Improved parameter estimation in epidemic modeling using continuous data assimilation methods

In this work, we investigate the numerical performance of a continuous data assimilation algorithm based on ideas from the feedback control theory of dynamical systems, in the context of the SEIR (Susceptible-Exposed-Infectious-Recovered) compartmental mathematical model of epidemics. The motivation behind this work is to propose a new approach that estimates the infection rate parameter in the SEIR model. However, this model may have limited predictive value due to the idealized assumptions underlying it and measurement errors in the experimental data and parameters. We first implement this model as a paradigm because of its application to more realistic epidemic models, then we present our theoretical results. In addition, we provide numerous numerical simulations and significant examples to illustrate the first part of our work.