Observer design for a schistosomiasis model.

Area of Science:

• Epidemiology
• Control Theory
• Mathematical Biology

Background:

• Schistosomiasis infection dynamics are modeled using continuous nonlinear systems.
• Accurate state estimation is crucial for understanding disease spread and control.
• Measuring only the infected human population presents a challenge for state estimation.

Purpose of the Study:

• To develop state estimation techniques for a schistosomiasis dynamical model.
• To design observers for both known and uncertain model parameters.
• To evaluate observer performance through numerical simulations.

Main Methods:

• Construction of a simple observer and a high-gain Luenberger observer for known parameters.
• Development of a guaranteed interval observer for bounded-error contexts with uncertain parameters.
• Implementation of observers based on a canonical controller form for nonlinear dynamics.

Main Results:

• The study successfully designed and implemented observers for schistosomiasis state estimation.
• Both simple and high-gain Luenberger observers demonstrated effectiveness for known parameters.
• The interval observer provided guaranteed bounds for uncertain system parameters.

Conclusions:

• The proposed observer designs are effective for schistosomiasis dynamical models.
• These methods enhance the ability to estimate disease states even with limited measurements.
• The findings contribute to better disease surveillance and control strategies.