Structural and practical parameter identification of an immuno-respiratory epidemic model: sensitivity analysis approach (pp.277-311)

In this investigation, we introduce a sophisticated multi-scale immune-respiratory transmission model that incorporates the intricate nested rule.We analyze the global stability of the equilibria by constructing Lyapunov functions.To comprehensively explore the identifiability of the parameters, we employ the esteemed techniques of principal component analysis and the eigenvalue method, utilizing pristine, noise-free data.We further validate the practical identifiability of these parameters by employing Monte Carlo Simulation and the approximate Bayesian calculation, leveraging the wealth of available dataset.Moreover, the results obtained from our meticulous sensitivity analysis reinforce the identifiability of the parameters, suggesting that curtailing the replication rate and transmission rate of SARS-CoV-2 within hosts plays a pivotal role in attenuating the spread of COVID-19.