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

Andrey Y Lokhov1, Marc Mézard2, Hiroki Ohta1

  • 1LPTMS, Université Paris-Sud and CNRS-UMR 8626, 91405 Orsay, France.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
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We developed a new algorithm to pinpoint the source of an epidemic outbreak using contact networks and spread data. This method improves accuracy, even with partial network information, aiding in outbreak source estimation.

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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.