Data Science for Social Good

The traditional epidemiological surveillance systems have proven effective over time but face significant delays due to dependency on hospitals, laboratories, and government databases. Moreover, underreporting and slow data collection hinder timely outbreak detection. This study proposes a novel framework, leveraging social media data-particularly from platforms such as Twitter, Google Trends, Reddit, and Facebook-to predict and monitor the spread of infectious diseases in real-time.

The EPSILON project - European Platform for Social Data Science Incubation, Learning, Operation and Network - aims to bridge the gap between data science and social good by fostering impactful, research-driven initiatives. Co-funded by the European Union, EPSILON provides open educational resources and best practices for leveraging data science methodologies in addressing societal challenges. This document synthesizes OER learning materials created by the EPSILON project team, structured across six modules, covering fundamental concepts in data science, ethical considerations, comparative analyses of "Data for Good" initiatives, and real-world case studies. Topics range from big data processing workflows and data science project life cycle management to best practices in data science volunteer management and data science applications in social sectors. The materials highlight the growing real-world impact of volunteer-driven, nonprofit data initiatives, with an emphasis on the importance of ethical responsibility, sustainable project design, and scientific collaboration. EPSILON was co-funded by the European Union (2021-1-DE01-KA220-HED-000029711). All views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or DAAD. Neither the European Union nor the granting authority can be held responsible for them.

People involved in mass emergencies increasingly publish information-rich contents in online social networks (OSNs), thus acting as a distributed and resilient network of human sensors. In this work, we present HERMES, a system designed to enrich the information spontaneously disclosed by OSN users in the aftermath of disasters. HERMES leverages a mixed data collection strategy, called hybrid crowdsensing, and state-of-the-art AI techniques. Evaluated in real-world emergencies, HERMES proved to increase: (i) the amount of the available damage information; (ii) the density (up to 7×) and the variety (up to 18×) of the retrieved geographic information; (iii) the geographic coverage (up to 30%) and granularity.

Crowdsensing systems can be either participatory or opportunistic, depending on whether the user intentionally contributes data, or she simply acts as the bearer of a sensing device from which data is transparently collected. In this paper, we propose hybrid crowdsensing, a social mediabased paradigm which aims at combining the strengths of both participatory and opportunistic crowdsensing. With hybrid crowdsensing, possibly relevant data is collected via an opportunistic approach. Then, users that spontaneously contributed are directly contacted and asked to provide additional information following a participatory approach. To demonstrate its feasibility and usefulness, we experimented the proposed paradigm for involving Twitter users in an emergency relief scenario. For each of the two real-world experiments we analyze the answer ratio to our questions, their time distribution, and responders' willingness to collaborate. Results support the adoption of hybrid crowdsensing, especially in those practical scenarios where users are emotionally involved.

Nowadays, social media analysis systems are feeding on user contributed data, either for beneficial purposes, such as emergency management, or for user profiling and mass surveillance. Here, we carry out a discussion about the power and pitfalls of public accessibility to social media-based systems, with specific regards to the emergency management application EARS (Earthquake Alert and Report System). We investigate whether opening such systems to the population at large would further strengthen the link between communities of volunteer citizens, intelligent systems, and decision makers, thus going in the direction of developing more sustainable and resilient societies. Our analysis highlights fundamental challenges and provides interesting insights into a number of research directions with the aim of developing human-centered social media-based systems.

Crowdsensing systems can be either participatory or opportunistic, depending on whether the user intentionally contributes data, or she simply acts as the bearer of a sensing device from which data is transparently collected. In this paper, we propose hybrid crowdsensing, a social mediabased paradigm which aims at combining the strengths of both participatory and opportunistic crowdsensing. With hybrid crowdsensing, possibly relevant data is collected via an opportunistic approach. Then, users that spontaneously contributed are directly contacted and asked to provide additional information following a participatory approach. To demonstrate its feasibility and usefulness, we experimented the proposed paradigm for involving Twitter users in an emergency relief scenario. For each of the two real-world experiments we analyze the answer ratio to our questions, their time distribution, and responders' willingness to collaborate. Results support the adoption of hybrid crowdsensing, especially in those practical scenarios where users are emotionally involved.

