Architectures of epidemic models: accommodating constraints from empirical and clinical data

TL;DR

This paper explores how the structure of deterministic epidemic models affects their ability to fit empirical and clinical data, emphasizing the importance of model architecture in capturing complex disease dynamics.

Contribution

It analyzes the impact of model architecture constraints on fitting epidemic data, highlighting the need for flexible structures to accommodate heterogeneity and variable transition times.

Findings

Model structure imposes constraints on data fitting.

Heterogeneous groups improve model flexibility.

Variable transition times affect model accuracy.

Abstract

Deterministic compartmental models have been used extensively in modeling epidemic propagation. These models are required to fit available data and numerical procedures are often implemented to this end. But not every model architecture is able to fit the data because the structure of the model imposes hard constraints on the solutions. We investigate in this work two such situations: first the distribution of transition times from a compartment to another may impose a variable number of intermediary states; secondly, a non-linear relationship between time-dependent measures of compartments sizes may indicate the need for structurations (i.e., considering several groups of individuals of heterogeneous characteristics).