Flexible Bayesian Nowcasting with application to COVID-19 fatalities in Sweden

The real-time analysis of infectious disease surveillance data, e.g. time-series of reported cases or fatalities, can help to provide situational awareness about the current state of a pandemic. This task is challenged by reporting delays that give rise to occurred-but-not-yet-reported events. If these events are not taken into consideration, this can lead to an under-estimation of the counts-to-be-reported and, hence, introduces misconceptions by the interpreter, the media or the general public -- as has been seen for example for reported fatalities during the COVID-19 pandemic. Nowcasting methods provide close to 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. In this report, we consider nowcasting the number of COVID-19 related fatalities in Sweden. We propose a flexible Bayesian approach that considers temporal changes in the reporting delay distribution and, as an extension to existing nowcasting methods, incorporates a regression component for the (lagged) time-series of the number of ICU admissions. This results in a model considering both the past behavior of the time-series of fatalities as well as additional data streams that are in a time-lagged association with the number of fatalities.