Correcting delayed reporting of COVID-19 using the generalized-Dirichlet-multinomial method.

ABSTRACT: 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 propose a general hierarchical approach to correcting delayed reporting of COVID-19 and apply this to daily English hospital deaths, resulting in a flexible prediction tool which could be used to better inform pandemic decision-making. We compare this approach to competing mod

SUBMITTER: Stoner O 

PROVIDER: S-EPMC9877609 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature