Dr. Tara Prezioso: Agent-Based Temporal Network Model for PEDV | Ep. 234

In this episode of The Swine Health Blackbelt Podcast, Dr. Tara Prezioso, Graduate Research and Teaching Assistant at the University of Illinois Urbana-Champaign, explains how agent-based temporal network models can track direct and indirect PEDV transmission. She discusses daily movement data, outbreak validation, machine learning, high-risk premises, and biosecurity scenarios that could support stronger disease preparedness. Listen now on all major platforms!

"Daily network data can reveal how direct and indirect movements shape PEDV transmission and help identify which premises face infection next."

𝗠𝗲𝗲𝘁 𝘁𝗵𝗲 𝗴𝘂𝗲𝘀𝘁: Dr. Tara Prezioso is a Graduate Research and Teaching Assistant at the University of Illinois Urbana-Champaign. A veterinarian with an MPH in Epidemiology and an applied master’s degree in Statistics, she studies veterinary epidemiology, infectious disease modeling, and statistical analysis. Learn more about PEDV network modeling with Dr. Tara Prezioso on The Swine Health Blackbelt Podcast, available on all major platforms.

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What you will learn:

• (00:00) Highlight
• (01:20) Introduction
• (03:11) Vehicle movement data
• (03:38) Daily network resolution
• (05:39) Model validation
• (06:55) Disease prediction
• (09:56) Biosecurity pathways
• (12:39) Closing thoughts

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* Boehringer Ingelheim

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