Speaker
Description
The determinants of influenza transmission at the level of individual contacts remain poorly understood. We analyze a unique dataset of controlled ferret transmission experiments in which one or two infected donors interact with four susceptible recipients for varying durations (typically 1–4 hours), combining high-resolution video and longitudinal viral load measurements to link time-resolved behavior with infection outcomes.
Our approach integrates machine learning–based behavioral tracking using Social LEAP Estimates Animal Poses (SLEAP) with statistical and mechanistic modeling to quantify how contact patterns influence infection risk. We reconstruct individual-level exposure histories and relate them to infection outcomes to estimate associations between specific contact patterns and transmission, and to explore parsimonious transmission-kernel formulations.
The primary objective is to determine which features of behavior and infectiousness are identifiable from these data, and which are fundamentally confounded. More broadly, this work asks a general question: given realistic behavioral and biological data, what can we actually learn about transmission mechanisms?
