Quantifying Uncertainty in Infectious Disease Mechanistic Models

TL;DR

This paper discusses methods to quantify statistical uncertainty in infectious disease mechanistic models, focusing on data, stochastic, and structural uncertainties, with practical R code and a SARS-CoV-2 case study.

Contribution

It introduces a comprehensive framework for quantifying and analyzing different sources of uncertainty in infectious disease models, supported by R code and a real-world case study.

Findings

Quantified data, stochastic, and structural uncertainties in models.

Applied uncertainty quantification to estimate SARS-CoV-2 R0.

Provided practical R tools for uncertainty analysis.

Abstract

This primer describes the statistical uncertainty in mechanistic models and provides R code to quantify it. We begin with an overview of mechanistic models for infectious disease, and then describe the sources of statistical uncertainty in the context of a case study on SARS-CoV-2. We describe the statistical uncertainty as belonging to three categories: data uncertainty, stochastic uncertainty, and structural uncertainty. We demonstrate how to account for each of these via statistical uncertainty measures and sensitivity analyses broadly, as well as in a specific case study on estimating the basic reproductive number, $R_0$, for SARS-CoV-2.