Well-being is an important value for people’s lives, and it could be considered as an index of societal progress. Researchers have suggested two main approaches for the overall measurement of well-being, the objective and the subjective well-being. Both approaches, as well as their relevant dimensions, have been traditionally captured with surveys. During the last decades, new data sources have been suggested as an alternative or complement to traditional data. This paper aims to present the theoretical background of well-being, by distinguishing between objective and subjective approaches, their relevant dimensions, the new data sources used for their measurement and relevant studies. We also intend to shed light on still barely unexplored dimensions and data sources that could potentially contribute as a key for public policing and social development.

Nowadays, social media analysis systems are feeding on user contributed data, either for beneficial purposes, such as emergency management, or for user profiling and mass surveillance. Here, we carry out a discussion about the power and pitfalls of public accessibility to social media-based systems, with specific regards to the emergency management application EARS (Earthquake Alert and Report System). We investigate whether opening such systems to the population at large would further strengthen the link between communities of volunteer citizens, intelligent systems, and decision makers, thus going in the direction of developing more sustainable and resilient societies. Our analysis highlights fundamental challenges and provides interesting insights into a number of research directions with the aim of developing human-centered social media-based systems.

The exploration of people's everyday life has long been of interest to social scientists. Recent years have witnessed a growing interest in analyzing human behavioral data generated by technology (e.g. mobile phones). To date, a few large-scale studies have been designed to measure human behaviors and interactions using multiple sources of data. A common characteristic of these studies is the population under investigation: students having similar daily routines and needs. This choice constraints the range of behaviors, of places and the generalization of the results. In order to widen this line of studies, we focus on a different target group: parents with young children aged 0 through 10 years. Children influence multiple aspects of their parents' lives, from the satisfaction of basic human needs and the fulfillment of social roles to their financial status and sleep quality. In this paper, we describe the Mobile Territorial Lab (MTL) project, a longitudinal living lab which has been sensing by means of technology (mobile phones) the lives of more than 100 parents in different areas of the Trentino region in Northern Italy. We present the preliminary results after two years of experimentation of, to the best of our knowledge, the most complete picture of parents' daily lives. Through the collection and analysis of the collected data, we created a multi-layered view of the participants' lives, tracking social interactions, mobility routines, spending patterns, and personality characteristics. Overall, our results prove the relevance of living lab approaches to measure human behaviors and interactions, which can pave the way to new studies exploiting a richer number of behavioral indicators. Moreover, we believe that the proposed methodology and the collected data could be very valuable for researchers from different disciplines such as social psychology, sociology, computer science, economy, etc., which are interested in understanding human behaviour.

The exponential growth of the urban data generated by urban sensors, government reports, and crowd-sourcing services endorses the rapid development of urban computing and spatial data mining technologies. Easier accessibility to such enormous urban data may be a double-bladed sword. On the one hand, urban data can be applied to solve a wide range of practical issues such as urban safety analysis and urban event detection. On the other hand, ethical issues such as biasedly polluted urban data, problematic algorithms, and unprotected privacy may cause moral disaster not only for the research fields but also for the society. This paper seeks to identify ethical vulnerabilities from three primary research directions of urban computing: urban safety analysis, urban transportation analysis, and social media analysis for urban events. Visions for future improvements in the perspective of ethics are addressed.

Urban income segregation is a widespread phenomenon that challenges societies across the globe. Classical studies on segregation have largely focused on the geographic distribution of residential neighborhoods rather than on patterns of social behaviors and interactions. In this study, we analyze segregation in economic and social interactions by observing credit card transactions and Twitter mentions among thousands of individuals in three culturally different metropolitan areas. We show that segregated interaction is amplified relative to the expected effects of geographic segregation in terms of both purchase activity and online communication. Furthermore, we find that segregation increases with difference in socio-economic status but is asymmetric for purchase activity, i.e., the amount of interaction from poorer to wealthier neighborhoods is larger than vice versa. Our results provide novel insights into the understanding of behavioral segregation in human interactions with sig...

