On the simultaneous inference of susceptibility distributions and intervention effects from epidemic curves

TL;DR

This paper evaluates the ability of SEIR models with individual susceptibility variation to accurately infer epidemic parameters, highlighting challenges and solutions for their use in policy-making.

Contribution

It provides the first systematic validation of susceptibility and intervention effect inference in SEIR models through simulation and maximum likelihood estimation.

Findings

Identified identifiability issues in parameter estimation.

Shared parameters across multiple epidemics improve inference accuracy.

Demonstrated potential for these models to inform policy with proper validation.

Abstract

Susceptible-Exposed-Infectious-Recovered (SEIR) models with inter-individual variation in susceptibility or exposure to infection were proposed early in the COVID-19 pandemic as a potential element of the mathematical/statistical toolset available to policy development. In comparison with other models employed at the time, those designed to fully estimate the effects of such variation tended to predict small epidemic waves and hence require less containment to achieve the same outcomes. However, these models never made it to mainstream COVID-19 policy making due to lack of prior validation of their inference capabilities. Here we report the results of the first systematic investigation of this matter. We simulate datasets using the model with strategically chosen parameter values, and then conduct maximum likelihood estimation to assess how well we can retrieve the assumed parameter values. We identify some identifiability issues which can be overcome by creatively fitting multiple epidemics with shared parameters.