Related Experiment Video
Updated: Mar 29, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
Published on: December 9, 2015
Observer design for a class of nonlinear piecewise systems. Application to an epidemic model with treatment
Abdessamad Abdelhedi1, Driss Boutat2, Lassaad Sbita1
1National Engineering School of Gabes, Research Unit of Photovoltaic, Wind and Geothermal Systems, Tunisia.
Abstract:
Susceptible Exposed Infectious and Recovered epidemic model endowed with a treatment function (SEIR-T model) is a well-known model used to reproduce the behavior of an epidemic, where the susceptible population and the exposed population need to be estimated to predict and control the propagation of a contagious disease. This paper focuses on the nonlinear observer design for a class of nonlinear piecewise systems including SEIR-T models. For this purpose, two changes of coordinates are provided to transform the considered systems into an extended nonlinear observer normal form, on which a high gain observer can be applied. Then, the proposed method is applied to a SEIR-T model. Finally, simulation results are given to show its efficiency.
More Related Videos
Related Concept Videos
Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Modeling with Differential Equations
Mechanistic Models: Compartment Models in Individual and Population Analysis
Steps in Outbreak Investigation
Pharmacodynamic Models: Additive and Proportional Drug Effect Model