The advent of Big Data is having an important impact on the production and analysis of data, and is changing the environment within which the official statistical community operates. Spurred by the increased demands for timely and disaggregated data for the monitoring of the new sustainable development goals of the Post-2015 development agenda, innovative technologies and new data sources are required. The rapid spread of mobile phone usage offers a great opportunity to improve timeliness and fill data gaps. There are today almost 7 billion mobile-cellular subscriptions worldwide. Each of these mobile phones generates data and leaves a digital footprint by communicating, accessing the internet and using various applications. Mobile phone data provides – among others – the location and movement of mobile phones, and is considered one the most promising data sources for measuring and monitoring spatial-temporal activities of the population. In parallel, geospatial information services...

Our paper presents the products, preliminary findings, and methodology of the Equitable Futures project, an investigation into the active gentrification process and increasingly inequitable access to opportunities in Seattle. The project is currently underway at the University of Washington eScience Institute’s Data Science for Social Good (DSSG) summer program, and connects issues usually analyzed by people working in the built environment, geography, sociology, economics, social work and local governments with a heavy focus on accessibility and transparency. The project team is developing a tool that allows stakeholders in the city’s development process to analyze, model, and visualize existing trends and the impact of potential changes in the built environment. Our tool uses publicly available data to model and visualize equity indicators on the city and neighborhood scale. In this paper, we focus on the incorporation of data transparency and model interpretability strategies int...

The advent of Big Data is having an important impact on the production and analysis of data, and is changing the environment within which the official statistical community operates. Spurred by the increased demands for timely and disaggregated data for the monitoring of the new sustainable development goals of the Post-2015 development agenda, innovative technologies and new data sources are required. The rapid spread of mobile phone usage offers a great opportunity to improve timeliness and fill data gaps. There are today almost 7 billion mobile-cellular subscriptions worldwide. Each of these mobile phones generates data and leaves a digital footprint by communicating, accessing the internet and using various applications. Mobile phone data provides – among others – the location and movement of mobile phones, and is considered one the most promising data sources for measuring and monitoring spatial-temporal activities of the population. In parallel, geospatial information services...

Against a geographically isolated and economically — if not culturally — impoverished backdrop, brothers B.B. and D.D. Dougherty founded Watauga Academy, a forerunner of Appalachian State University, in 1899 to provide educational opportunity for the region. Its founding ethos reflected the character of its community: a self-reliant and self-sustaining institution with a pioneering and nimble spirit. From these humble beginnings, Appalachian State has emerged as a nationally-recognized regional university characterized by a commitment to sustainability and innovative education. It is an institution that appreciates a bootstrapping sensibility.

The University of Washington eScience Institute runs an annual Data Science for Social Good (DSSG) program that selects four projects each year to train students from a wide range of disciplines while helping community members execute social good projects, often with an urban focus. We present observations and deliberations of one such project, the DSSG 2017 'Equitable Futures' project, which investigates the ongoing gentrification process and the increasingly inequitable access to opportunities in Seattle. Similar processes can be observed in many major cities. The project connects issues usually analyzed in the disciplines of the built environment, geography, sociology, economics, social work and city governments with data science methodologies and visualizations.

University-hosted Data for Good (D4G) programs provide research, service, and learning opportunities to students through team-based projects run outside of normal course offerings (typically during the summer). Popular with students, D4G programs provide the opportunity to work directly with data science mentors and subject matter experts from non-profits, government agencies, community organizations, and academia. These programs fill an important niche—supporting the responsible utilization of data science tools and techniques to address social concerns. The mission of a D4G program can be tailored to support different types of students, partners, and projects as envisioned by program and university leadership. University-hosted Data for Good (D4G) programs provide research, service, and learning opportunities to students through team-based projects run outside of normal course offerings (typically during the summer). Popular with students, D4G programs provide the opportunity to work directly with data science mentors and subject matter experts from non-profits, government agencies, community organizations, and academia. These programs fill an important niche—supporting the responsible utilization of data science tools and techniques to address social concerns. The mission of a D4G program can be tailored to support different types of students, partners, and projects as envisioned by program and university leadership.

