  As COVID-19 spread through the United States in 2020, states began to set up
alert systems to inform policy decisions and serve as risk communication tools
for the general public. Many of these systems, like in Ohio, included
indicators based on an assessment of trends in reported cases. However, when
cases are indexed by date of disease onset, reporting delays complicate the
interpretation of trends. Despite a foundation of statistical literature to
address this problem, these methods have not been widely applied in practice.
In this paper, we develop a Bayesian spatio-temporal nowcasting model for
assessing trends in county-level COVID-19 cases in Ohio. We compare the
performance of our model to the current approach used in Ohio and the approach
that was recommended by the Centers for Disease Control and Prevention. We
demonstrate gains in performance while still retaining interpretability using
our model. In addition, we are able to fully account for uncertainty in both
the time series of cases and in the reporting process. While we cannot
eliminate all of the uncertainty in public health surveillance and subsequent
decision-making, we must use approaches that embrace these challenges and
deliver more accurate and honest assessments to policymakers.
