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As COVID-19 spread through the United States in 2020, states began to set upalert systems to inform policy decisions and serve as risk communication toolsfor the general public. Many of these systems, like in Ohio, includedindicators based on an assessment of trends in reported cases. However, whencases are indexed by date of disease onset, reporting delays complicate theinterpretation of trends. Despite a foundation of statistical literature toaddress this problem, these methods have not been widely applied in practice.In this paper, we develop a Bayesian spatio-temporal nowcasting model forassessing trends in county-level COVID-19 cases in Ohio. We compare theperformance of our model to the current approach used in Ohio and the approachthat was recommended by the Centers for Disease Control and Prevention. Wedemonstrate gains in performance while still retaining interpretability usingour model. In addition, we are able to fully account for uncertainty in boththe time series of cases and in the reporting process. While we cannoteliminate all of the uncertainty in public health surveillance and subsequentdecision-making, we must use approaches that embrace these challenges anddeliver more accurate and honest assessments to policymakers.

A Bayesian spatio-temporal nowcasting model for public health decision-making and surveillance