Biometrical Journal has just published two Open Access articles about nowcasting in the context of Covid-19 in Germany. Both articles have lay abstracts, reproduced below.
The first paper entitled Nowcasting fatal COVID‐19 infections on a regional level in Germany by Schneble et al. is currently on Early View:
, , , . Nowcasting fatal COVID‐19 infections on a regional level in Germany. Biometrical Journal. 2020; 1– 19. https://doi.org/10.1002/bimj.202000143
Lay Abstract
The COVID-19 pandemic causes multiple infections, some of which end fatal. Overall, fatal outcomes are of particular interest, since not only they exhibit the cruel side of the disease but also, since numerous (if not most) fatal events occur after hospitalization, the number of fatalities mirrors the pressure on intensive care units in hospitals. Looking exclusively at fatalities provides a picture of the course of the disease which is not blurred by or mixed with testing capacities and testing strategies.
The paper by Schneble et al. (2020) pursues this approach and looks exclusively at infections with fatal outcomes. The paper relates the fatality date of each deceased patient to the corresponding day of registration of the infection, leading to a so called nowcasting model which allows us to estimate the number of present-day infections that will, at a later date, prove to be fatal. A key result of this research is that the survival time until death after registration as COVID-19 infected does not vary significantly among different age groups. The paper makes use of data from Germany and also proposes to break down the results to the district level in Germany while also taking the age and gender structure of the regional population into account. This enables to highlight areas with unexpectedly high disease activity.
The second paper is entitled Nowcasting the COVID-19 pandemic in Bavaria by
, , , , . Nowcasting the COVID‐19 pandemic in Bavaria. Biometrical Journal. 2020; 1– 13. https://doi.org/10.1002/bimj.202000112
Lay Abstract
Situational awareness of the course of the COVID–19 pandemic is essential for political decisions and public information. Currently, much of the public focus is concentrated on the daily reported numbers of newly registered cases. However, these numbers are not so informative about the true development of the disease spread because of time-varying reporting delays and a strong dependence on the day of the week (less cases reported on the weekend).
In this article, we developed and adapted the so-called nowcasting method for the estimation of daily newly diseased cases to the COVID-19 pandemic using statistical modelling, which allows for a clearer picture of how the pandemic is evolving. We use official data from the Bavarian health authorities to estimate the epidemic curve and the time varying reproduction number R for Bavaria and Munich. Continuous updates can be found at https://corona.stat.uni-muenchen.de/nowcast/ Our method allows the timely identification of new waves and as well the real-time assessment of interventions.