Author: Deva, Ayush; Shingi, Siddhant; Tiwari, Avtansh; Bannur, Nayana; Jain, Sansiddh; White, Jerome; Raval, Alpan; Merugu, Srujana
Title: Interpretability of Epidemiological Models : The Curse of Non-Identifiability Cord-id: tcg2ezjx Document date: 2021_4_30
ID: tcg2ezjx
Snippet: 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
Document: 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.
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