Drawing on the collective experience of organizers running “Data Science for Social Good”, “Data Science for Public Good” and “Data Science for Common Good”programs in the United States, Canada, and UK— this paper covers key decision points in designing a Data for Good program.

Our paper presents the products, preliminary findings, and methodology of the Equitable Futures project, an investigation into the active gentrification process and increasingly inequitable access to opportunities in Seattle. The project is currently underway at the University of Washington eScience Institute’s Data Science for Social Good (DSSG) summer program, and connects issues usually analyzed by people working in the built environment, geography, sociology, economics, social work and local governments with a heavy focus on accessibility and transparency. The project team is developing a tool that allows stakeholders in the city’s development process to analyze, model, and visualize existing trends and the impact of potential changes in the built environment. Our tool uses publicly available data to model and visualize equity indicators on the city and neighborhood scale. In this paper, we focus on the incorporation of data transparency and model interpretability strategies int...

The University of Washington eScience Institute runs an annual Data Science for Social Good (DSSG) program that selects four projects each year to train students from a wide range of disciplines while helping community members execute social good projects, often with an urban focus. We present observations and deliberations of one such project, the DSSG 2017 'Equitable Futures' project, which investigates the ongoing gentrification process and the increasingly inequitable access to opportunities in Seattle. Similar processes can be observed in many major cities. The project connects issues usually analyzed in the disciplines of the built environment, geography, sociology, economics, social work and city governments with data science methodologies and visualizations.

Historically, the great cities of the world have built public spaces that have often been used as venues for spectacle, and displays of power and status. These public venues are part of the identity of these cities and have an importance and influence far beyond their physical dimensions or geometric shapes. They are all platforms that accommodate, both physically and metaphorically, the various expressions of a society. This paper will address historical events and their transcendency within the context of the city and their historical importance and effect in a larger global context. In addition, it will apply theoretical concepts about city space and the public sphere through an application of post-structuralist theory, critical theory, and social capital theory. 1

Atmosphere, atmospheric, or atmotopo attempts to capture a crucial cultural moment that weaves together different schemes of thought with myriad technologies of communication and visualization. The methods of representation are arguably more varied than ever, and with them, design methods cross all kinds of knowledge. But almost none of the elements that constitute the problem of the atmosphere are aligned under the same ideology. Therefore, addressing the atmosphere within architectural thought becomes a pressing issue today. It involves the acceptance of heterogeneities, contradictions, and antagonisms between the different ways that the term is being used. From Fumifugium: or the Inconvenience of Aer and Smoak of London Dissipated, (1661) of John Evelyn´s to the implants of nature (2003) of Olafur Eliasson on weather dispositions (arrangements), nature, ecology, energy, economy, urbanism, and architecture are aligned under the context of the term “atmosphere.” Embracing such diff...

In 2013-14, the California State University system funded 23 grants on 14 campuses in an effort to spur innovation in sustainability. The funding for these grants came from leveraging $250,000 of system-wide resources slated for energy efficiency improvements towards the support of educational initiatives that bridged facilities and the academy2. The intent of this initiative was to inspire applied research that tied teaching and learning to campus buildings, landscapes, and infrastructure in ways that would inform future project investments related to cost and energy savings as well as sustainability practices and increase the understanding of facility performance while utilizing high-impact educational practices.

The integration between official statistics and social media data is a challenging topic. This contribution aims to present a recently-designed framework to compare sentiment analysis on social media content with social and economic data. Such framework - which has already been applied, in a preliminary fashion, to the Felicitta project - is meant to integrate official statistics and correlate it with online social media data. Its ultimate goal, in fact, namely consists in giving a contribution to the definition of a measure of subjective well-being that could fully benefit from both traditional, well-established social indicators and dynamic data obtained from the web.

