Measuring objective and subjective well-being: dimensions and data sources

AbstractWell-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

Identification and characterization of diseases on social web

[no abstract

2012 Annual Research Symposium Abstract Book

2012 annual volume of abstracts for science research projects conducted by students at Trinity College

Three Essays on Health, Risk and Behavior

Health-related decisions could be explained by a variety of factors, ranging from the perception of the risk the activity involves to the knowledge of the long-term effects of the decision. An individual deciding to eat unhealthy or not to exercise could face health problems in the long-run. Individuals with health issues have been found to be more risk averse when it comes to financial decisions, such as portfolio allocation. Financial incentives to engage in healthier behaviors have been successful but short-lived. Prior research leaves many questions such as: What are the factors that explain why an individual decides to engage in healthy or unhealthy behaviors? Are risk preferences a determinant in the decision to seek treatment for an illness? Do beliefs about the outcome of unhealthy and healthy decisions play a role? This dissertation aims at investigating the following questions: How do social aspects of risk impact health-related decisions? And, more broadly, what is the relationship between health, risk preferences and perception and behavior? I focus on three main applications: flu vaccinations, asthma in children and the impact of chronic illnesses (and other joint risks) in risky decisions. This dissertation investigates different ways in which risk, health and behavior interact with each other. I focus on three health-related applications: flu vaccines, asthma and how health influences individual risk preferences. The findings of these three chapters are intended to contribute to the discussion of how health impacts behavior and the perception of risk. I also analyze the effect of different sources of endogeneity, such as the beliefs about the flu vaccine and the severity of an influenza episode, beliefs about the severity of asthma or the effectiveness of treatments to tackle an asthma episode. This research will help policy makers better understand how risk perceptions about the outcomes of different health-related behaviors. I aim at continuing the conversation about which would be the best way to incentivize healthy behaviors and modify negative views of treatments to deal with chronic illnesses

Social evolution : opinions and behaviors in face-to-face networks

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 133-143).Exposure to new ideas and opinions, and their diffusion within social networks, are important questions in education, business, and government. However until recently there has been no method to automatically capture fine-grained face-to-face interactions between people, to better model the diffusion process. In this thesis, we describe the use of colocation and communication sensors in 'socially aware' mobile phones to model the spread of opinions and behaviors of 78 residents of an undergraduate residence hall for an entire academic year, based on over 320,000 hours of behavior data. Political scientists (Huckfeldt and Sprague, APSR, 1983) have noted the problem of mutual causation between face-to-face networks and political opinions. During the last three months of the 2008 US presidential campaigns of Barack Obama and John McCain, we find that political discussants have characteristic interaction patterns that can be used to recover the self-reported 'political discussant' ties within the community. Automatically measured mobile phone features allow us to estimate exposure to different types of opinions in this community. We propose a measure of 'dynamic homophily' which reveals surprising short-term, population-wide behavior changes around external political events such as election debates and Election Day. To our knowledge, this is the first time such dynamic homophily effects have been measured. We find that social exposure to peers in the network predicts individual future opinions (R 2 ~ 0.8, p < 0.001). The use of mobile phone based dynamic exposure increases the explained variance for future political opinions by up to 30%. It is well known that face-to-face networks are the main vehicle for airborne contagious diseases (Elliott, Spatial Epidemiology, 2000). However, epidemiologists have not had access to tools to quantitatively measure the likelihood of contagion, as a function of contact/exposure with infected individuals, in realistic scenarios (Musher, NEJM, 2003), since it requires data about both symptoms and social interactions between individuals. We use of co-location and communication sensors to understand the role of face-to-face interactions in the contagion process. We find that there are characteristic changes in behavior when individuals become sick, reflected in features like total communication, temporal structure in communication (e.g., late nights and weekends), interaction diversity, and movement entropy (both within and outside the university). These behavior variations can be used to infer the likelihood of an individual being symptomatic, based on their network interactions alone, without the use of health-reports. We use a recently-developed signal processing approach (Nolte, Nature, 2008) to better understand the temporal information flux between physical symptoms (i.e., common colds, influenza), measured behavior variations and mental health symptoms (i.e., stress and early depression). Longitudinal studies indicate that health-related behaviors from obesity (Christakis and Fowler, 2007) to happiness (Fowler and Christakis, 2008) may spread through social ties. The effects of social networks and social support on physical health are well-documented (Berkman, 1994; Marmot and Wilkinson, 2006). However, these studies do not quantify actual face-to-face interactions that lead to the adoption of health-related behaviors. We study the variations in BMI, weight (in lbs), unhealthy eating habits, diet and exercise, and find that social exposure measured using mobile phones is a better predictor of BMI change over a semester, than self-report data, in stark contrast to previous work. From a smaller pilot study of social exposure in face-to-face networks and the propagation of viral music, we find that phone communication and location features predict the sharing of music between people, and also identify social ties that are 'close friends' or 'casual acquaintances'. These interaction and music sharing features can be used to model latent influences between participants in the music sharing process.by Anmol Madan.Ph.D

