Unconventional data, unprecedented insights: leveraging non-traditional data during a pandemic.

Introduction: The COVID-19 pandemic prompted new interest in non-traditional data sources to inform response efforts and mitigate knowledge gaps. While non-traditional data offers some advantages over traditional data, it also raises concerns related to biases, representativity, informed consent and security vulnerabilities. This study focuses on three specific types of non-traditional data: mobility, social media, and participatory surveillance platform data. Qualitative results are presented on the successes, challenges, and recommendations of key informants who used these non-traditional data sources during the COVID-19 pandemic in Spain and Italy. Methods: A qualitative semi-structured methodology was conducted through interviews with experts in artificial intelligence, data science, epidemiology, and/or policy making who utilized non-traditional data in Spain or Italy during the pandemic. Questions focused on barriers and facilitators to data use, as well as opportunities for improving utility and uptake within public health. Interviews were transcribed, coded, and analyzed using the framework analysis method. Results: Non-traditional data proved valuable in providing rapid results and filling data gaps, especially when traditional data faced delays. Increased data access and innovative collaborative efforts across sectors facilitated its use. Challenges included unreliable access and data quality concerns, particularly the lack of comprehensive demographic and geographic information. To further leverage non-traditional data, participants recommended prioritizing data governance, establishing data brokers, and sustaining multi-institutional collaborations. The value of non-traditional data was perceived as underutilized in public health surveillance, program evaluation and policymaking. Participants saw opportunities to integrate them into public health systems with the necessary investments in data pipelines, infrastructure, and technical capacity. Discussion: While the utility of non-traditional data was demonstrated during the pandemic, opportunities exist to enhance its impact. Challenges reveal a need for data governance frameworks to guide practices and policies of use. Despite the perceived benefit of collaborations and improved data infrastructure, efforts are needed to strengthen and sustain them beyond the pandemic. Lessons from these findings can guide research institutions, multilateral organizations, governments, and public health authorities in optimizing the use of non-traditional data.

Measuring Marine Biotoxins in a Hypersaline Coastal Lagoon.

Marine biotoxins have posed a persistent problem along various coasts for many years. Coastal lagoons are ecosystems prone to phytoplankton blooms when altered by eutrophication. The Mar Menor is the largest hypersaline coastal lagoon in Europe. Sixteen marine toxins, including lipophilic toxins, yessotoxins, and domoic acid (DA), in seawater samples from the Mar Menor coastal lagoon were measured in one year. Only DA was detected in the range of 44.9-173.8 ng L-1. Environmental stressors and mechanisms controlling the presence of DA in the lagoon are discussed. As an enrichment and clean-up method, we employed solid phase extraction to filter and acidify 75 mL of the sample, followed by pre-concentration through a C18 SPE cartridge. The analytes were recovered in aqueous solutions and directly injected into the liquid chromatography system (LC-MS), which was equipped with a C18 column. The system operated in gradient mode, and we used tandem mass spectrometry (MS/MS) with a triple quadrupole (QqQ) in the multiple reaction monitoring mode (MRM) for analysis. The absence of matrix effects was checked and the limits of detection for most toxins were low, ranging from 0.05 to 91.2 ng L-1, depending on the compound. To validate the measurements, we performed recovery studies, falling in the range of 74-122%, with an intraday precision below 14.9% RSD.

Predicting COVID-19 pandemic waves including vaccination data with deep learning.

Introduction: During the recent COVID-19 pandemics, many models were developed to predict the number of new infections. After almost a year, models had also the challenge to include information about the waning effect of vaccines and by infection, and also how this effect start to disappear. Methods: We present a deep learning-based approach to predict the number of daily COVID-19 cases in 30 countries, considering the non-pharmaceutical interventions (NPIs) applied in those countries and including vaccination data of the most used vaccines. Results: We empirically validate the proposed approach for 4 months between January and April 2021, once vaccination was available and applied to the population and the COVID-19 variants were closer to the one considered for developing the vaccines. With the predictions of new cases, we can prescribe NPIs plans that present the best trade-off between the expected number of COVID-19 cases and the social and economic cost of applying such interventions. Discussion: Whereas, mathematical models which include the effect of vaccines in the spread of the SARS-COV-2 pandemic are available, to the best of our knowledge we are the first to propose a data driven method based on recurrent neural networks that considers the waning effect of the immunization acquired either by vaccine administration or by recovering from the illness. This work contributes with an accurate, scalable, data-driven approach to modeling the pandemic curves of cases when vaccination data is available.

