Nowcasting with leading indicators applied to COVID-19 fatalities in Sweden

TL;DR

This paper introduces a Bayesian nowcasting method for COVID-19 fatalities in Sweden, incorporating leading indicators like case reports and ICU admissions to improve real-time estimates amid reporting delays.

Contribution

It extends existing nowcasting techniques by integrating regression components with leading indicators, enhancing accuracy in real-time fatality estimation.

Findings

Including ICU admissions improved nowcasting accuracy.

The method outperformed existing approaches in retrospective evaluations.

Leading indicators provided valuable real-time signals for better estimates.

Abstract

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.