  COVID-19 forecasting models have been used to inform decision making around
resource allocation and intervention decisions e.g., hospital beds or
stay-at-home orders. State of the art deep learning models often use multimodal
data such as mobility or socio-demographic data to enhance COVID-19 case
prediction models. Nevertheless, related work has revealed under-reporting bias
in COVID-19 cases as well as sampling bias in mobility data for certain
minority racial and ethnic groups, which could in turn affect the fairness of
the COVID-19 predictions along race labels. In this paper, we show that state
of the art deep learning models output mean prediction errors that are
significantly different across racial and ethnic groups; and which could, in
turn, support unfair policy decisions. We also propose a novel de-biasing
method, DemOpts, to increase the fairness of deep learning based forecasting
models trained on potentially biased datasets. Our results show that DemOpts
can achieve better error parity that other state of the art de-biasing
approaches, thus effectively reducing the differences in the mean error
distributions across more racial and ethnic groups.
