Seungho Baek, Haneol Cho, SangChul Lee, Myeongsu Yoo, Donghyok Kwon, KyuHwan Lee, Yeonju Kim, Chansoo Kim
Abstract
COVID-19, an unprecedented global pandemic, has caused successive waves that pose unique challenges to public health and epidemiological research. Traditional Susceptible–Infected–Recovered (SIR) models often struggle to capture these complex dynamics, especially given that the virus has spawned multiple sub-variants.
Introduction
Since the outbreak of the novel coronavirus disease (COVID-19), which was first reported in Wuhan, China, in December 2019 [1–3], a vast array of mathematical and computational models have been developed to understand and predict the pandemic’s course.
Materials and methods
In this study, we employ the following logistic form as the foundational basis for modeling infectious diseases.
Results
Our integrated model is designed to capture the unique epidemiological properties of each variant by fitting separate SIR models (approximated by a logistic function) to the data corresponding to during the dominance period of each variant.
Discussion
We have demonstrated that emerging and dominant variants are significantly associated with the epidemiological waves of COVID-19. Incorporating these variants into a single model markedly enhances the performance relative to a single-strain SIR approach.
Conclusion
In this study, we demonstrated that incorporating variant-specific data into an integrated SIR model significantly enhances its performance in representing the dynamics of COVID-19 outbreaks. Our analysis confirms that the emergence and dominance of new variants are closely associated with the epidemiological waves observed during the pandemic.
Citation: Baek S, Cho H, Lee S, Yoo M, Kwon D, Lee K, et al. (2026) Integrated disease model considering mutation-induced infection waves with COVID-19 cases. PLoS One 21(3): e0341667. https://doi.org/10.1371/journal.pone.0341667
Editor: Yury E. Khudyakov, Centers for Disease Control and Prevention, UNITED STATES OF AMERICA
Received: July 28, 2025; Accepted: January 10, 2026; Published: March 6, 2026
Copyright: © 2026 Baek 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: All data and analysis code used in this study are publicly available. The analysis code and processed datasets are deposited at GitHub: https://github.com/alcyonesha/COVID19_variant_wave_model. Raw COVID-19 case data were obtained from Our World in Data (https://ourworldindata.org/). Viral variant data were obtained from GISAID (https://gisaid.org/); access requires registration and acceptance of the GISAID Database Access Agreement. We gratefully acknowledge the contributors of sequences to GISAID for making viral genomic data openly available.
Funding: This research and authors (SB, HC, SL, and CK) are supported by research grants Nos. RS-2023-00262155; 2024-00339583; 2024-00460980; and 2025-02304717 (IITP) funded by the Korea government (Ministry of Science and ICT).
Competing interests: No authors have competing interests.
