Utility of compartmental models to test the competing hypotheses of pathogen evolution and human intervention
Bibliographic Data
| ID | 22071230 |
|---|---|
| Authors | Barsha Saha (0000-0001-5605-4583, University of Missouri–Kansas City), Majid Bani-Yaghoub (0000-0002-7627-3940, University of Missouri–Kansas City, corresponding author), Chandranath Podder (University of Dhaka) |
| Year | 2026 |
| Volume | 13 |
| Pages | 1702428-1702428 |
| Publication date | 2026-01-13 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Public Health (JOURNAL) |
| Journal identifiers | ISSN: 2296-2565 • E-ISSN: 2296-2565 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fpubh.2025.1702428 |
| PMID | 41626397 |
| OpenAlex | W7123799544 |
| Language | EN |
| References cited | 76 |
Compartmental models are essential for studying host-pathogen dynamics, evaluating intervention effectiveness, and predicting infection trends. However, the utility of these models for testing competing hypotheses is often overlooked. To address this, we propose a new model-based hypothesis testing (MBHT) approach, which uses compartmental models to evaluate the hypotheses in epidemiology. In our case, using the COVID-19 pandemic as a case study, we formulate hypotheses of SARS-CoV-2 mutation and construct a transmission model to test them. In addition to analyzing steady-state stability, deriving the basic reproduction number, and identifying a backward bifurcation, the model is fitted to seven peaks of U.S. COVID-19 data, each corresponding to periods of viral mutation and morbidity peaks. The estimated posterior probabilities reveal that Short-term within host selection primarily shaped mutations during the early pandemic stages, followed by immune selection driven by natural and vaccine-induced immunity. In later stages, mutations aligned with vaccination-induced virulence and transmission-virulence correlation, while the declining virulence and immune selection partially explained the final stages of SARS-CoV-2 mutation. In conclusion, model-based hypothesis testing offers a powerful yet underutilized approach to uncovering drivers of viral mutation and gaining deeper insights into pathogen evolution during outbreaks
Mutation · Mutation Accumulation · Mutation rate · Natural selection · Pandemic · Pathogen · Virulence · COVID-19 epidemiological studies · Evolution and Genetic Dynamics · SARS-CoV-2 and COVID-19 Research
IX. On the problem of the most efficient tests of statistical hypotheses
Inferring the effectiveness of government interventions against Covid-19
Bayes Factors
Evaluating the Impact of Sars-CoV-2 Variants on the Covid-19 Epidemic and Social Restoration in the United States
Assessment of Retrospective Covid-19 Spatial Clusters with Respect to Demographic Factors
Dominance statistics
| Highly cited | No |
|---|