After an epidemic outbreak, governments introduce interventions to reduce infections. In practice, the effectiveness of such measures is often reduced by frictions, such as delayed reactions, imperfect information, economic costs, and changes in population behavior. We want to gain some insights how these frictions may affect epidemic dynamics and management.
We present a compartmental epidemiological model with a rule-based control mechanism. Countermeasures are activated when certain indicators exceed given threshold values. Model components include economic losses caused by illness and by intervention measures, as well as behavioural responses that may reduce compliance over time.
Analytical results focus on stability properties and on the reproduction number under dynamic interventions. Simulation results illustrate the sensitivity of the model to key parameters and the role of the control mechanism. For some concrete data, we use scenarios inspired by the early COVID-19 outbreak in Italy.
Due to limited data on economic and psychological factors, the model is not intended yet for quantitative prediction. Instead, it provides a conceptual framework for studying trade-offs in epidemic management. We conclude with a discussion of limitations and an outlook on extensions including spatial dynamics.