Nowcasting the COVID-19 Pandemic in Bavaria

Guenther, F.; Bender, A.; Katz, K.; Kuechenhoff, H.; Hoehle, M.

To assess the current dynamic of an epidemic it is central to collect information on the daily number of newly diseased cases. This is especially important in real-time surveillance, when one aims at evaluating the effects of interventions on disease spread. Reporting delays between disease onset and case reporting hamper our ability to understand the dynamic of an epidemic when looking at the number of daily reported cases only. Nowcasting can be used to adjust daily case counts for occurred-but-not-yet-reported events. Here, we present a novel application of nowcasting to data on the current COVID-19 pandemic in Bavaria. It is based on a hierarchical Bayesian model that considers changes in the reporting delay distribution associated with the week and weekday of reporting and assumes a smooth epidemic curve. Furthermore, we present a way to estimate the time-dependent case reproduction number R(t) based on predictions of the nowcast. We provide methodological details of the developed approach, illustrate results based on data of the current epidemic, discuss limitations and alternative estimation strategies, and provide code for reproduction or adaption of the nowcasting to data from different regions. Results of the nowcasting approach are reported to the Bavarian health authority and published on a webpage on a daily basis.