Speakers
Description
Epidemiological models are often used to address real-world policy and intervention questions for which it is challenging to collect large-scale and precise data. As a result, such models frequently involve the use of noisy or sparse data. Traditional modeling and parameter estimation methods used in the wrong context can lead to erroneous conclusions about the impact of interventions, or our understanding of the underlying mechanisms. For example, parameter estimation is often used as a first step before simulating different control scenarios, or performing sensitivity analyses to identify effective targets for public health interventions; if those initial estimates are inaccurate, the subsequent analyses are likely also invalid. In order to mitigate this challenge and appropriately incorporate this noisy or sparse data often encountered in epidemiology, it is key to consider model identifiability and parameter uncertainty as well as work with innovative modeling types. This mini-symposium brings together researchers working on model identifiability, parameter uncertainty and novel modeling structures (e.g., integral projection models) that integrate empirical data, with applications to infectious disease dynamics. It will showcase cutting edge methodologies to assess these factors as well as the applicability of such models to biological questions involving the evaluation and control of infectious disease outbreaks.
