Statistical techniques to estimate the SARS-CoV-2 infection fatality rate

TL;DR

This paper reviews and develops statistical methods for accurately estimating the SARS-CoV-2 infection fatality rate, addressing biases, combining studies, and providing practical guidelines for public health decision-making.

Contribution

It introduces new statistical techniques and correction methods for IFR estimation, along with best practice recommendations and code implementation.

Findings

Developed inverse problem approach for bias correction

Reviewed methods for combining multiple IFR estimates

Provided practical guidelines and code for accurate IFR estimation

Abstract

The determination of the infection fatality rate (IFR) for the novel SARS-CoV-2 coronavirus is a key aim for many of the field studies that are currently being undertaken in response to the pandemic. The IFR together with the basic reproduction number $R_0$, are the main epidemic parameters describing severity and transmissibility of the virus, respectively. The IFR can be also used as a basis for estimating and monitoring the number of infected individuals in a population, which may be subsequently used to inform policy decisions relating to public health interventions and lockdown strategies. The interpretation of IFR measurements requires the calculation of confidence intervals. We present a number of statistical methods that are relevant in this context and develop an inverse problem formulation to determine correction factors to mitigate time-dependent effects that can lead to biased IFR estimates. We also review a number of methods to combine IFR estimates from multiple independent studies, provide example calculations throughout this note and conclude with a summary and "best practice" recommendations. The developed code is available online.