  One of the central difficulties of addressing the COVID-19 pandemic has been
accurately measuring and predicting the spread of infections. In particular,
official COVID-19 case counts in the United States are under counts of actual
caseloads due to the absence of universal testing policies. Researchers have
proposed a variety of methods for recovering true caseloads, often through the
estimation of statistical models on more reliable measures, such as death and
hospitalization counts, positivity rates, and demographics. However, given the
disproportionate impact of COVID-19 on marginalized racial, ethnic, and
socioeconomic groups, it is important to consider potential unintended effects
of case correction methods on these groups. Thus, we investigate two of these
correction methods for their impact on a downstream COVID-19 case prediction
task. For that purpose, we tailor an auditing approach and evaluation protocol
to analyze the fairness of the COVID-19 prediction task by measuring the
difference in model performance between majority-White counties and
majority-minority counties. We find that one of the correction methods improves
fairness, decreasing differences in performance between majority-White and
majority-minority counties, while the other method increases differences,
introducing bias. While these results are mixed, it is evident that correction
methods have the potential to exacerbate existing biases in COVID-19 case data
and in downstream prediction tasks. Researchers planning to develop or use case
correction methods must be careful to consider negative effects on marginalized
groups.