2022 EIS Conference

Welcome to the first ever virtual EIS conference. For the past 2 years, we were unable to host the conference because EIS officers, their supervisors, and likely anyone reading this was fully engaged in the pandemic response. While the pandemic is not over, we are resuming critical training elements of the EIS program. EIS conference is a foundational training opportunity for officers to refine their skills with communicating scientific information; for attendees to learn from and ask challenging questionsabout EIS officer investigations and analyses; and for EIS officers, alumni, CDC staff, and the public health community to come together and exchange ideas around the practice of consequential epidemiology. We are grateful you can join us for the conference.Publication date from document properties.eis-conference-2022-508.pdf20221166

Mining social media data for biomedical signals and health-related behavior

Social media data has been increasingly used to study biomedical and health-related phenomena. From cohort level discussions of a condition to planetary level analyses of sentiment, social media has provided scientists with unprecedented amounts of data to study human behavior and response associated with a variety of health conditions and medical treatments. Here we review recent work in mining social media for biomedical, epidemiological, and social phenomena information relevant to the multilevel complexity of human health. We pay particular attention to topics where social media data analysis has shown the most progress, including pharmacovigilance, sentiment analysis especially for mental health, and other areas. We also discuss a variety of innovative uses of social media data for health-related applications and important limitations in social media data access and use.Comment: To appear in the Annual Review of Biomedical Data Scienc

Human dynamic networks in opportunistic routing and epidemiology

Measuring human behavioral patterns has broad application across different sciences. An individual’s social, proximal and geographical contact patterns can have significant importance in Delay Tolerant Networking (DTN) and epidemiological modeling. Recent advances in computer science have not only provided the opportunity to record these behaviors with considerably higher temporal resolution and phenomenological accuracy, but also made it possible to record specific aspects of the behaviors which have been previously difficult to measure. This thesis presents a data collection system using tiny sensors which is capable of recording humans’ proximal contacts and their visiting pattern to a set of geographical locations. The system also collects information on participants’ health status using weekly surveys. The system is tested on a population of 36 participants and 11 high-traffic public places. The resulting dataset offers rich information on human proximal and geographic contact patterns cross-linked with their health information. In addition to the basic analysis of the dataset, the collected data is applied to two different applications. In DTNs the dataset is used to study the importance of public places as relay nodes, and described an algorithm that takes advantage of stationary nodes to improve routing performance and load balancing in the network. In epidemiological modeling, the collected dataset is combined with data on H1N1 infection spread over the same time period and designed a model on H1N1 pathogen transmission based on these data. Using the collected high-resolution contact data as the model’s contact patterns, this work represents the importance of contact density in addition to contact diversity in infection transmission rate. It also shows that the network measurements which are tied to contact duration are more representative of the relation between centrality of a person and their chance of contracting the infection

Current Perspectives on Viral Disease Outbreaks

The COVID-19 pandemic has reminded the world that infectious diseases are still important. The last 40 years have experienced the emergence of new or resurging viral diseases such as AIDS, ebola, MERS, SARS, Zika, and others. These diseases display diverse epidemiologies ranging from sexual transmission to vector-borne transmission (or both, in the case of Zika). This book provides an overview of recent developments in the detection, monitoring, treatment, and control of several viral diseases that have caused recent epidemics or pandemics