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

Area of Science:

• Epidemiology
• Network Science
• Computer Science

Background:

• Estimating the origin of an epidemic outbreak is crucial for controlling its spread in various networks.
• Existing methods for infection source identification face challenges, especially with incomplete data.

Purpose of the Study:

• To introduce an efficient and accurate algorithm for identifying the source of an epidemic outbreak.
• To improve upon current approaches for infection source estimation in contact networks.

Main Methods:

• Developed a novel inference algorithm based on dynamic message-passing equations.
• Applied the algorithm to epidemic spread data within a susceptible-infected-recovered (SIR) model framework.
• Evaluated algorithm performance on partial network snapshots.

Main Results:

• The proposed algorithm significantly improves the accuracy of infection source estimation.
• The method demonstrates efficiency and effectiveness even when only a portion of the network is observed.
• Outperforms existing approaches in identifying the epidemic's origin.

Conclusions:

• The dynamic message-passing algorithm offers a powerful tool for outbreak source estimation.
• This approach is robust and scalable for real-world applications in computer and social networks.
• Effective even with limited observational data, facilitating timely intervention.