  In this work we: (1) review likelihood-based inference for parameter
estimation and the construction of confidence regions; and, (2) explore the use
of techniques from information geometry, including geodesic curves and Riemann
scalar curvature, to supplement typical techniques for uncertainty
quantification such as Bayesian methods, profile likelihood, asymptotic
analysis and bootstrapping. These techniques from information geometry provide
data-independent insights into uncertainty and identifiability, and can be used
to inform data collection decisions. All code used in this work to implement
the inference and information geometry techniques is available on GitHub.

Methodology;Applications