How many people are infected? A case study on SARS-CoV-2 prevalence in Austria

TL;DR

This paper estimates the true prevalence of SARS-CoV-2 in Austrian counties during December 2020 by addressing missing data and test inaccuracies using a partial identification framework, providing credible bounds on infection rates.

Contribution

It introduces a novel framework that jointly accounts for missing test results and test imperfections, improving prevalence estimates under different selection assumptions.

Findings

Stronger assumptions significantly reduce prevalence uncertainty.

Weak assumptions provide limited bounds on infection rates.

The framework offers credible bounds for SARS-CoV-2 prevalence in Austria.

Abstract

Using recent data from voluntary mass testing, I provide credible bounds on prevalence of SARS-CoV-2 for Austrian counties in early December 2020. When estimating prevalence, a natural missing data problem arises: no test results are generated for non-tested people. In addition, tests are not perfectly predictive for the underlying infection. This is particularly relevant for mass SARS-CoV-2 testing as these are conducted with rapid Antigen tests, which are known to be somewhat imprecise. Using insights from the literature on partial identification, I propose a framework addressing both issues at once. I use the framework to study differing selection assumptions for the Austrian data. Whereas weak monotone selection assumptions provide limited identification power, reasonably stronger assumptions reduce the uncertainty on prevalence significantly.