# Contagion Source Detection in Epidemic and Infodemic Outbreaks: Mathematical Analysis and Network Algorithms

arXiv: https://arxiv.org/abs/2307.04061 · HTML: https://mlsanity.com/paper/2307.04061/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2023.

## Built on by (most cited first, 1 of 1)

- [TrumorGPT: Graph-Based Retrieval-Augmented Large Language Model for Fact-Checking](https://mlsanity.com/paper/2505.07891/) (2025)

## Abstract (arXiv)

This monograph provides an overview of the mathematical theories and computational algorithm design for contagion source detection in large networks. By leveraging network centrality as a tool for statistical inference, we can accurately identify the source of contagions, trace their spread, and predict future trajectories. This approach provides fundamental insights into surveillance capability and asymptotic behavior of contagion spreading in networks. Mathematical theory and computational algorithms are vital to understanding contagion dynamics, improving surveillance capabilities, and developing effective strategies to prevent the spread of infectious diseases and misinformation.