Policy making for public health under uncertainty
Understanding and handling uncertainty in agent-based models and other individual-level models (ILMs) more broadly is an ongoing challenge. The problem becomes particularly acute when ILMs are employed in a public health setting, where there are large uncertainties in both the input data as well as the underlying behavioural models. For models to produce reliable predictions that can be trusted, it is crucial that uncertainties are captured and communicated properly.
This special session will explore the challenges associated with using ILMs to simulate scenarios in public health systems, focussing on how to quantify, understand and/or reduce uncertainty in the modelled predictions. We seek papers that present important methodological or empirical approaches to solve the problems with uncertainty in ILMs for public health models, such as:
- Methodological approaches to quantify or reducing uncertainty in agent-based and individual-level model predictions;
- Empirical examples of the use of agent-based modelling in a public health setting that include uncertainty estimates