Epidemic outbreaks are an important healthcare challenge, especially in developing countries where they represent one of the major causes of mortality. Approaches that can rapidly target subpopulations for surveillance and control are critical for enhancing containment and mitigation processes during epidemics. Using a real-world dataset from Ivory Coast, this work presents an attempt to unveil the socio-geographical heterogeneity of disease transmission dynamics. By employing a spatially explicit meta-population epidemic model derived from mobile phone Call Detail Records (CDRs), we investigate how the differences in mobility patterns may affect the course of a hypothetical infectious disease outbreak. We consider different existing measures of the spatial dimension of human mobility and interactions, and we analyse their relevance in identifying the highest risk sub-population of individuals, as the best candidates for isolation countermeasures. The approaches presented in this paper provide further evidence that mobile phone data can be effectively exploited to facilitate our understanding of individuals' spatial behaviour and its relationship with the risk of infectious diseases' contagion. In particular, we show that CDRs-based indicators of individuals' spatial activities and interactions hold promise for gaining insight of contagion heterogeneity and thus for developing mitigation strategies to support decision-making during country-level epidemics.

The pursuit of happiness. What does that mean? Perhaps a more prominent question to ask is, 'how does one know whether people have succeeded in their pursuit'? Survey data, thus far, has served us well in determining where people see themselves on their journey. However, in an everchanging world, one needs high-frequency data instead of data released with significant time-lags. High-frequency data, which stems from Big Data, allows policymakers access to virtually real-time information that can assist in effective decision-making to increase the quality of life for all. Additionally, Big Data collected from, for example, social media platforms give researchers unprecedented insight into human behaviour, allowing significant future predictive powers.

Our paper presents the products, preliminary findings, and methodology of the Equitable Futures project, an investigation into the active gentrification process and increasingly inequitable access to opportunities in Seattle. The project is currently underway at the University of Washington eScience Institute’s Data Science for Social Good (DSSG) summer program, and connects issues usually analyzed by people working in the built environment, geography, sociology, economics, social work and local governments with a heavy focus on accessibility and transparency. The project team is developing a tool that allows stakeholders in the city’s development process to analyze, model, and visualize existing trends and the impact of potential changes in the built environment. Our tool uses publicly available data to model and visualize equity indicators on the city and neighborhood scale. In this paper, we focus on the incorporation of data transparency and model interpretability strategies int...

Twitter is a unique social media channel, in the sense that users discuss and talk about the most diverse topics, including their health conditions. In this paper we analyze how Dengue epidemic is reflected on Twitter and to what extent that information can be used for the sake of surveillance. Dengue is a mosquito-borne infectious disease that is a leading cause of illness and death in tropical and subtropical regions, including Brazil. We propose an active surveillance methodology that is based on four dimensions: volume, location, time and public perception. First we explore the public perception dimension by performing sentiment analysis. This analysis enables us to filter out content that is not relevant for the sake of Dengue surveillance. Then, we verify the high correlation between the number of cases reported by official statistics and the number of tweets posted during the same time period (i.e., R 2 = 0.9578). A clustering approach was used in order to exploit the spatiotemporal dimension, and the quality of the clusters obtained becomes evident when they are compared to official data (i.e., RandIndex = 0.8914). As an application, we propose a Dengue surveillance system that shows the evolution of the dengue situation reported in tweets, which is implemented in www.observatorio.inweb.org.br/dengue/.

Twitter is a unique social media channel, in the sense that users discuss and talk about the most diverse topics, including their health conditions. In this paper we analyze how Dengue epidemic is re ected on Twitter and to what extent that information can be used for the sake of surveillance. Dengue is a mosquito-borne infectious disease that is a leading cause of illness and death in tropical and subtropical regions, including Brazil. We propose an active surveillance methodology that is based on four dimensions: volume, location, time and ...

The University of Washington eScience Institute runs an annual Data Science for Social Good (DSSG) program that selects four projects each year to train students from a wide range of disciplines while helping community members execute social good projects, often with an urban focus [6]. We present observations and deliberations of one such project, the DSSG 2017 `Equitable Futures' project, which investigates the ongoing gentri fication process and the increasingly inequitable access to opportunities in Seattle. Similar processes can be observed in many major cities. The project connects issues usually analyzed in the disciplines of the built environment, geography, sociology, economics, social work and city governments with data science methodologies and visualizations.

