Data-Driven Learning of Contact Networks for Targeted Vaccination in Outbreaks
Poster presentation at The Pandemic Institute Scientific Meeting 2025
Presented by Sepehr Elahi & Paula Mürmann at PISM 2025.
Originally presented and published at ICML 2025, see our ICML project page.
| Paper PDF | Poster |
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| Link | Link |
TL;DR: We develop theoretical algorithms to learn the contact network from outbreak data and propose effective vaccination strategies. We show that our vaccination strategies outperform ring vaccinations on flu outbreak contact networks.
Summary: Imagine a contagious disease spreading through a population, but the contact network—who is infecting whom—remains completely hidden. Only who gets infected and when is observed, and a limited number of vaccines must be allocated to stop the outbreak as quickly as possible. Our work tackles exactly this: how to choose effective vaccinations when the contact network is hidden. We first develop an algorithm that reconstructs the hidden contact network using only observations of who gets sick and when. With the learned network in hand, we propose two vaccination strategies that determine who to vaccinate: one that’s mathematically optimal but slow to compute on big networks, and another that’s much quicker and almost as good. We show that our learn-to-vaccinate approach effectively controls simulated outbreaks across a range of settings, including real-world contact networks from flu epidemics. This early work paves the way for smarter, data-efficient vaccination strategies that can support faster, more effective outbreak response—even when the underlying contact network is unknown.
