Quantitative Methods in Defense and National Security 2007

Using Social Network Analysis to Evaluate Public Health Preparedness
Leslie McIntosh, (Saint Louis University ), mcintold@slu.edu


For this research, I propose using social network analysis (SNA) as a viable means to gauge the preparedness level of communities who participate in multidisciplinary emergency response exercises. With millions of dollars being spent on public health preparedness programs and trainings, there are few methods by which to evaluate these programs. Further difficulties exist due to the many facets of public health preparedness which need to be assessed from individuals competencies and skill sets to organizational capacity and participation levels.

In a three-part evaluation, the awareness, performance, and integration abilities of participants are assessed. Awareness is the measurement of who knows what information; while performance is the measurement of who has what skills. Participants are rated as proficient or not proficient in their awareness and performance. The integration aspect is characterized through multiple measurements (i.e. density, centrality) using SNA and depicts how the participants relate to one another during the exercise.

Awareness proficiency is assessed using existing tools such as the National Incident Management System (NIMS) competencies, and performance proficiency is measured using metrics established from sources such as Target Capabilities List (TCL).

A social network is formed that retains information of individual awareness and performance abilities while assessing the group-level integration capacity. From this information, I look at the shape and density of the network, along with measuring the ability to disable the network. The shape of the network can take on many forms such as hierarchical or cellular, while the density of the network ranges from 0 (no participants are connected) to 1 (everyone is connected). In addition, I hypothesize that the strength of the network can also be revealed using statistical algorithms that disable the network.

Together, these data and analyzes elucidate community levels of preparedness. This information can then be used to visually and statistically depict emergency response exercise preparedness. These data then become the base-line measurements that can be used for follow-up evaluations of future exercises.

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