Social Network Analysis

Emerging event detection is the process of automatically identifying novel and emerging ideas from text with minimal human intervention. With the rise of social networks like Twitter, topic detection has begun leveraging measures of user influence to identify emerging events. Twitter's highly skewed follower/followee structure lends itself to an intuitive model of influence, yet in a context like the Emerging Infections Network (EIN), a sentinel surveillance listserv of over 1400 infectious disease experts, developing a useful model of authority becomes less clear. Who should we listen to on the EIN? To explore this, we annotated a body of important EIN discussions and tested how well 3 models of user authority performed in identifying those discussions. In previous work we proposed a process by which only posts that are based on specific "important" topics are read, thus drastically reducing the amount of posts that need to be read. The process works by finding a set of "bellwether" users that act as indicators for "important" topics and only posts relating to these topics are then read. This approach does not consider the text of messages, only the patterns of user participation. Our text analysis approach follows that of Cataldi et al.[1], using the idea of semantic "energy" to identify emerging topics within Twitter posts. Authority is calculated via PageRank and used to weight each author's contribution to the semantic energy of all terms occurring in within some interval ti. A decay parameter d defines the impact of prior time steps on the current interval.

Objective

To explore how different models of user influence or authority perform when detecting emerging events within a small-scale community of infectious disease experts.

Taiwan had established a nation-wide emergency department (ED)-based syndromic surveillance system since 2004, with a mean detection sensitivity of 0.67 in 2004-06 [1]. However, this system may not represent the true epidemic situation of infectious disease in community, particularly those who don't seek medical care [2]. Moreover, the epidemiological settings, sources of the infection and social network all together may still facilitate the transmissions. These rooted problems cannot be rapidly solved.

Objective

This study has two specific aims:

(1) to establish a web-based, public-access infectious disease reporting system (www.eid.url.tw), using newly designed public syndrome groups and based on computational and participatory epidemiology

(2) to evaluate this system by comparing the epidemiological patterns with national-wide electronic health-database and traditional passive surveillance systems from Taiwan-CDC.

Hypoglycemia is a serious sequela of diabetes treatment that is not tracked by current health surveillance efforts despite substantial related morbidity and mortality. We take a novel approach to hypoglycemia surveillance, engaging members of an international online diabetes social network in reporting about this issue as members of a consented, distributed public health research cohort.

Objective

To measure the prevalence of hypoglycemic episodes and associated harms among participants in an international, online diabetes social network.

With the proliferation of social networks, the web has become a warehouse of patient discussions and reports, estimated at 10 billion records and growing at a rate of 40 percent per year. First Life Research, Ltd. (FLR), has searched and mapped thousands of these discussions and indexed hundreds of millions of reports (currently 960M) and is engaged in building web-based solutions that enable the public and public health practitioners to access massive health-related information and knowledge generated from the crowd.

Objective

With a large population sharing experiences regarding health issues and treatments online via social media platforms, generating novel data sets composed of massive unstructured user-generated content of health reports. This collective intelligence is referred to as the ‘Wisdom of the crowd’. This is a brief overview of data research engaging this unique statistical sample referred to as the ‘Crowd trial’ as an innovative element in health monitoring, enabling early detection and intervention by health professionals, regulators and pharmaceutical companies.

One of the common tasks faced by the U.S. Department of Agriculture (USDA) food safety analysts is to estimate the risk of observing positive outcomes of microbial tests of food samples collected at the slaughter and food processing establishments. Resulting risk estimates can be used, among other criteria, to drive allocation of FSIS investigative resources. The Activity From Demographics and Links (AFDL) algorithm is a computationally efficient method for estimating activity of unlabeled entities in a graph from patterns of connectivity of known active entities, and from their demographic profiles. It has been successfully used in social network analysis and intelligence applications. In order to test its utility in the food safety context, we treat a co-occurrence of the same strain of bacteria (in particular a specific serotype of Salmonella) in samples taken at different establishments at roughly the same time, as a link in the graph spanning all of the USDA controlled establishments. Now, given the historical patterns of linkage and the information about the distribution of the currently observed microbial positives (which make the corresponding establishments “active” in the AFDL terminology), we aim at predicting which of the remaining establishments are likely to also report positive results of tests. Even though such definition of a link produces uncertain data given that the co-occurrences of specific test results at different establishments may be purely coincidental and our analysis does not attempt to distinguish them from truly correlated instances, we expect that using this inherently noisy data in combination with demographic features of establishments, would lead to useful predictability of microbial events.

Objective

The objective of the research summarized in this paper is to evaluate utility of the AFDL in predicting likelihood of positive isolates obtained from microbial testing of food samples collected at the USDA controlled establishments.

In epidemiology, contact tracing is a process to control the spread of an infectious disease and identify individuals who were previously exposed to patients with the disease. After the emergence of AIDS, SNA was demonstrated to be a good supplementary tool for contact tracing [1]. Traditionally, social networks for disease investigation are constructed only with personal contacts since personal contacts are the most identifiable paths for disease transmission. However, for diseases which transmit not only through personal contacts, incorporating geographical contacts into SNA has been demonstrated to reveal potential contacts among patients [2][3].

Objective

In this research, we aim to investigate the necessity of incorporating geographical contacts into Social Network Analysis (SNA) for contact tracing in epidemiology and explore the strengths of multi-mode networks with patients and geographical locations in network visualization for disease spread investigation.

In the realm of public health, there has been an increasing trend in exploration of social network analyses (SNAs). SNAs are methodological and theoretical tools that describe the connections of people, partnerships, disease transmission, the interorganizational structure of health systems, the role of social support, and social capital1. The Florida Department of Health in Orange County (DOH-Orange) developed a reproducible baseline social network analysis of patient movement across healthcare entities to gain a county-wide perspective of all actors and influences in our healthcare system. The recognition of the role each healthcare entity contributes to Orange County, Florida can assist DOH-Orange in developing facility-specific implementations such as increased usage of personal protective equipment, environmental assessments, and enhanced surveillance.

Objective:

To create a baseline social network analysis to assess connectivity of healthcare entities through patient movement in Orange County, Florida.