Accurately summarizing an outbreak using epidemiological models takes time

TL;DR

This paper examines the practical identifiability of key epidemiological statistics in outbreak models, revealing that some parameters are poorly estimated early on, especially in slow or less severe outbreaks.

Contribution

It introduces a new measure for assessing the practical identifiability of epidemiological statistics and evaluates its implications across different outbreak scenarios.

Findings

Basic reproductive number often poorly identified

Peak intensity and timing are better identified early

Identifiability issues are worse in slow or mild outbreaks

Abstract

Recent outbreaks of monkeypox and Ebola, and worrying waves of COVID-19, influenza and respiratory syncytial virus, have all led to a sharp increase in the use of epidemiological models to estimate key epidemiological parameters. The feasibility of this estimation task is known as the practical identifiability (PI) problem. Here, we investigate the PI of eight commonly reported statistics of the classic Susceptible-Infectious-Recovered model using a new measure that shows how much a researcher can expect to learn in a model-based Bayesian analysis of prevalence data. Our findings show that the basic reproductive number and final outbreak size are often poorly identified, with learning exceeding that of individual model parameters only in the early stages of an outbreak. The peak intensity, peak timing, and initial growth rate are better identified, being in expectation over 20 times more probable having seen the data by the time the underlying outbreak peaks. We then test PI for a variety of true parameter combinations, and find that PI is especially problematic in slow-growing or less-severe outbreaks. These results add to the growing body of literature questioning the reliability of inferences from epidemiological models when limited data are available.