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Modern data and computational resources, coupled with algorithmic and theoretical advances to exploit these, allow disease dynamic models to be parameterised with increasing detail and accuracy. While this enhances models' usefulness in prediction and policy, major challenges remain. In particular, lack of identifiability of a model's parameters may limit the usefulness of the model. While lack of parameter identifiability may be resolved through incorporation into an inference procedure of prior knowledge, formulating such knowledge is often difficult. Furthermore, there are practical challenges associated with acquiring data of sufficient quantity and quality. Here, we discuss recent progress on these issues.

Original publication

DOI

10.1016/j.epidem.2020.100393

Type

Journal article

Journal

Epidemics

Publication Date

17/05/2020

Volume

32

Addresses

School of Mathematics, Faculty of Science, Monash University, Melbourne, Australia.