  The real-time analysis of infectious disease surveillance data, e.g., in the
form of a time-series of reported cases or fatalities, is essential in
obtaining situational awareness about the current dynamics of an adverse health
event such as the COVID-19 pandemic. This real-time analysis is complicated by
reporting delays that lead to underreporting of the number of events for the
most recent time points (e.g., days or weeks). This can lead to misconceptions
by the interpreter, e.g., the media or the public, as was the case with the
time-series of reported fatalities during the COVID-19 pandemic in Sweden.
Nowcasting methods provide real-time estimates of the complete number of events
using the incomplete time-series of currently reported events by using
information about the reporting delays from the past. Here, we consider
nowcasting the number of COVID-19-related fatalities in Sweden. We propose a
flexible Bayesian approach, extending existing nowcasting methods by
incorporating regression components to accommodate additional information
provided by leading indicators such as time-series of the number of reported
cases and ICU admissions. By a retrospective evaluation, we show that the
inclusion of ICU admissions as a leading signal improved the nowcasting
performance of case fatalities for COVID-19 in Sweden compared to existing
methods.
