Title : Utility of infection disease compartmental models to test the competing hypotheses of pathogen evolution and human intervention
Abstract:
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.
