Computation of COVID-19 epidemiological data in Hungary using dynamic model inversion

TL;DR

This paper presents a method to estimate COVID-19 epidemiological parameters in Hungary using a control theory-based dynamic model inversion, relying solely on hospitalization data to infer infection spread and reproduction numbers.

Contribution

It introduces a novel application of system and control theory techniques to estimate epidemic data from limited hospitalization information in Hungary.

Findings

Estimated the number of latent infected individuals over time.

Tracked the time-dependent reproduction number.

Validated results using literature data.

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.