The structural identifiability of the susceptible infected recovered model with seasonal forcing.

In this paper, it is shown that the SIR epidemic model, with the force of infection subject to seasonal variation, and a proportion of either the prevalence or the incidence measured, is unidentifiable unless certain key system parameters are known, or measurable. This means that an uncountable number of different parameter vectors can, theoretically, give rise to the same idealised output data. Any subsequent parameter estimation from real data must be viewed with little confidence as a result.

A mathematical model for the in vitro kinetics of the anti-cancer agent topotecan.

In this paper a compartmental modelling approach is applied to provide a mathematical description of the activity of the anti-cancer agent topotecan, and delivery to its nuclear DNA target following administration. The activity of topotecan in defined buffers is first modelled using a linear two compartment model that then forms the basis of a cell based model for drug activity in live cell experiments. An identifiability analysis is performed before parameter estimation to ensure that the model output (i.

Structural identifiability for a class of non-linear compartmental systems using linear/non-linear splitting and symbolic computation.

Under certain controllability and observability restrictions, two different parameterisations for a non-linear compartmental model can only have the same input-output behaviour if they differ by a locally diffeomorphic change of basis for the state space. With further restrictions, it is possible to gain valuable information with respect to identifiability via a linear analysis. Examples are presented where non-linear identifiability analyses are substantially simplified by means of an initial linear analysis.