Nowcasting fatal COVID‐19 infections on a regional level in Germany

Schneble, Marc; Nicola, Giacomo De; Kauermann, Göran; Berger, Ursula

doi:10.1002/bimj.202000143

Cited by 32 publications

(28 citation statements)

References 27 publications

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“…In the following, we will refer to this delay adjustment approach as the nowcast and define the reporting delay as the time between disease onset and official case reporting by a health authority. Other authors use the term nowcasting for models that focus on adjusting the administrative delay between the first case report to a local health authority and registration (in aggregated data) at higher (e.g., state and/or federal) authorities (De Nicola, Schneble, Kauermann, & Berger, 2020), or to perform nowcasting of fatal cases between case registration and fatality date (Schneble, De Nicola, Kauermann, & Berger, 2020).…”

Section: Introductionmentioning

confidence: 99%

Nowcasting the COVID‐19 pandemic in Bavaria

Günther

Bender

Katz³

et al. 2020

Biometrical J

124

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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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Section: Introductionmentioning

confidence: 99%

Nowcasting the COVID‐19 pandemic in Bavaria

Günther

Bender

Katz³

et al. 2020

Biometrical J

124

View full text Add to dashboard Cite

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“…Their model provides better estimates than seven days' averages. Schneble et al 20 present a nowcasting model based on the number of deaths, as quantifying their correct number is more reliable than for infected people. Their epidemic spread model considers region and age-specific Poisson distributions, where they consider lag to report.…”

Section: Bayesian Approachesmentioning

confidence: 99%

Improving the estimation of the COVID-19 effective reproduction number using nowcasting

Salas

2021

Stat Methods Med Res

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As the interactions between people increases, the impending menace of COVID-19 outbreaks materializes, and there is an inclination to apply lockdowns. In this context, it is essential to have easy-to-use indicators for people to employ as a reference. The effective reproduction number of confirmed positives, Rt, fulfills such a role. This document proposes a data-driven approach to nowcast Rt based on previous observations’ statistical behavior. As more information arrives, the method naturally becomes more precise about the final count of confirmed positives. Our method’s strength is that it is based on the self-reported onset of symptoms, in contrast to other methods that use the daily report’s count to infer this quantity. We show that our approach may be the foundation for determining useful epidemy tracking indicators.

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“…The terms nowcasting , or predicting the present, and hindcasting , or predicting through the day prior to the present, describe a wide range of statistical adjustments used to fill in cases that are not yet reported, offering health officials a more up-to-date picture for situational awareness [ 10 ]. For example, researchers have assessed the potential to nowcast COVID-19 cases and deaths using Google Trends data available in near-real time [ 11 ], and have applied a range of modeling approaches that leverage reporting delays to estimate the number of not-yet-reported cases and deaths [ 12 , 13 ]. Using mathematical models to exploit COVID-19 transmission dynamics, nowcasting also has been extended to COVID-19 forecasting systems [ 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Nowcasting for Real-Time COVID-19 Tracking in New York City: An Evaluation Using Reportable Disease Data From Early in the Pandemic

Greene¹,

McGough²,

Culp³

et al. 2021

JMIR Public Health Surveill

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Background Nowcasting approaches enhance the utility of reportable disease data for trend monitoring by correcting for delays, but implementation details affect accuracy. Objective To support real-time COVID-19 situational awareness, the New York City Department of Health and Mental Hygiene used nowcasting to account for testing and reporting delays. We conducted an evaluation to determine which implementation details would yield the most accurate estimated case counts. Methods A time-correlated Bayesian approach called Nowcasting by Bayesian Smoothing (NobBS) was applied in real time to line lists of reportable disease surveillance data, accounting for the delay from diagnosis to reporting and the shape of the epidemic curve. We retrospectively evaluated nowcasting performance for confirmed case counts among residents diagnosed during the period from March to May 2020, a period when the median reporting delay was 2 days. Results Nowcasts with a 2-week moving window and a negative binomial distribution had lower mean absolute error, lower relative root mean square error, and higher 95% prediction interval coverage than nowcasts conducted with a 3-week moving window or with a Poisson distribution. Nowcasts conducted toward the end of the week outperformed nowcasts performed earlier in the week, given fewer patients diagnosed on weekends and lack of day-of-week adjustments. When estimating case counts for weekdays only, metrics were similar across days when the nowcasts were conducted, with Mondays having the lowest mean absolute error of 183 cases in the context of an average daily weekday case count of 2914. Conclusions Nowcasting using NobBS can effectively support COVID-19 trend monitoring. Accounting for overdispersion, shortening the moving window, and suppressing diagnoses on weekends—when fewer patients submitted specimens for testing—improved the accuracy of estimated case counts. Nowcasting ensured that recent decreases in observed case counts were not overinterpreted as true declines and supported officials in anticipating the magnitude and timing of hospitalizations and deaths and allocating resources geographically.

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Nowcasting fatal COVID‐19 infections on a regional level in Germany

Cited by 32 publications

References 27 publications

Nowcasting the COVID‐19 pandemic in Bavaria

Nowcasting the COVID‐19 pandemic in Bavaria

Improving the estimation of the COVID-19 effective reproduction number using nowcasting

Nowcasting for Real-Time COVID-19 Tracking in New York City: An Evaluation Using Reportable Disease Data From Early in the Pandemic

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