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Scalable Estimation of Epidemic Thresholds via Node Sampling.

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Summary
This summary is machine-generated.

Understanding epidemic thresholds is crucial for predicting disease spread in social networks. This study introduces efficient approximation methods to overcome computational challenges in analyzing contagion processes and network inference.

Keywords:
Configuration modelEpidemic thresholdEpidemiology.NetworksRandom walkSampling

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Area of Science:

  • Network Science
  • Epidemiology
  • Statistical Inference

Background:

  • Contagious diseases spread through social contact networks, posing global public health risks.
  • The epidemic threshold determines if a disease outbreak will occur or fade.
  • Accurate estimation of epidemic thresholds is vital for public health interventions.

Purpose of the Study:

  • To investigate epidemic thresholds using statistical network inference.
  • To address computational and sampling complexities associated with epidemic threshold analysis.
  • To develop efficient and accurate approximation techniques for epidemic threshold estimation.

Main Methods:

  • Developed two statistically accurate and computationally efficient approximation techniques.
  • Utilized the Chung-Lu modeling framework for network analysis.
  • Employed random walk sampling for a data-efficient approximation method.

Main Results:

  • The proposed methods provide statistically accurate approximations of epidemic thresholds.
  • The random walk sampling method requires only a small fraction of network data.
  • Both methods demonstrate superior empirical performance compared to existing approaches.

Conclusions:

  • The developed approximation techniques effectively address the challenges in epidemic threshold analysis.
  • These methods offer computationally efficient and accurate tools for network epidemiology.
  • The random walk sampling approach enables threshold estimation even with limited network data.