On the estimation of the time-dependent transmission rate in epidemiological models

Abstract The COVID-19 pandemic highlighted the need to improve the modeling, estimation, and prediction of how infectious diseases spread. Susceptible-exposed-infected-removed (SEIR)-like models have been particularly successful in providing accurate short-term predictions. this study fills a notable literature gap by exploring the following question: is it possible to incorporate a nonparametric SEIR COVID-19 model into the inverse-problem regularization framework when the transmission coefficient varies over time? our positive response considers varying degrees of disease severity, vaccination, and other time-dependent parameters. in addition, we demonstrate the continuity, differentiability, and injectivity of the operator that link the transmission parameter to the observed infection numbers. by employing Tikhonov-type regularization to the corresponding inverse problem, we establish the existence and stability of regularized solutions. numerical examples using both synthetic and real infection data from Chicago and Canada illustrate the accuracy of the model estimation and its ability to fit the data effectively.