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Ritabrata Dutta1, Antonietta Mira1,2, Jukka-Pekka Onnela3

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Summary
This summary is machine-generated.

This study uses network analysis and approximate Bayesian computation to understand disease spread. The method effectively infers epidemic parameters and initial infection points, performing better on complex, heterogeneous networks.

Keywords:
Bayesian inferenceapproximate Bayesian computationepidemicsnetworkspreading process

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Area of Science:

  • Epidemiology
  • Network Science
  • Computational Biology

Background:

  • Understanding disease spread relies on analyzing interaction networks.
  • Network structure significantly influences the dynamics of infectious disease transmission.
  • Previous methods often struggle with inferring epidemic parameters and origins.

Purpose of the Study:

  • To develop a novel inference method for simultaneously estimating epidemic spreading parameters and the initial infected node.
  • To assess the performance of this method across various network topologies and epidemic models.
  • To evaluate the applicability of the method to real-world contact and social networks.

Main Methods:

  • Utilized approximate Bayesian computation (ABC), a likelihood-free inference technique.
  • Applied the method to simulated epidemics on synthetic networks.
  • Tested the approach on empirical network data, including a village social network and an online social network.

Main Results:

  • The inference scheme accurately identifies spreading parameters and the initial infected node.
  • The method demonstrates robustness across different network structures and epidemic processes.
  • Inference was found to be more efficient on heterogeneous network topologies compared to homogeneous ones.

Conclusions:

  • The developed approximate Bayesian computation method offers a powerful, network-agnostic approach for epidemic analysis.
  • The enhanced performance on heterogeneous networks suggests strong potential for real-world applications.
  • This work advances the prediction and control of infectious disease outbreaks.