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serosim: An R package for simulating serological data arising from vaccination, epidemiological and antibody kinetics

Arthur Menezes1, Saki Takahashi2, Isobel Routledge3

  • 1Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, United States of America.

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|August 14, 2023
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Summary

The R package serosim simulates serological study data using a random effects model. This tool aids researchers in designing studies and evaluating inference methods for population immunity assessment.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Immunology

Background:

  • Serological studies assess population immunity by measuring antibody titers.
  • Increasingly sophisticated analytical techniques require robust simulation tools.
  • Existing tools lack comprehensive simulation capabilities for serological data.

Purpose of the Study:

  • Introduce serosim, an open-source R package for simulating serological study data.
  • Provide a framework for evaluating inference methods and optimizing study designs.
  • Facilitate understanding of factors influencing observed antibody titers.

Main Methods:

  • Utilizes a random effects model to simulate data.
  • Allows user specification of vaccine and antibody kinetics.
  • Incorporates parameters for population demography, infection, and vaccination patterns.

Main Results:

  • serosim enables flexible simulation of complex serological scenarios.
  • The package supports the adjustment of various model inputs to represent real-world systems.
  • Facilitates the generation of synthetic data for method validation.

Conclusions:

  • serosim offers a valuable resource for planning serological studies.
  • Enhances the evaluation of statistical methods for analyzing serological data.
  • Aids in understanding population immunity dynamics and informing public health strategies.