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Nonparametric serial interval estimation with uniform mixtures.

Oswaldo Gressani1, Niel Hens1,2

  • 1Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Data Science Institute, Hasselt University, Hasselt, Belgium.

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|August 4, 2025
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Summary
This summary is machine-generated.

We developed a new nonparametric method to estimate the serial interval distribution for infectious diseases using interval-censored data. This data-driven approach is simple, computationally inexpensive, and complements existing parametric models.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Infectious Disease Modeling

Background:

  • The serial interval is crucial for understanding infectious disease transmission dynamics.
  • Estimating the serial interval distribution is challenging due to data censoring.
  • Current methods often rely on parametric models, limiting flexibility.

Purpose of the Study:

  • To present a fully data-driven, nonparametric methodology for estimating the serial interval distribution.
  • To address challenges posed by interval-censored serial interval data.
  • To provide a user-friendly and computationally efficient tool for epidemiological analysis.

Main Methods:

  • Developed a nonparametric estimator for the cumulative distribution function of the serial interval.
  • Utilized a class of uniform mixtures for the estimation.
  • Employed the bootstrap method for constructing confidence intervals.
  • Algorithms are designed for simplicity, stability, and computational efficiency.

Main Results:

  • The proposed nonparametric method accurately estimates the serial interval distribution from interval-censored data.
  • Closed-form solutions are available for estimating serial interval features.
  • The method is implemented in the user-friendly EpiDelays R package.

Conclusions:

  • This nonparametric approach offers a valuable alternative to parametric methods for serial interval estimation.
  • The methodology is flexible and can be applied to various epidemiological delay modeling scenarios.
  • The EpiDelays package facilitates the practical implementation of this robust estimation technique.