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Estimating and interpreting secondary attack risk: Binomial considered biased.

Yushuf Sharker1, Eben Kenah2

  • 1Division of Biometrics, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland, United States of America.

Plos Computational Biology
|January 20, 2021
PubMed
Summary
This summary is machine-generated.

Estimating secondary attack risk (SAR) using simple binomial models is flawed. These models overestimate infection risk and provide inaccurate confidence intervals, even for small probabilities, failing to account for multiple transmission generations.

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Area of Science:

  • Epidemiology
  • Infectious Disease Transmission

Background:

  • Household secondary attack risk (SAR) is crucial for understanding disease spread.
  • Binomial models are commonly used to estimate SAR, assuming infections stem solely from the primary case.

Purpose of the Study:

  • To investigate the accuracy of binomial models in estimating household SAR.
  • To demonstrate the impact of multiple transmission generations on SAR estimation.

Main Methods:

  • Utilized probability generating functions and simulations.
  • Employed longitudinal chain binomial models and pairwise survival analysis for accurate estimation.

Main Results:

  • Binomial models overestimate SAR and yield biased confidence intervals, even with small probabilities.
  • The proportion of infected household members can exceed SAR due to multiple transmission generations.

Conclusions:

  • Standard binomial models for SAR estimation are inadequate.
  • Accurate SAR estimation requires models that account for multiple transmission generations and external infection risks.