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

TL;DR

This paper reviews mathematical theories and network algorithms for detecting contagion sources in large networks, enhancing understanding and prediction of epidemic and infodemic spreads.

Contribution

It introduces a unified framework combining network centrality and statistical inference for accurate contagion source detection and spread prediction.

Findings

Network centrality effectively identifies contagion sources.

Mathematical models improve surveillance and prediction accuracy.

Algorithms enable tracing and forecasting contagion trajectories.

Abstract

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.