Scientific Publications Database

Article Title: A Bayesian model calibration framework for stochastic compartmental models with both time-varying and timeinvariant parameters
Authors: Robinson, Brandon; Bisaillon, Philippe; Edwards, Jodi D.; Kendzerska, Tetyana; Khalil, Mohammad; Poirel, Dominique; Sarkar, Abhijit
Journal: INFECTIOUS DISEASE MODELLING Volume 9 Issue 4
Date of Publication:2024
Abstract:
We consider state and parameter estimation for compartmental models having both timevarying and time-invariant parameters. In this manuscript, we first detail a general Bayesian computational framework as a continuation of our previous work. Subsequently, this framework is speci fically tailored to the susceptible-infectious-removed (SIR) model which describes a basic mechanism for the spread of infectious diseases through a system of coupled nonlinear differential equations. The SIR model consists of three states, namely, the susceptible, infectious, and removed compartments. The coupling among these states is controlled by two parameters, the infection rate and the recovery rate. The simplicity of the SIR model and similar compartmental models make them applicable to many classes of infectious diseases. However, the combined assumption of a deterministic model and time-invariance among the model parameters are two signi ficant impediments which critically limit their use for long-term predictions. The tendency of certain model parameters to vary in time due to seasonal trends, non-pharmaceutical interventions, and other random effects necessitates a model that structurally permits the incorporation of such time-varying effects. Complementary to this, is the need for a robust mechanism for the estimation of the parameters of the resulting model from data. To this end, we consider an augmented state vector, which appends the time-varying parameters to the original system states whereby the time evolution of the time-varying parameters are driven by an arti ficial noise process in a standard manner. Distinguishing between time-varying and time-invariant parameters in this fashion limits the introduction of arti ficial dynamics into the system, and provides a robust, fully Bayesian approach for estimating the timeinvariant system parameters as well as the elements of the process noise covariance matrix. This computational framework is implemented by leveraging the robustness of the Markov chain Monte Carlo algorithm permits the estimation of time-invariant parameters while nested nonlinear filters concurrently perform the joint estimation of the system.