodology used combines subjective data gathered from individuals’ reported perceptions of insecurity with objective data derived from digital mobile phone tracking, providing a comprehensive view of how these fears affect people’s daily routines and mobility patterns. The results highlight that perceived insecurity is significantly related to a lower mobility of individuals of both genders. This effect is more pronounced in women, reflecting significant gender-based differences in the impact of perceived insecurity on daily mobility. The findings, revealing higher levels of insecurity and fear of crime among women, require policy action. Public policy must prioritize making urban spaces, such as bus stops, squares, parks, sports courts, and streets, safer and more welcoming for women. This approach is essential to create an urban environment that is inclusive, secure, and conducive to the well-being of all its inhabitants.

The global SARS-CoV-2 pandemic prompted nations to implement mobility limitations to curb virus spread. In Chile, targeted interregional measures were employed to mitigate the social and economic costs. Here, we employ a novel real-time methodology to assess the impact of such mobility restrictions on epidemic control. Leveraging telecom-derived eXtended Detail Records (XDR) and official COVID-19 epidemiological data, we estimate interregional mobility and disease prevalence. Employing Bayesian adjustments, we compare different mobility restriction scenarios: business-as-usual (BAU), initial measures, and total lockdown. Mobility reductions significantly curtailed cases and risk across regions. Even modest mobility declines under total lockdowns considerably lowered imported cases. The high-risk Santiago Region, a national source of infections to other regions, demonstrated lowered risk due to mobility restrictions. Our approach facilitates rapid regional insights for informed policy responses.### Competing Interest StatementThe authors have declared no competing interest.### Funding StatementF and LB thank the funding and support of Telefonica R&D Chile and CISCO Chile. This research was supported by FONDECYT Grant No. 1130902 to Loreto Bravo and FONDECYT Grant No. 1221315 to Leo Ferres. BG acknowledges funding from the Oxford Martin School Pandemic Genomics programme and the European Union Horizon 2020 MOOD (#874850). LF also acknowledges financial support from the Lagrange Project of the Institute for Scientific Interchange Foundation (ISI Foundation), funded by Fondazione Cassa di Risparmio di Torino (Fondazione CRT).### Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:This study utilises anonymized, unidentifiable data collected from individual mobile phone devices and publicly available aggregated case count data in Chile. It does not use or access individual personal information, and has therefore been waived for an ethical approval. This decision was made by the Institutional Ethics Committee at Universidad del Desarrollo, Santiago de Chile, Chile, communicated to the authors on March 30, 2022.I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.YesData and code related to the analysis and figures in tis manuscript are publicly available at GitHub. Raw mobility data from individual mobile phones cannot be made publicly available to protect the individual privacy of users.[https://github.com/jesusfberrios/covid\_rt\_reg_restr.git][1] [1]: https://github.com/jesusfberrios/covid_rt_reg_restr.git

Human mobility is strongly associated with the spread of SARS-CoV-2 via air travel on an international scale and with population mixing and the number of people moving between locations on a local scale. However, these conclusions are drawn mostly from observations in the context of the global north where international and domestic connectivity is heavily influenced by the air travel network; scenarios where land-based mobility can also dominate viral spread remain understudied. Furthermore, research on the effects of nonpharmaceutical interventions (NPIs) has mostly focused on national- or regional-scale implementations, leaving gaps in our understanding of the potential benefits of implementing NPIs at higher granularity. Here, we use Chile as a model to explore the role of human mobility on disease spread within the global south; the country implemented a systematic genomic surveillance program and NPIs at a very high spatial granularity. We combine viral genomic data, anonymized human mobility data from mobile phones and official records of international travelers entering the country to characterize the routes of importation of different variants, the relative contributions of airport and land border importations, and the real-time impact of the country's mobility network on the diffusion of SARS-CoV-2. The introduction of variants which are dominant in neighboring countries (and not detected through airport genomic surveillance) is predicted by land border crossings and not by air travelers, and the strength of connectivity between comunas (Chile's lowest administrative divisions) predicts the time of arrival of imported lineages to new locations. A higher stringency of local NPIs was also associated with fewer domestic viral importations. Our analysis sheds light on the drivers of emerging respiratory infectious disease spread outside of air travel and on the consequences of disrupting regular movement patterns at lower spatial scales.