In this contribution we summarize insights on the geographical veracity of using mobile phone data to create (statistical) indicators. We focus on problems that persist with spatial allocation, spatial delineation and spatial aggregation of information obtained from mobile phone data. For each of the cases, we offer insights from our works on a French CDR dataset and propose both short and long term solutions. As such, we aim at offering a list of challenges, and a roadmap for future work on the topic.

Data science has developed a culture of “data science for social good,” or DSSG, to address the ethical dilemma that their work and innovations benefit primarily the corporate and investment sectors. DSSG programs provide data analysis to public and nonprofit organizations with limited access to resources. A multidisciplinary team from a built environment college had the opportunity to participate in a DSSG summer program. Their project focused on identifying the most significant indicators of equity and the building of a web-based tool for analyzing and visualizing urban equity across the built fabric of a city. The tool is capable of visually displaying the raw data sets that were sourced from the US Census Bureau and city records as well as analysis from a structural equation model. The team successfully explored how urban equity could be better understood and hopefully, better mitigated by combining the domain knowledge of the built environment and data science. Four key lessons...

Esta publicación contiene una estructura informativa y datos estadísticos sobre las organizaciones sin fines de lucro en México hasta 2021. Contiene también datos que dimensionan algunas de las problemáticas actuales a las que se enfrentan las organizaciones, el origen de sus recursos, su relación con los donantes y algunas de las principales afectaciones que tuvo la sociedad civil y las OSC durante la pandemia.

The high population density in cities confers many advantages, including improved social interaction and information exchange. However, it is often argued that urban living comes at the expense of reducing happiness. The goal of this research is to shed light on the relationship between urban communication and urban happiness. We analyze geo-located social media posts (tweets) within a major urban center (Milan) to produce a detailed spatial map of urban sentiments. We combine this data with high-resolution mobile communication intensity data among different urban areas. Our results reveal that happy (respectively unhappy) areas preferentially communicate with other areas of their type. This observation constitutes evidence of homophilous communities at the scale of an entire city (Milan), and has implications on interventions that aim to improve urban well-being.

Early detection of disease outbreaks is crucial and even small improvements in detection can significantly impact on a country's public health. In this work, we investigate the use of a crowdsourcing application and a real-time disease outbreak surveillance system for five diseases; Influenza, Gastroenteritis, Upper Respiratory Tract Infection (URTI), Scabies and Conjunctivitis, that are closely monitored in Mauritius. We also analyze and correlate the collected data with past statistics. A crowdsourcing mobile application known as Disease Outbreak Tracker (DOT) was implemented and made public. A real-time disease surveillance system using the Early Aberration Reporting System algorithm (EARS) for analysis of the collected data was also implemented. The collected data were correlated to historical data for 2017. Data were successfully collected and plotted on a daily basis. The results show that a few cases of Flu and Scabies were reported in some districts. The EARS methods C1, C2 and C3 also depicted spikes above the set threshold on some days. The study covers data collected over a period of one month. Once symptoms data were collected using DOT, probabilistic methods were used to find the disease or diseases that the user was suffering from. The data were further processed to find the extent of the disease outbreak district-wise, per disease. These data were represented graphically for a rapid understanding of the situation in each district. Our findings concur with existing data for the same period for previous years showing that the crowdsourcing application can aid in the detection of disease outbreaks.

