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Simulation-based validation of a method to detect changes in SARS-CoV-2 reinfection risk.

Belinda Lombard¹, Harry Moultrie², Juliet R C Pulliam¹

¹South African DSI-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch, South Africa.

Plos Computational Biology

|February 3, 2025

Summary

This study validates a catalytic model for tracking SARS-CoV-2 reinfection risk. The model accurately detects changes in reinfection risk but requires sufficient data, especially in smaller epidemics.

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Area of Science:

Epidemiology
Mathematical Modeling
Infectious Disease Dynamics

Background:

High global seroprevalence of SARS-CoV-2 necessitates understanding reinfection risks.
Models tracking reinfection trends must be robust against data biases.

Purpose of the Study:

To perform simulation-based validation of a catalytic model for detecting changes in SARS-CoV-2 reinfection risk.
To assess model robustness against biases from imperfect data observation and mortality.

Main Methods:

Simulated primary and reinfection datasets based on South African SARS-CoV-2 epidemic data.
Incorporated biases like imperfect observation and mortality into simulations.
Employed a Bayesian approach with a negative binomial distribution to fit the catalytic model.

Main Results:

The catalytic model successfully detected changes in reinfection risk when simulated.
Model parameters converged in most scenarios, aligning with anticipated outcomes.
Low observation probabilities (10%) led to poor convergence and low observed cases.

Conclusions:

The catalytic model is robust to imperfect observation and mortality in most scenarios.
Model performance may differ in settings with smaller epidemics; further validation is recommended.
Ensuring model parameter convergence is crucial to avoid false positives in detecting reinfection risk shifts.