LASSO-ODE: A framework for mechanistic model identifiability and selection in disease transmission modeling

TL;DR

This paper introduces LASSO-ODE, a framework that combines penalized regression with mechanistic epidemic models to improve parameter identifiability and model selection from sparse data, aiding public health decision-making.

Contribution

The paper presents a novel integration of LASSO-based covariate selection with ODE models, enabling rapid development of interpretable, parsimonious, and identifiable epidemic models from limited data.

Findings

LASSO-ODE effectively selects parsimonious models from unidentifiable larger models.

The framework performs well with sparse data and limited observed compartments.

Cross-validation techniques for time series improve model validation.

Abstract

To be fully useful for public health practice, models for epidemic response must be able to do more than predict -- it is also important to incorporate the mechanisms underlying transmission dynamics to enable policymakers and practitioners to be able to evaluate what-if scenarios and intervention options. However, most mechanistic models suffer from uncertainty in both the parameters (e.g., parameter unidentifiability) and the model structure itself, which can hinder both successful parameter estimation and model interpretation. To enable rapid development of interpretable and parsimonious mechanistic models, we use penalized regression and covariate selection methods to integrate parameter identifiability and model selection directly into the parameter estimation procedure for (in this case) traditional ordinary differential equation (ODE) models. For both simulated and real-world epidemiological data, we demonstrate that the LASSO-ODE framework is highly effective in selecting a parsimonious, identifiable model from larger, more realistic but potentially unidentifiable models, from realistically sparse data with only a single measured compartment and multiple latent (unobserved) variables. While we focus on epidemic models in this paper as a case study, these same approaches are applicable to a wide range of application areas that are faced with relatively sparse data but a need for realistic mechanistic models (e.g. mathematical oncology and mathematical biology more broadly). Additionally, the cross-validation techniques designed for time series data introduced in our study can be used across a range of time series analysis and modeling approaches.