Early detection of disease outbreaks is crucial and even small improvements in detection can significantly impact on a country's public health. In this work, we investigate the use of a crowdsourcing application and a real-time disease outbreak surveillance system for five diseases; Influenza, Gastroenteritis, Upper Respiratory Tract Infection (URTI), Scabies and Conjunctivitis, that are closely monitored in Mauritius. We also analyze and correlate the collected data with past statistics. A crowdsourcing mobile application known as Disease Outbreak Tracker (DOT) was implemented and made public. A real-time disease surveillance system using the Early Aberration Reporting System algorithm (EARS) for analysis of the collected data was also implemented. The collected data were correlated to historical data for 2017. Data were successfully collected and plotted on a daily basis. The results show that a few cases of Flu and Scabies were reported in some districts. The EARS methods C1, C2 and C3 also depicted spikes above the set threshold on some days. The study covers data collected over a period of one month. Once symptoms data were collected using DOT, probabilistic methods were used to find the disease or diseases that the user was suffering from. The data were further processed to find the extent of the disease outbreak district-wise, per disease. These data were represented graphically for a rapid understanding of the situation in each district. Our findings concur with existing data for the same period for previous years showing that the crowdsourcing application can aid in the detection of disease outbreaks.

Early detection of disease outbreaks is crucial and even small improvements in detection can significantly impact on a country's public health. In this work, we investigate the use of a crowdsourcing application and a real-time disease outbreak surveillance system for five diseases; Influenza, Gastroenteritis, Upper Respiratory Tract Infection (URTI), Scabies and Conjunctivitis, that are closely monitored in Mauritius. We also analyze and correlate the collected data with past statistics. A crowdsourcing mobile application known as Disease Outbreak Tracker (DOT) was implemented and made public. A real-time disease surveillance system using the Early Aberration Reporting System algorithm (EARS) for analysis of the collected data was also implemented. The collected data were correlated to historical data for 2017. Data were successfully collected and plotted on a daily basis. The results show that a few cases of Flu and Scabies were reported in some districts. The EARS methods C1, C2 and C3 also depicted spikes above the set threshold on some days. The study covers data collected over a period of one month. Once symptoms data were collected using DOT, probabilistic methods were used to find the disease or diseases that the user was suffering from. The data were further processed to find the extent of the disease outbreak district-wise, per disease. These data were represented graphically for a rapid understanding of the situation in each district. Our findings concur with existing data for the same period for previous years showing that the crowdsourcing application can aid in the detection of disease outbreaks.

Well-being is an important value for people's lives, and it could be considered as an index of societal progress. Researchers have suggested two main approaches for the overall measurement of well-being, the objective and the subjective well-being. Both approaches, as well as their relevant dimensions, have been traditionally captured with surveys. During the last decades, new data sources have been suggested as an alternative or complement to traditional data. This paper aims to present the theoretical background of well-being, by distinguishing between objective and subjective approaches, their relevant dimensions, the new data sources used for their measurement and relevant studies. We also intend to shed light on still barely unexplored dimensions and data sources that could potentially contribute as a key for public policing and social development. Keywords Well-being • Objective well-being • Subjective well-being • Well-being dimensions • New data sources • Data science for social good • Artificial intelligence for social good B Luca Pappalardo

Early detection of disease outbreaks is crucial and even small improvements in detection can significantly impact on a country's public health. In this work, we investigate the use of a crowdsourcing application and a real-time disease outbreak surveillance system for five diseases; Influenza, Gastroenteritis, Upper Respiratory Tract Infection (URTI), Scabies and Conjunctivitis, that are closely monitored in Mauritius. We also analyze and correlate the collected data with past statistics. A crowdsourcing mobile application known as Disease Outbreak Tracker (DOT) was implemented and made public. A real-time disease surveillance system using the Early Aberration Reporting System algorithm (EARS) for analysis of the collected data was also implemented. The collected data were correlated to historical data for 2017. Data were successfully collected and plotted on a daily basis. The results show that a few cases of Flu and Scabies were reported in some districts. The EARS methods C1, C2 and C3 also depicted spikes above the set threshold on some days. The study covers data collected over a period of one month. Once symptoms data were collected using DOT, probabilistic methods were used to find the disease or diseases that the user was suffering from. The data were further processed to find the extent of the disease outbreak district-wise, per disease. These data were represented graphically for a rapid understanding of the situation in each district. Our findings concur with existing data for the same period for previous years showing that the crowdsourcing application can aid in the detection of disease outbreaks.