Epidemiological and Threat Network Analysis

# Epidemiological and Threat Network Analysis **Domain:** Austin Research / Concepts **Doc Type:** Corpus analysis **Source basis:** Supplied research cluster with linked references; see the [[wiki/Austin Research Evidence Map|evidence map]] for verification scope. **Epidemiological and Threat Network Analysis** is the corpus’s comparison between reconstructing hidden transmission networks and reconstructing networks supporting hostile activity. Both start with incomplete, delayed, noisy, and unevenly distributed observations; both use relationships and temporal structure to identify consequential changes and possible intervention points. An epidemic model may infer transmission through hosts, contacts, geography, and time. Counter-IED intelligence may infer relationships among production, financing, transport, placement, and reconnaissance. Detection of an observed event opens investigation into a larger network whose structure is only partly visible. Barber’s ONR, DHS, and [[wiki/Defense Threat Reduction Agency|DTRA]] grant sequence documents research continuity across these uses. Meyers’ BSVE contribution documents a specific forecasting transfer. The comparison between epidemic propagation and broader terrorist-network behavior is analytical: no identity between their governing dynamics or shared operational model follows from the comparison alone. Model transfer should be tested against held-out observations, changing behavior, source bias, and the effect of intervention. Deliberate deception is especially important in adversarial networks; biological transmission has different causal constraints. An anomaly or inferred link also has a different meaning from an identified actor or attributed attack. ## Connected entries [[wiki/Multi-Use Inference and Control|Multi-Use Inference and Control]] · [[wiki/Task Force ODIN|Task Force ODIN]] · [[wiki/Lauren Ancel Meyers|Lauren Ancel Meyers]] · [[wiki/Suzanne Barber|Suzanne Barber]] · [[wiki/Sensor and Social Networks Armed to Detect and Defend against Terrorist Attacks|Sensor and Social Networks Armed to Detect and Defend against Terrorist Attacks]] · [[wiki/Biosurveillance|Biosurveillance]] · [[wiki/Detection Before Attribution|Detection Before Attribution]] · [[wiki/State Estimation|State Estimation]] · [[wiki/Law Enforcement Analysis Portal|Law Enforcement Analysis Portal]] · [[wiki/Support for Predicting Improvised Explosive Device Attacks|Support for Predicting Improvised Explosive Device Attacks]] ## Sources and corpus context - [[research/The Austin Executable Loop|The Austin Executable Loop — master document]] - [[research/Oden Institute - Machine Learning, Medicine, and Terrorism|Oden Institute - Machine Learning, Medicine, and Terrorism]] - [[research/ODIN - Observe, Detect, Identify, Neutralize|ODIN - Observe, Detect, Identify, Neutralize]] - [Task Force ODIN Transfer of Authority](https://www.centcom.mil/MEDIA/NEWS-ARTICLES/News-Article-View/Article/1131834/task-force-odin-transfer-of-authority/) - [Prof. Suzanne Barber Awarded DTRA Grant for Work on Surety BioEvent App](https://ece.utexas.edu/news/prof-suzanne-barber-awarded-dtra-grant-work-surety-bioevent-app) - [Flu Season Forecasts Could Be More Accurate with Access to Health Care Companies’ Data](https://news.utexas.edu/2018/09/19/this-data-source-could-enable-better-flu-forecasts/) - [Lauren Meyers — Applied Mathematics Group](https://amg.oden.utexas.edu/members/lauren-meyers/) **Cluster:** [[wiki/Austin Executable Loop|Austin Executable Loop]] · [[wiki/Austin Research Evidence Map|Austin Research Evidence Map]]  ## Where network analysis becomes operational The surveillance-field essay adds concrete interfaces to the comparison. LEAP combines police records across jurisdictions; the watchlist encounter process turns a confirmed match into notification and additional analysis; syndromic systems turn clinical observations into population alerts. Each expands an initial observation into a larger analytical context. The shared problem is estimating a partly hidden state from uneven evidence. The entity being estimated, mechanisms of spread or association, consequences of a false match, and available interventions still need separate models. [[wiki/Surveillance Record Persistence|Persistence]] adds a temporal issue: later inference may depend on an observation whose original interpretation has already changed. **Follow:** [[wiki/Law Enforcement Analysis Portal|Law Enforcement Analysis Portal]] · [[wiki/Watchlist Encounter Process|Watchlist Encounter Process]] · [[wiki/Population State Estimation|Population State Estimation]] · [[wiki/Surveillance Record Persistence|Surveillance Record Persistence]] **Source:** [[research/The Austin Surveillance Field|The Austin Surveillance Field]]; [City of Austin — LEAP interlocal agreement, recitals and §§2–7](https://services.austintexas.gov/edims/document.cfm?id=176641); [GAO-26-108650 — Terrorist Watchlist, January 12, 2026; Appendix II, pp. 37–38](https://www.gao.gov/assets/gao-26-108650.pdf).