# Interpretability of Epidemiological Models : The Curse of Non-Identifiability

arXiv: https://arxiv.org/abs/2104.14821 · HTML: https://mlsanity.com/paper/2104.14821/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 22 references that are in the map.
- First posted on arXiv 30 Apr 2021.

## Builds on (most cited first, 2 of 2)

- [On structural and practical identifiability](https://mlsanity.com/paper/2102.05100/) (2021)
- [Rank Conditions for Observability and Controllability for Time-varying Nonlinear Systems](https://mlsanity.com/paper/2003.09721/) (2020)

## Built on by (most cited first, 1 of 1)

- [Seven challenges for harmonizing explainability requirements](https://mlsanity.com/paper/2108.05390/) (2021)

## Abstract (arXiv)

Interpretability of epidemiological models is a key consideration, especially when these models are used in a public health setting. Interpretability is strongly linked to the identifiability of the underlying model parameters, i.e., the ability to estimate parameter values with high confidence given observations. In this paper, we define three separate notions of identifiability that explore the different roles played by the model definition, the loss function, the fitting methodology, and the quality and quantity of data. We define an epidemiological compartmental model framework in which we highlight these non-identifiability issues and their mitigation.