A statistical model of COVID-19 testing in populations: effects of sampling bias and testing errors

TL;DR

This paper presents a statistical model for COVID-19 testing that accounts for sampling biases and testing errors, enabling more accurate estimation of disease prevalence and fatality ratios from testing data.

Contribution

It introduces a comprehensive statistical framework that incorporates biases, multiple test types, and retesting, improving interpretation of COVID-19 testing results.

Findings

Estimated disease prevalence with uncertainty quantification

Adjusted fatality ratios accounting for testing errors

Demonstrated model application on real COVID-19 data

Abstract

We develop a statistical model for the testing of disease prevalence in a population. The model assumes a binary test result, positive or negative, but allows for biases in sample selection and both type I (false positive) and type II (false negative) testing errors. Our model also incorporates multiple test types and is able to distinguish between retesting and exclusion after testing. Our quantitative framework allows us to directly interpret testing results as a function of errors and biases. By applying our testing model to COVID-19 testing data and actual case data from specific jurisdictions, we are able to estimate and provide uncertainty quantification of indices that are crucial in a pandemic, such as disease prevalence and fatality ratios.