  The COVID-19 pandemic has highlighted delayed reporting as a significant
impediment to effective disease surveillance and decision-making. In the
absence of timely data, statistical models which account for delays can be
adopted to nowcast and forecast cases or deaths. We discuss the four key
sources of systematic and random variability in available data for COVID-19 and
other diseases, and critically evaluate current state-of-the-art methods with
respect to appropriately separating and capturing this variability. We present
a general spatio-temporal hierarchical framework for correcting delayed
reporting and demonstrate its application to daily English hospital deaths from
COVID-19 and Severe Acute Respiratory Infection cases in Brazil. We compare our
approach to competing models with respect to theoretical flexibility and
quantitative metrics from a rolling nowcasting experiment imitating a realistic
operational scenario. Based on consistent and compelling leads in nowcasting
accuracy, bias, and precision, we demonstrate that our approach represents the
current best-practice for correcting delayed reporting.
