Nowcasting the COVID‐19 pandemic in Bavaria
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Abstract
To assess the current dynamics 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, where the aim is to gain situational awareness, for example, if cases are currently increasing or decreasing. Reporting delays between disease onset and case reporting hamper our ability to understand the dynamics of an epidemic close to now 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 over time and associated with the weekday of reporting. Furthermore, we present a way to estimate the effective time‐varying case reproduction number based on predictions of the nowcast. The approaches are based on previously published work, that we considerably extended and adapted to the current task of nowcasting COVID‐19 cases. We provide methodological details of the developed approach, illustrate results based on data of the current pandemic, and evaluate the model based on synthetic and retrospective data on COVID‐19 in Bavaria. Results of our nowcasting are reported to the Bavarian health authority and published on a webpage on a daily basis ( https://corona.stat.uni-muenchen.de/ ). Code and synthetic data for the analysis are available from https://github.com/FelixGuenther/nc_covid19_bavaria and can be used for adaption of our approach to different data.
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SciScore for 10.1101/2020.06.26.20140210: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:However, there are important limitations of any nowcasting estimation: (i) we correct for a bias due to delays between disease onset and case reporting, but provide no correction for possible cases in the population that were not tested. This is a big issue in understanding COVID–19 spread, since there are possibly many undetected cases. Assuming a constant factor of under-reporting we can analyze the dynamics of the outbreak in a more …
SciScore for 10.1101/2020.06.26.20140210: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:However, there are important limitations of any nowcasting estimation: (i) we correct for a bias due to delays between disease onset and case reporting, but provide no correction for possible cases in the population that were not tested. This is a big issue in understanding COVID–19 spread, since there are possibly many undetected cases. Assuming a constant factor of under-reporting we can analyze the dynamics of the outbreak in a more reliable way by our nowcasting method compared to focusing on daily counts of newly reported cases. Furthermore, R(t) estimates would be invariant to such constant under-reporting. However, if the proportion of undetected cases varies over time, then the dynamics of the epidemic is not described adequately by our approach as well. (ii) We model the temporal variation in the delay distribution in a flexible way. However, short term changes, especially in the time close to the current day can lead to a bias, because it is particularly hard to distinguish between developments in the epidemic curve and changes in the reporting delay with no or very little data. (iii) Our imputation method includes a missing at random assumption, which implies that the time between disease onset and reporting is the same for individuals with and without available symptom onset date. This could be violated due to many asymptomatic and pre-symptomatic among the reported COVID–19 cases. However, the sensitivity analyses in the appendix (5) show that our results are rel...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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