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Related Experiment Video

Updated: Jul 15, 2025

Estimating Virus Production Rates in Aquatic Systems

Estimating Virus Production Rates in Aquatic Systems

Published on: September 22, 2010

Estimating seroconversion rates accounting for repeated infections by approximate Bayesian computation.

Peter F M Teunis¹, Yuke Wang¹, Kristen Aiemjoy^2,3

¹Hubert Department of Global Health, Center for Global Safe WASH, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA.

Statistics in Medicine

|September 27, 2023

Summary

This study introduces a new quantitative model to estimate infection incidence by analyzing serum antibody responses, accounting for past infections. The method improves accuracy, especially in high-infection settings where other models falter.

Keywords:

approximate Bayesian computation empirical distribution function reinfection seroincidence

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Last Updated: Jul 15, 2025

Estimating Virus Production Rates in Aquatic Systems

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Published on: July 28, 2022

Area of Science:

Epidemiology
Immunology
Computational Biology

Background:

Current infection incidence models often fail to account for an individual's cumulative infection history.
This limitation complicates the accurate determination of antibody concentration distributions.

Purpose of the Study:

To develop a novel quantitative model for inferring infection incidence using serum antibody responses.
To address the limitations of existing methodologies by incorporating the effects of repeated infections.

Main Methods:

Utilized approximate Bayesian computation to simulate cross-sectional antibody responses.
Compared simulated data with observed data, including the impact of repeated infections.
Employed Kolmogorov deviance for goodness-of-fit assessment of empirical distribution functions.

Main Results:

The novel within-host model's predictions closely matched observed antibody distributions in a well-characterized population.
Findings align with likelihood-based methods under low infection pressure (e.g., pertussis in Europe).
Simulations indicated that likelihood-based methods underestimate infection incidence under high infection pressure.

Conclusions:

The new methodology offers computational efficiency comparable to existing likelihood-based analyses.
It enables joint estimation of antibody noise parameters.
This approach significantly advances infection incidence estimation and understanding of disease dynamics.