# Inferring the origin of an epidemic with a dynamic message-passing algorithm

arXiv: https://arxiv.org/abs/1303.5315 · HTML: https://mlsanity.com/paper/1303.5315/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Mar 2013.

## Built on by (most cited first, 5 of 5)

- [A Bayesian generative neural network framework for epidemic inference problems](https://mlsanity.com/paper/2111.03383/) (2021)
- [Learning of networked spreading models from noisy and incomplete data](https://mlsanity.com/paper/2401.00011/) (2023)
- [Backward and Forward Inference in Interacting Independent-Cascade Processes: A Scalable and Convergent Message-Passing Approach](https://mlsanity.com/paper/2310.19138/) (2023)
- [Graph Neural Networks for Source Detection: A Review and Benchmark Study](https://mlsanity.com/paper/2512.20657/) (2025)
- [Network World Models as Environments for Algorithm Design on Complex Systems](https://mlsanity.com/paper/2610.01048/) (2026)

## Abstract (arXiv)

We study the problem of estimating the origin of an epidemic outbreak -- given a contact network and a snapshot of epidemic spread at a certain time, determine the infection source. Finding the source is important in different contexts of computer or social networks. We assume that the epidemic spread follows the most commonly used susceptible-infected-recovered model. We introduce an inference algorithm based on dynamic message-passing equations, and we show that it leads to significant improvement of performance compared to existing approaches. Importantly, this algorithm remains efficient in the case where one knows the state of only a fraction of nodes.