ABSTRACT
In this paper, a model-based method is proposed for the reconstruction of non-measured epidemic data of the COVID-19 pandemic in Hungary. Only the data series showing the daily number of hospitalized people are used for the reconstruction together with a nonlinear dynamical model of epidemic spread containing 8 compartments. The unknown input of the model is the infection rate, which is computed through the solution of a feedback linearization-based asymptotic output tracking problem, where the reference is the actually observed number of hospitalized people. Computations show good match with of hospitalized people. Computations show good match with previous reconstruction results, and show a roughly 3.5-4-fold underdetection of infections until the Omicron wave. © 2022 IEEE.
ABSTRACT
In this paper, a predictive-control-based approach is proposed for pandemic mitigation with multiple control inputs. Using previous results on the dynamical modeling of symptom-based testing, the testing intensity is introduced as a new manipulable input to the control system model in addition to the stringency of non-pharmaceutical measures. The control objective is the minimization of the severity of interventions, while the main constraints are the bounds on the daily number of hospitalized people and on the total number of available tests. For the control design and simulation, a nonlinear dynamical model containing 14 compartments is used, where the effect of vaccination is also taken into consideration. The computation results clearly show that the optimization-based design of testing intensity significantly reduces the stringency of the measures to be introduced to reach the control goal and fulfill the prescribed constraints.
ABSTRACT
In this paper, we estimate epidemiological data of the COVID-19 pandemic in Hungary using only the daily number of hospitalized patients, and applying well-known techniques from systems and control theory. We use a previously published and validated compartmental model for the description of epidemic spread. Exploiting the fact that an important subsystem of the model is linear, first we compute the number of latent infected persons in time. Then an estimate can be given for the number of people in other compartments. From these data, it is possible to track the time dependent reproduction numbers via a recursive least squares estimate. The credibility of the obtained results is discussed using available data from the literature. © 2021 IEEE.