Reconstruction of epidemiological data using nonlinear control

2022. 10. 04. 17:00
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Polcz Péter

Péter Polcz, Balázs Csutak, Gábor Szederkényi (Pázmány P. Catholic Univ., Budapest)

It is known that infection numbers of the COVID epidemic have been significantly underdetected. Therefore, we used model-based nonlinear control for the analysis of past epidemic data to try to reconstruct more realistic data. The computed input of the controlled system
operator is the disease transmission coefficient and the output is the observed number of hospitalized people. Moreover, it is possible to estimate the population in non-measured compartments such as the number of latent, pre-symptomatic, or asymptomatic people during the epidemic waves.

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