In this study, we conduct a detailed empirical analysis of the relationship between personal feelings of insecurity, fear of crime, and the way individuals move and travel in their daily lives, with a particular focus on differences between genders. Our methodology combines subjective data gathered from individuals' reported perceptions of insecurity with objective data derived from digital mobile phone tracking, providing a comprehensive view of how these fears affect people's daily routines and travel patterns. The results of our research highlight that perceived insecurity significantly limits the mobility of individuals from both genders. However, this effect is more acute in women, indicating notable gender-based differences in the impact of perceived insecurity on day-to-day movements. The findings, revealing higher levels of insecurity and fear of crime among women, necessitate urgent policy action. Public policy must prioritize making public spaces, such as bus stops, squares, parks, sports courts, and streets, safer and more welcoming for women. This approach is essential for creating an urban environment that is inclusive, secure, and conducive to the well-being of all its inhabitants.

This study leverages mobile data for 5.4 million users to unveil the complex dynamics of daily mobility and longer-term relocations in and from Santiago, Chile, during the COVID-19 pandemic, focusing on socioeconomic differentials. We estimated a relative increase in daily mobility, in 2020, for lower-income compared to higher-income regions. In contrast, longer-term relocation rose primarily among higher-income groups. These shifts indicate nuanced responses to the pandemic across socioeconomic classes. Compared to 2017, economic factors in 2020 had a stronger influence on the decision to relocate and the selection of destinations, suggesting transformations in mobility behaviors. Contrary to previously held beliefs, there was no evidence supporting a preference for rural over urban destinations, despite the surge in emigration from Santiago during the pandemic. This study enhances our understanding of how varying socioeconomic conditions interact with mobility decisions during crises and provides insights for policymakers aiming to enact fair and evidence-based measures in rapidly changing circumstances.

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.

Despite increased global attention on violence against women, understanding the factors that lead to women becoming victims remains a critical challenge. Notably, the impact of domestic violence on women's mobility—a critical determinant of their social and economic independence—has remained largely unexplored. This study bridges this gap, employing police records to quantify physical and psychological domestic violence, while leveraging mobile phone data to proxy women's mobility. Our analyses reveal a negative correlation between physical violence and female mobility, an association that withstands robustness checks, including controls for economic independence variables like education, employment, and occupational segregation, bootstrapping of the data set, and applying a generalized propensity score matching identification strategy. The study emphasizes the potential causal role of physical violence on decreased female mobility, asserting the value of interdisciplinary research in exploring such multifaceted social phenomena to open avenues for preventive measures. The implications of this research extend into the realm of public policy and intervention development, offering new strategies to combat and ultimately eradicate domestic violence against women, thereby contributing to wider efforts toward gender equity.

We study the spatio-temporal spread of SARS-CoV-2 in Santiago de Chile using anonymized mobile phone data from 1.4 million users, 22% of the whole population in the area, characterizing the effects of non-pharmaceutical interventions (NPIs) on the epidemic dynamics. We integrate these data into a mechanistic epidemic model calibrated on surveillance data. As of August 1, 2020, we estimate a detection rate of 102 cases per 1000 infections (90% CI: [95–112 per 1000]). We show that the introduction of a full lockdown on May 15, 2020, while causing a modest additional decrease in mobility and contacts with respect to previous NPIs, was decisive in bringing the epidemic under control, highlighting the importance of a timely governmental response to COVID-19 outbreaks. We find that the impact of NPIs on individuals’ mobility correlates with the Human Development Index of comunas in the city. Indeed, more developed and wealthier areas became more isolated after government interventions and experienced a significantly lower burden of the pandemic. The heterogeneity of COVID-19 impact raises important issues in the implementation of NPIs and highlights the challenges that communities affected by systemic health and social inequalities face adapting their behaviors during an epidemic.