COVID-19 outbreaks analysis in the Valencian Region of Spain in the prelude of the third wave.

Introduction: The COVID-19 pandemic has led to unprecedented social and mobility restrictions on a global scale. Since its start in the spring of 2020, numerous scientific papers have been published on the characteristics of the virus, and the healthcare, economic and social consequences of the pandemic. However, in-depth analyses of the evolution of single coronavirus outbreaks have been rarely reported. Methods: In this paper, we analyze the main properties of all the tracked COVID-19 outbreaks in the Valencian Region between September and December of 2020. Our analysis includes the evaluation of the origin, dynamic evolution, duration, and spatial distribution of the outbreaks. Results: We find that the duration of the outbreaks follows a power-law distribution: most outbreaks are controlled within 2 weeks of their onset, and only a few last more than 2 months. We do not identify any significant differences in the outbreak properties with respect to the geographical location across the entire region. Finally, we also determine the cluster size distribution of each infection origin through a Bayesian statistical model. Discussion: We hope that our work will assist in optimizing and planning the resource assignment for future pandemic tracking efforts.

Social isolation during the COVID-19 pandemic in Spain: a population study.

Since March of 2020, billions of people worldwide have been asked to limit their social contacts in an effort to contain the spread of the SARS-CoV-2 virus. However, little research has been carried out to date on the impact of such social distancing measures on the social isolation levels of the population. In this paper, we study the impact of the pandemic on the social isolation of the Spanish population, by means of 32,359 answers to a citizen survey collected over a period of 7 months. We uncover (1) a significant increase in the prevalence of social isolation in the population, reaching almost 26%; (2) gender and age differences, with the largest prevalence of isolation among middle-aged individuals; (3) a strong relationship between economic impact and social isolation; and (4) differences in social isolation, depending on the number of COVID-19 protection measures and on the perception of coronavirus infection risk by our participants. Our research sheds quantitative light on the sociological impact of the pandemic, and enables us to identify key factors in the interplay between the deployment of non-pharmaceutical interventions to contain the spread of an infectious disease and a population's levels of social isolation.

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

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

Key factors affecting people's unwillingness to be confined during the COVID-19 pandemic in Spain: a large-scale population study.

Population confinements have been one of the most widely adopted non-pharmaceutical interventions (NPIs) implemented by governments across the globe to help contain the spread of the SARS-CoV-2 virus. While confinement measures have been proven to be effective to reduce the number of infections, they entail significant economic and social costs. Thus, different policy makers and social groups have exhibited varying levels of acceptance of this type of measures. In this context, understanding the factors that determine the willingness of individuals to be confined during a pandemic is of paramount importance, particularly, to policy and decision-makers. In this paper, we study the factors that influence the unwillingness to be confined during the COVID-19 pandemic by the means of a large-scale, online population survey deployed in Spain. We perform two types of analyses (logistic regression and automatic pattern discovery) and consider socio-demographic, economic and psychological factors, together with the 14-day cumulative incidence per 100,000 inhabitants. Our analysis of 109,515 answers to the survey covers data spanning over a 5-month time period to shed light on the impact of the passage of time. We find evidence of pandemic fatigue as the percentage of those who report an unwillingness to be in confinement increases over time; we identify significant gender differences, with women being generally less likely than men to be able to sustain long-term confinement of at least 6 months; we uncover that the psychological impact was the most important factor to determine the willingness to be in confinement at the beginning of the pandemic, to be replaced by the economic impact as the most important variable towards the end of our period of study. Our results highlight the need to design gender and age specific public policies, to implement psychological and economic support programs and to address the evident pandemic fatigue as the success of potential future confinements will depend on the population's willingness to comply with them.

Ethical machines: The human-centric use of artificial intelligence.

Today's increased availability of large amounts of human behavioral data and advances in artificial intelligence (AI) are contributing to a growing reliance on algorithms to make consequential decisions for humans, including those related to access to credit or medical treatments, hiring, etc. Algorithmic decision-making processes might lead to more objective decisions than those made by humans who may be influenced by prejudice, conflicts of interest, or fatigue. However, algorithmic decision-making has been criticized for its potential to lead to privacy invasion, information asymmetry, opacity, and discrimination. In this paper, we describe available technical solutions in three large areas that we consider to be of critical importance to achieve a human-centric AI: (1) privacy and data ownership; (2) accountability and transparency; and (3) fairness. We also highlight the criticality and urgency to engage multi-disciplinary teams of researchers, practitioners, policy makers, and citizens to co-develop and evaluate in the real-world algorithmic decision-making processes designed to maximize fairness, accountability, and transparency while respecting privacy.

