Literature DB >> 33215765

Nowcasting fatal COVID-19 infections on a regional level in Germany.

Marc Schneble1, Giacomo De Nicola1, Göran Kauermann1, Ursula Berger2.   

Abstract

We analyse the temporal and regional structure in mortality rates related to COVID-19 infections, making use of the openly available data on registered cases in Germany published by the Robert Koch Institute on a daily basis. Estimates for the number of present-day infections that will, at a later date, prove to be fatal are derived through a nowcasting model, which relates the day of death of each deceased patient to the corresponding day of registration of the infection. Our district-level modelling approach for fatal infections disentangles spatial variation into a global pattern for Germany, district-specific long-term effects and short-term dynamics, 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 analysis of death counts contributes to a better understanding of the spread of the disease while being, to some extent, less dependent on testing strategy and capacity in comparison to infection counts. The proposed approach and the presented results thus provide reliable insight into the state and the dynamics of the pandemic during the early phases of the infection wave in spring 2020 in Germany, when little was known about the disease and limited data were available.
© 2020 The Authors. Biometrical Journal published by Wiley-VCH GmbH.

Entities:  

Keywords:  COVID-19; disease mapping; generalized regression model; nowcasting

Year:  2020        PMID: 33215765     DOI: 10.1002/bimj.202000143

Source DB:  PubMed          Journal:  Biom J        ISSN: 0323-3847            Impact factor:   2.207


  6 in total

1.  Estimating the course of the COVID-19 pandemic in Germany via spline-based hierarchical modelling of death counts.

Authors:  Tobias Wistuba; Andreas Mayr; Christian Staerk
Journal:  Sci Rep       Date:  2022-06-13       Impact factor: 4.996

2.  Bayesian imputation of COVID-19 positive test counts for nowcasting under reporting lag.

Authors:  Radka Jersakova; James Lomax; James Hetherington; Brieuc Lehmann; George Nicholson; Mark Briers; Chris Holmes
Journal:  J R Stat Soc Ser C Appl Stat       Date:  2022-04-23       Impact factor: 1.680

3.  Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly COVID-19 cases in Germany.

Authors:  Cornelius Fritz; Emilio Dorigatti; David Rügamer
Journal:  Sci Rep       Date:  2022-03-10       Impact factor: 4.379

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

Authors:  Sharon K Greene; Sarah F McGough; Gretchen M Culp; Laura E Graf; Marc Lipsitch; Nicolas A Menzies; Rebecca Kahn
Journal:  JMIR Public Health Surveill       Date:  2021-01-15

5.  Combining rank-size and k-means for clustering countries over the COVID-19 new deaths per million.

Authors:  Roy Cerqueti; Valerio Ficcadenti
Journal:  Chaos Solitons Fractals       Date:  2022-03-11       Impact factor: 9.922

6.  Nowcasting the COVID-19 pandemic in Bavaria.

Authors:  Felix Günther; Andreas Bender; Katharina Katz; Helmut Küchenhoff; Michael Höhle
Journal:  Biom J       Date:  2020-12-01       Impact factor: 1.715

  6 in total

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