Home detection, assigning a phone device to its home antenna, is a ubiquitous part of most studies in the literature on mobile phone data. Despite its widespread use, home detection relies on a few assumptions that are difficult to check without ground truth, i.e., where the individual that owns the device resides. In this paper, we provide an unprecedented evaluation of the accuracy of home detection algorithms on a group of sixty-five participants for whom we know their exact home address and the antennas that might serve them. Besides, we analyze not only Call Detail Records (CDRs) but also two other mobile phone streams: eXtended Detail Records (XDRs, the ``data'' channel) and Control Plane Records (CPRs, the network stream). These data streams vary not only in their temporal granularity but also they differ in the data generation mechanism', e.g., CDRs are purely human-triggered while CPR is purely machine-triggered events. Finally, we quantify the amount of data that is needed for each stream to carry out successful home detection for each stream. We find that the choice of stream and the algorithm heavily influences home detection, with an hour-of-day algorithm for the XDRs performing the best, and with CPRs performing best for the amount of data needed to perform home detection. Our work is useful for researchers and practitioners in order to minimize data requests and to maximize the accuracy of home antenna location.

The always increasing mobile connectivity affects every aspect of our daily lives, including how and when we keep ourselves informed and consult news media. By studying a DPI (deep packet inspection) dataset, provided by one of the major Chilean telecommunication companies, we investigate how different cohorts of the population of Santiago De Chile consume news media content through their smartphones. We find that some socio-demographic attributes are highly associated to specific news media consumption patterns. In particular, education and age play a significant role in shaping the consumers behaviour even in the digital context, in agreement with a large body of literature on off-line media distribution channels.

The power of the press to shape the informational landscape of a population is unparalleled, even now in the era of democratic access to all information outlets. However, it is known that news outlets (particularly more traditional ones) tend to discriminate who they want to reach, and who to leave aside. In this work, we attempt to shed some light on the audience targeting patterns of newspapers, using the Chilean media ecosystem. First, we use the gravity model to analyze geography as a factor in explaining audience reachability. This shows that some newspapers are indeed driven by geographical factors (mostly local news outlets) but some others are not (national-distribution outlets). For those which are not, we use a regression model to study the influence of socioeconomic and political characteristics in news outlets adoption. We conclude that indeed larger, national-distribution news outlets target populations based on these factors, rather than on geography or immediacy.

Since 2016, through an association between Telefónica R&D and the Institute of Data Science in Chile, a group of researchers and myself have been working with trillions of digital traces left behind when people use their mobile phones. All of this work has been done under the general umbrella term of "data science for social good", and we have worked on anything from population displacement after external events like earthquakes, how people started using public spaces after the introduction of a popular mobile game, to actual social inclusion of people of different socio-economic backgrounds mixing in shopping malls or reading certain kinds of news, or patterns arising from gendered data sets. We will show how data in the private sector made us learn important social lessons such as how parks can become more secure when people went out to play Pokemon Go, how certain malls are hubs of social inclusion, how gender segregates the city and how different demographics keep themselves in their own informational filter bubble. However, even after all this benefits, the relationship with industry has never been fluid, and involves a lot of small and not so small compromises and "battles". In this talk, I will present a technical history of the work we've done with X/CDRs for social good including practical aspects of accessing and sharing data, the balance of research and industrial innovation, and issues of transactions costs while still providing value for the company itself, government, the university and society. I will also recount experiences about what it meant for a company like Telefónica and a research university like us to travel together in a very interesting context of huge data, incredible insights, privacy considerations, money, corporate interests, university expectations, and data-driven discovery.