On Parameter Identifiability in Network-Based Epidemic Models

Abstract

Modelling epidemics on networks represents an important departure from classical compartmental models which assume random mixing. However, the resulting models are high-dimensional and their analysis is often out of reach. It turns out that mean-field models, low-dimensional systems of differential equations, whose variables are carefully chosen expected quantities from the exact model provide a good approximation and incorporate explicitly some network properties. Despite the emergence of such mean-field models, there has been limited work on investigating whether these can be used for inference purposes. In this paper, we consider network-based mean-field models and explore the problem of parameter identifiability when observations about an epidemic are available. Making use of the analytical tractability of most network-based mean-field models, e.g. explicit analytical expressions for leading eigenvalue and final epidemic size, we set up the parameter identifiability problem as finding the solution or solutions of a system of coupled equations. More precisely, subject to observing/measuring growth rate and final epidemic size, we seek to identify parameter values leading to these measurements. We are particularly concerned with disentangling transmission rate from the network density. To do this, we give a condition for practical identifiability and we find that except for the simplest model, parameters cannot be uniquely determined, that is, they are practically unidentifiable. This means that there exist multiple solutions (a manifold of infinite measure) which give rise to model output that is close to the data. Identifying, formalising and analytically describing this problem should lead to a better appreciation of the complexity involved in fitting models with many parameters to data.

Convergence, Sampling and Total Order Estimator Effects on Parameter Orthogonality in Global Sensitivity Analysis

“…Practical identifiability accounts for the role of noise and sampling frequency inter alia in hindering the ability uniquely to estimate inputs. These issues notwithstanding, the study of unique parameter estimation is very important to the complex models increasingly used in life sciences, which encompass pharmacology, epidemiology and cardiovascular applications [2, 3, 4]. Assuming one can identify inputs representative of the data, we arrive at model personalisation - a process of effectively calibrating a life science model using data available from an individual subject or patient.…”