论文标题
有多少人被感染？奥地利SARS-COV-2患病率的案例研究
How many people are infected? A case study on SARS-CoV-2 prevalence in Austria
论文作者
论文摘要
使用自愿质量测试的最新数据，我在2020年12月上旬为奥地利县的SARS-COV-2流行提供了可靠的界限。当估计患病率时，出现了自然缺失的数据问题：未经测试的人没有产生测试结果。此外，测试并不能完全预测潜在的感染。这与质量SARS-COV-2测试尤其重要，因为这些测试是通过快速抗原测试进行的，该测试已知有些不精确。使用有关部分身份证明的文献的见解，我提出了一个框架，立即解决这两个问题。我使用该框架来研究奥地利数据的不同选择假设。尽管弱单调选择假设提供有限的识别能力，但相当强大的假设显着降低了患病率的不确定性。
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.