Give more data, awareness and control to individual citizens, and they will help COVID-19 containment.

The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the "phase 2" of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens' privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking. We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens' "personal data stores", to be shared separately and selectively (e.g., with a backend system, but possibly also with other citizens), voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity. This approach better protects the personal sphere of citizens and affords multiple benefits: it allows for detailed information gathering for infected people in a privacy-preserving fashion; and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. The decentralized approach is also scalable to large populations, in that only the data of positive patients need be handled at a central level. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allow the user to share spatio-temporal aggregates-if and when they want and for specific aims-with health authorities, for instance. Second, we favour a longer-term pursuit of realizing a Personal Data Store vision, giving users the opportunity to contribute to collective good in the measure they want, enhancing self-awareness, and cultivating collective efforts for rebuilding society.

Assessing the Impact of the COVID-19 Pandemic in Spain: Large-Scale, Online, Self-Reported Population Survey.

BACKGROUND: Spain has been one of the countries most impacted by the COVID-19 pandemic. Since the first confirmed case was reported on January 31, 2020, there have been over 405,000 cases and 28,000 deaths in Spain. The economic and social impact is without precedent. Thus, it is important to quickly assess the situation and perception of the population. Large-scale online surveys have been shown to be an effective tool for this purpose. OBJECTIVE: We aim to assess the situation and perception of the Spanish population in four key areas related to the COVID-19 pandemic: social contact behavior during confinement, personal economic impact, labor situation, and health status. METHODS: We obtained a large sample using an online survey with 24 questions related to COVID-19 in the week of March 28-April 2, 2020, during the peak of the first wave of COVID-19 in Spain. The self-selection online survey method of nonprobability sampling was used to recruit 156,614 participants via social media posts that targeted the general adult population (age >18 years). Given such a large sample, the 95% CI was ±0.843 for all reported proportions. RESULTS: Regarding social behavior during confinement, participants mainly left their homes to satisfy basic needs. We found several statistically significant differences in social behavior across genders and age groups. The population's willingness to comply with the confinement measures is evident. From the survey answers, we identified a significant adverse economic impact of the pandemic on those working in small businesses and a negative correlation between economic damage and willingness to stay in confinement. The survey revealed that close contacts play an important role in the transmission of the disease, and 28% of the participants lacked the necessary resources to properly isolate themselves. We also identified a significant lack of testing, with only 1% of the population tested and 6% of respondents unable to be tested despite their doctor's recommendation. We developed a generalized linear model to identify the variables that were correlated with a positive SARS-CoV-2 test result. Using this model, we estimated an average of 5% for SARS-CoV-2 prevalence in the Spanish population during the time of the study. A seroprevalence study carried out later by the Spanish Ministry of Health reported a similar level of disease prevalence (5%). CONCLUSIONS: Large-scale online population surveys, distributed via social media and online messaging platforms, can be an effective, cheap, and fast tool to assess the impact and prevalence of an infectious disease in the context of a pandemic, particularly when there is a scarcity of official data and limited testing capacity.

The bridging and bonding structures of place-centric networks: Evidence from a developing country.

Social capital has long been associated with opportunities of access to valuable resources that individuals, groups, communities, and places can extract from the social structure emerging from their interactions. Despite the overall consensus on the structural signature of social capital, there is still controversy over the relative benefits associated with different types of social structure. In this article, we advocate a two-faceted perspective on social capital, regarded as value originating from both closed (rich in third-party relationships) and open (rich in brokerage opportunities) bridging structures. We uncover these structures from place-centric networks and investigate their association with key socio-economic indicators. To this end, we draw on aggregated mobile phone usage data, and construct the place-centric communication and mobility networks in the city of Abidjan in Côte d'Ivoire. We begin by defining appropriate network metrics to capture the interplay between bonding and bridging social structures in each of the 10 districts (communes) in Abidjan. We then examine the correlation between these metrics and a number of district-level socio-economic indicators related to economic prosperity, wealth, security and democratic participation. Our findings suggest that both closed and open structures can serve as wellsprings of social capital: while closed bonding structures are associated with higher economic prosperity, open bridging structures are associated with increased democratic participation and security. By uncovering sources of social capital from communication and mobility place-centric networks in a developing country, our work contributes to a better understanding of how these networks could be used to enhance and sustain socio-economic growth and prosperity, and ultimately paves the way towards a broader comparative study of social capital in developed and developing countries.

