Structural Identifiability and Observability of Compartmental Models of the COVID-19 Pandemic

TL;DR

This paper evaluates the structural identifiability and observability of 36 compartmental models for COVID-19, analyzing 255 variants to guide model selection and improve reliability of epidemic predictions.

Contribution

It systematically assesses the identifiability and observability of COVID-19 models under various assumptions, providing practical guidelines for model choice.

Findings

Many models lack full observability and identifiability.

Time-varying parameters affect model analysis.

Remedies can improve model observability.

Abstract

The recent coronavirus disease (COVID-19) outbreak has dramatically increased the public awareness and appreciation of the utility of dynamic models. At the same time, the dissemination of contradictory model predictions has highlighted their limitations. If some parameters and/or state variables of a model cannot be determined from output measurements, its ability to yield correct insights -- as well as the possibility of controlling the system -- may be compromised. Epidemic dynamics are commonly analysed using compartmental models, and many variations of such models have been used for analysing and predicting the evolution of the COVID-19 pandemic. In this paper we survey the different models proposed in the literature, assembling a list of 36 model structures and assessing their ability to provide reliable information. We address the problem using the control theoretic concepts of structural identifiability and observability. Since some parameters can vary during the course of an epidemic, we consider both the constant and time-varying parameter assumptions. We analyse the structural identifiability and observability of all of the models, considering all plausible choices of outputs and time-varying parameters, which leads us to analyse 255 different model versions. We classify the models according to their structural identifiability and observability under the different assumptions and discuss the implications of the results. We also illustrate with an example several alternative ways of remedying the lack of observability of a model. Our analyses provide guidelines for choosing the most informative model for each purpose, taking into account the available knowledge and measurements.