With the ever-growing emphasis on the importance of sound evidence in healthcare decision-making and policy, the power of data-informed mathematical models to provide much needed insight is substantial. In
order for conclusions drawn from a mathematical model to be reliable, it is essential for unknown model parameters to be estimated from data in a statistically sound manner and to fully account for uncertainty in the parameter values. Interest has been growing, both domestically and internationally, in developing new methods for parameter estimation in mathematical models of infectious diseases.
The dynamics of how an infectious disease spreads through a population is often modelled with sets of nonlinear ordinary differential equations or stochastic simulation. These dynamic models contain parameters such as rate constants and initial populations, however, these parameters often cannot be measured directly, or there is inherent uncertainty in the parameter values. As such, these parameter values need to be estimated using statistical techniques such as maximum likelihood estimation and Bayesian inference. In the last decade much research has focused on estimating the unknown parameters of dynamic models under a Bayesian framework.
This workshop addresses the need to use existing statistical methods and to develop new methods for parameter estimation in models of infectious diseases.