Strategies and limitations in app usage and human mobility.

Cognition has been found to constrain several aspects of human behaviour, such as the number of friends and the number of favourite places a person keeps stable over time. This limitation has been empirically defined in the physical and social spaces. But do people exhibit similar constraints in the digital space? We address this question through the analysis of pseudonymised mobility and mobile application (app) usage data of 400,000 individuals in a European country for six months. Despite the enormous heterogeneity of apps usage, we find that individuals exhibit a conserved capacity that limits the number of applications they regularly use. Moreover, we find that this capacity steadily decreases with age, as does the capacity in the physical space but with more complex dynamics. Even though people might have the same capacity, applications get added and removed over time. In this respect, we identify two profiles of individuals: app keepers and explorers, which differ in their stable (keepers) vs exploratory (explorers) behaviour regarding their use of mobile applications. Finally, we show that the capacity of applications predicts mobility capacity and vice-versa. By contrast, the behaviour of keepers and explorers may considerably vary across the two domains. Our empirical findings provide an intriguing picture linking human behaviour in the physical and digital worlds which bridges research studies from Computer Science, Social Physics and Computational Social Sciences.

Machine Learning and Data Analytics in Pervasive Health.

INTRODUCTION: This accompanying editorial provides a brief introduction to this focus theme, focused on "Machine Learning and Data Analytics in Pervasive Health". OBJECTIVE: The innovative use of machine learning technologies combining small and big data analytics will support a better provisioning of healthcare to citizens. This focus theme aims to present contributions at the crossroads of pervasive health technologies and data analytics as key enablers for achieving personalised medicine for diagnosis and treatment purposes. METHODS: A call for paper was announced to all participants of the "11th International Conference on Pervasive Computing Technologies for Healthcare", to different working groups of the International Medical Informatics Association (IMIA) and European Federation of Medical Informatics (EFMI) and was published in June 2017 on the website of Methods of Information in Medicine. A peer review process was conducted to select the papers for this focus theme. RESULTS: Four papers were selected to be included in this focus theme. The paper topics cover a broad range of machine learning and data analytics applications in healthcare including detection of injurious subtypes of patient-ventilator asynchrony, early detection of cognitive impairment, effective use of small data sets for estimating the performance of radiotherapy in bladder cancer treatment, and the use negation detection in and information extraction from unstructured medical texts. CONCLUSIONS: The use of machine learning and data analytics technologies in healthcare is facing a renewed impulse due to the availability of large amounts and new sources of human behavioral and physiological data, such as that captured by mobile and pervasive devices traditionally considered as nonmainstream for healthcare provision and management.

Mobile Network Data for Public Health: Opportunities and Challenges.

The ubiquity of mobile phones worldwide is generating an unprecedented amount of human behavioral data both at an individual and aggregated levels. The study of this data as a rich source of information about human behavior emerged almost a decade ago. Since then, it has grown into a fertile area of research named computational social sciences with a wide variety of applications in different fields such as social networks, urban and transport planning, economic development, emergency relief, and, recently, public health. In this paper, we briefly describe the state of the art on using mobile phone data for public health, and present the opportunities and challenges that this kind of data presents for public health.

Moves on the Street: Classifying Crime Hotspots Using Aggregated Anonymized Data on People Dynamics.

The wealth of information provided by real-time streams of data has paved the way for life-changing technological advancements, improving the quality of life of people in many ways, from facilitating knowledge exchange to self-understanding and self-monitoring. Moreover, the analysis of anonymized and aggregated large-scale human behavioral data offers new possibilities to understand global patterns of human behavior and helps decision makers tackle problems of societal importance. In this article, we highlight the potential societal benefits derived from big data applications with a focus on citizen safety and crime prevention. First, we introduce the emergent new research area of big data for social good. Next, we detail a case study tackling the problem of crime hotspot classification, that is, the classification of which areas in a city are more likely to witness crimes based on past data. In the proposed approach we use demographic information along with human mobility characteristics as derived from anonymized and aggregated mobile network data. The hypothesis that aggregated human behavioral data captured from the mobile network infrastructure, in combination with basic demographic information, can be used to predict crime is supported by our findings. Our models, built on and evaluated against real crime data from London, obtain accuracy of almost 70% when classifying whether a specific area in the city will be a crime hotspot or not in the following month.