Transferring control strategies in epidemiological models using τ-equivalences

Abstract

Mathematical modeling plays a crucial role in understanding and controlling infectious diseases. Traditional SIR models, including both demographic (incorporating births and deaths) and non-demographic variations, have been extensively used to develop control strategies. However, the transfer of control strategies derived from simpler non-demographic models to more realistic demographic models remains challenging. This research addresses this gap by exploring the concept of τ-equivalence, aiming to transfer control strategies derived from non-demographic SIR models to demographic ones. Employing numerical simulations and parameter optimization, we demonstrated that appropriately calibrated non-demographic models closely mirrored demographic model dynamics, particularly under moderate control intensities. Nevertheless, our approach faced limitations under scenarios requiring rapid and significant infection reduction, revealing potential stability challenges. Our findings highlight the practical value of the τ-equivalence calibration method, while acknowledging that its broader applicability across diverse epidemiological models warrants further investigation.