An Egocentric Network Contact Tracing Experiment: Testing Different Procedures to Elicit Contacts and Places

Contact tracing is one of the oldest social network health interventions used to reduce the diffusion of various infectious diseases. However, some infectious diseases like COVID-19 amass at such a great scope that traditional methods of conducting contact tracing (e.g., face-to-face interviews) remain difficult to implement, pointing to the need to develop reliable and valid survey approaches. The purpose of this research is to test the effectiveness of three different egocentric survey methods for extracting contact tracing data: (1) a baseline approach, (2) a retrieval cue approach, and (3) a context-based approach. A sample of 397 college students were randomized into one condition each. They were prompted to anonymously provide contacts and populated places visited from the past four days depending on what condition they were given. After controlling for various demographic, social identity, psychological, and physiological variables, participants in the context-based condition were significantly more likely to recall more contacts (medium effect size) and places (large effect size) than the other two conditions. Theoretically, the research supports suggestions by field theory that assume network recall can be significantly improved by activating relevant activity foci. Practically, the research contributes to the development of innovative social network data collection methods for contract tracing survey instruments

Social movements as networks of communication episodes

Social movements (SMs) are common, yet complex phenomenon of study, generating eclectic and even conflicting perspectives on what actually constitutes a SM. This notion points towards the need of an inclusive framework that attempts to talk with rather than past conflicting perspectives. The purpose of this dissertation is to develop a hybrid theoretical framework that incorporates three SM perspectives: (1) SMs as aggregates, (2) SMs as networks, and (3) SMs as symbolic interactions. I argue that a framework of SMs as networks communication episodes (CAMs) is one way to build a successful hybrid approach, arguing that SMs consist of relationships between and within actors and events. In order to put the CAM framework to use, I used multidimensional exponential random graph modeling (MERGM) to analyze four different SMS: (1) 1970s US Energy Policy Domain, (2) 1970s US Health Policy Domain, (3) 1980s Anti-Stalinist mobilization in Poland, and (4) 1980s US Labor Policy Domain. Multidimensional network simulation was used to determine which organizing patterns correlate to instrumental and expressive theories of collective action and MERGM was used to uncover the dominant multidimensional organizing patterns in the empirical data behind each SM. Results revealed that most collective action events were organized by single organizations across all four SMs and that the Polish SM was the only movement out of the four that contain positive estimates of parameters conducive to network theories of collective action. Based on these results, a working model of factors that are theorized to influence the CAM structure is proposed, along with an application to the Anti-Stalinist mobilization in Poland and anti-Three Mile Island nuclear power plant mobilization. Moreover, based on different patterns in the CAM framework, a typology of different modes of organizing for collective action is developed, challenging a recent and common perspective of collective action as either organized or un-organized

The Impact of Contact Tracing on the Spread of COVID-19: An Egocentric Agent-Based Model

At its core, contact tracing is a form of egocentric network analysis (ENA). One of the biggest obstacles for ENA is informant accuracy (i.e., amount of true contacts identified), which is even more prominent for interaction-based network ties because they often represent episodic relational events, rather than enduring relational states. This research examines the effect of informant accuracy on the spread of COVID-19 through an egocentric, agent-based model. Overall when the average person transmits COVID-19 to 1.62 other people (i.e., the R0), they must be, on average, 75% accurate with naming their contacts. In higher transmission contexts (i.e., transmitting to at least two other people), the results show that multi-level tracing (i.e., contact tracing the contacts) is the only viable strategy. Finally, sensitivity analysis shows that the effectiveness of contact tracing is negatively impacted by the timing and overall percent of asymptomatic cases. Overall, the results suggest that if contact tracing is to be effective, it must be fast, accurate, and accompanied by other interventions like mask-wearing to drive down the average R0

A tutorial for modeling the evolution of network dynamics for multiple groups

Researchers have been increasingly taking advantage of the stochastic actor-oriented modeling framework as a method to analyze the evolution of network ties. Although the framework has proven to be a useful method to model longitudinal network data, it is designed to analyze a sample of one bounded network. For group and team researchers, this can be a significant limitation because such researchers often collect data on more than one team. This paper presents a nontechnical and hands-on introduction for a meta-level technique for stochastic actor-oriented models in RSIENA where researchers can simultaneously analyze network drivers from multiple samples of teams and groups. Moreover, we follow up with a multilevel Bayesian version of the model when it is appropriate. We also provide a framework for researchers to understand what types of research questions and theories could be examined and tested

An Egocentric Network Contact Tracing Experiment: Testing Different Procedures to Elicit Contacts and Places

Contact tracing is one of the oldest social network health interventions used to reduce the diffusion of various infectious diseases. However, some infectious diseases like COVID-19 amass at such a great scope that traditional methods of conducting contact tracing (e.g., face-to-face interviews) remain difficult to implement, pointing to the need to develop reliable and valid survey approaches. The purpose of this research is to test the effectiveness of three different egocentric survey methods for extracting contact tracing data: (1) a baseline approach, (2) a retrieval cue approach, and (3) a context-based approach. A sample of 397 college students were randomized into one condition each. They were prompted to anonymously provide contacts and populated places visited from the past four days depending on what condition they were given. After controlling for various demographic, social identity, psychological, and physiological variables, participants in the context-based condition were significantly more likely to recall more contacts (medium effect size) and places (large effect size) than the other two conditions. Theoretically, the research supports suggestions by field theory that assume network recall can be significantly improved by activating relevant activity foci. Practically, the research contributes to the development of innovative social network data collection methods for contract tracing survey instruments

A Dynamic Social Network Experiment with Multi-team Systems

This paper describes the use of VBS High-Fidelity 3D Game to perform experiments on multi-team systems. Multi-team systems (MTS) are a natural part of human social phenomena and online social networks as people form groups with shared goals and interests. We gathered data on human players (on communications and interactions) who were engaged in a VBS game scenario. Using Relational Event Modeling (REM), we analyzed the results. The results suggest some synchronization and cross-team communication have both direct effects with team performance and, in some cases, can moderate the effect of false information in environments of uncertainty