Simulation-based Validation of a Method to Detect Changes in SARS-CoV-2 Reinfection Risk
Belinda Lombard, Harry Moultrie, Juliet R.C. Pulliam, Cari van Schalkwyk.
Abstract
Given the high global seroprevalence of SARS-CoV-2, understanding the risk of reinfection has become increasingly important. Models developed to track trends in reinfection risk should be robust against possible biases arising from imperfect data observation processes. We performed simulation-based validation of an existing catalytic model designed to detect changes in the risk of reinfection by SARS-CoV-2. The catalytic model assumes the risk of reinfection is proportional to observed infections. Validation involved using simulated primary infections, consistent with the number of observed infections in South Africa.
Introduction
The COVID-19 pandemic has had catastrophic health, economic, and social impact, directly affecting billions of lives. As of July 2023, the pandemic had resulted in at least 6.9 million deaths globally. Five major waves of infections were observed in South Africa. The first wave, driven by the original strain, peaked in mid-2020 and was followed by a second wave driven by the Beta variant towards the end of 2020.
Materials and Methods:
The catalytic model assesses changes in reinfection risk by SARS-CoV-2 by accounting for the number of previously infected individuals and the changing infection risk through time. Reinfections are defined as two positive tests at least 90 days apart, a period chosen to ensure that successive positive tests result from reinfection rather than prolonged viral shedding. Consequently, the model sets the risk of reinfection at zero for the first 90 days, and thereafter, it is proportional to the 7-day moving average of observed infections.
Discussion:
In this study we performed simulation-based validation on a method used for real-time monitoring of SARS-CoV-2 reinfections to detect changes in the risk of reinfection. The model parameters converged well under various observation biases and the model is robust when dealing with changes in observation probability for reinfections. The model showed strong parameter convergence, indicating reliable projection interval simulation, particularly when patterns seen in the simulated data were well represented during the fitting procedure.
Acknowledgments:
The authors would like to thank Yuri Munsamy, PhD of SACEMA, South Africa for providing writing assistance. The authors gratefully acknowledge the Centre for High Performance Computing (CHPC), South Africa, for providing computational resources to this research project. This work has benefited from input during the Clinic on Meaningful Modelling of Epidemiological Data (MMED) and the Software for the Applied Mathematical Sciences (SEAMS) workshop, both of which are part of the International Clinics on Infectious Disease Dynamics and Data (ICI3D) program. We specifically thank Carl Pearson, Tom Hladish, Arlin Stoltzfus, Shade Horn, Youngji Jo, Liz Villabona-Arenas for helpful discussions during the development of this work.
Citation: Lombard B, Moultrie H, Pulliam JR, van Schalkwyk C (2025) Simulation-based validation of a method to detect changes in SARS-CoV-2 reinfection risk. PLoS Comput Biol 21(2): e1012792. https://doi.org/10.1371/journal.pcbi.1012792
Editor: Samuel V. Scarpino, Northeastern University, UNITED STATES OF AMERICA
Received: March 12, 2024; Accepted: January 10, 2025; Published: February 3, 2025.
Copyright: © 2025 Lombard et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: There are no primary data in the paper, all datasets were simulated. All simulated data and code to reproduce the results are available at https://github.com/SACEMA/reinfectionsBelinda.
Funding: JRCP and CVS are supported by the Department of Science and Innovation and National Research Foundation, South Africa. This work was also supported by the Wellcome Trust (grant no. 221003/Z/20/Z) in collaboration with the Foreign, Commonwealth and Development Office, United Kingdom. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
