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Related Experiment Video

Updated: Dec 15, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
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Immunization strategies in networks with missing data.

Samuel F Rosenblatt1,2, Jeffrey A Smith3, G Robin Gauthier3

  • 1Department of Computer Science, University of Vermont, Burlington, Vermont, United States of America.

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|July 10, 2020
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Summary

Network immunization strategies are effective but often impractical due to incomplete network data. Global strategies like degree immunization are best, but stochastic methods like acquaintance immunization excel with significant data loss.

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Area of Science:

  • Epidemiology
  • Network Science
  • Computational Social Science

Background:

  • Network-based interventions are cost-efficient for controlling contagions.
  • Real-world network data is often incomplete, limiting strategy implementation.

Purpose of the Study:

  • To evaluate immunization strategies using partially-observed network data.
  • To compare global versus stochastic immunization under realistic conditions.

Main Methods:

  • Simulated network immunization strategies with varying levels of missing data.
  • Comparative analysis of degree immunization (global) and acquaintance immunization (stochastic).
  • Development of a novel acquaintance immunization variant using targeted data recovery.

Main Results:

  • Global immunization strategies (e.g., degree immunization) are optimal with sufficient data.
  • Stochastic strategies (e.g., acquaintance immunization) outperform global methods when data is highly incomplete.
  • A proposed acquaintance immunization variant improves performance with increasing missing data.

Conclusions:

  • Targeted immunization is effective in practice.
  • Idealized network assumptions can overestimate data accuracy.
  • Further data collection can enhance intervention effectiveness in incomplete networks.