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serocalculator, an R package for estimating seroincidence from cross-sectional serological data.

Kristina W Lai1, Chris Orwa2, Jessica C Seidman3

  • 1Department of Public Health Sciences, University of California Davis School of Medicine, Davis, CA, USA.

Medrxiv : the Preprint Server for Health Sciences
|June 12, 2025
PubMed
Summary
This summary is machine-generated.

Estimating new infections (seroincidence) from antibody data is challenging due to waning immunity. The serocalculator R package provides a robust statistical framework to accurately calculate seroincidence rates from serological surveys.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Infectious Disease Modeling

Background:

  • Seroincidence, the rate of new infections, is crucial for tracking pathogen transmission.
  • Estimating seroincidence from cross-sectional data is complex due to antibody decay, cross-reactivity, and individual differences.
  • Accurate seroincidence estimation is vital for effective public health interventions.

Purpose of the Study:

  • To introduce serocalculator, an open-source R package designed for estimating seroincidence rates.
  • To provide a user-friendly tool that addresses the complexities of seroincidence estimation from serological data.

Main Methods:

  • Utilizes a likelihood-based framework to model antibody decay.
  • Incorporates biological variability and measurement error into the estimation process.
  • Supports estimation using single or multiple biomarkers for overall and stratified analyses.

Main Results:

  • The serocalculator package enables robust estimation of seroincidence rates from cross-sectional serological data.
  • It accounts for antibody waning, biological variability, and measurement error.
  • The package is computationally efficient and includes a graphical user interface (R Shiny) for enhanced usability.

Conclusions:

  • serocalculator offers a powerful and accessible solution for researchers and public health professionals to accurately estimate seroincidence.
  • The package facilitates better understanding of pathogen transmission dynamics and informs public health strategies.
  • Its open-source nature and comprehensive documentation promote widespread adoption and application.