A statistical model for the dynamics of COVID‐19 infections and their case detection ratio in 2020

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

doi:10.1002/bimj.202100125

Cited by 18 publications

(16 citation statements)

References 40 publications

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“…While the classical mixed modelling approach provides age group specific estimates of relative changes in the CDR, our proposed Bayesian hierarchical modelling approach also yields estimates of absolute numbers of dark figures of infections as well as estimates of the effective reproduction number, by considering age-specific IFR estimates and dates of reported deaths. Our numerical results regarding trends in dark figures of infections generally support the results of Schneble et al [27]: dark figures in Germany are estimated to be largest at the beginning of the pandemic and, after a period of relatively low estimated dark figures (i.e. large CDR) during summer 2020, numbers of undetected cases are estimated to increase sharply in September 2020.…”

Section: Discussionsupporting

confidence: 88%

“…Our study is also related to the recent work of Schneble et al [27], which estimates relative changes in the case detection ratio (CDR) over time for different age groups. The authors employ a smooth generalized linear mixed model for confirmed cases and variables indicating whether the infections resulted in COVID-19 related deaths (without modelling the actual dates of deaths).…”

Section: Discussionmentioning

confidence: 73%

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Estimating the Course of the COVID-19 Pandemic in Germany via Spline-based Hierarchical Modelling of Death Counts

Wistuba

Mayr

Staerk

2021

Preprint

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The effective reproduction number is a key figure in context of the COVID-19 pandemic, which is typically estimated based on daily confirmed cases. Here, we consider a retrospective modelling approach for estimating effective reproduction numbers based on death counts during the first year of the pandemic in Germany. The proposed Bayesian hierarchical model incorporates splines to estimate reproduction numbers flexibly over time while adjusting for varying effective infection fatality rates. The approach also provides estimates of dark figures regarding undetected infections over time. Results for Germany illustrate that estimates based on death counts are often similar to classical estimates based on confirmed cases. However, considering death counts is more robust against shifts in testing policies: in particular, confirmed cases indicate a spike in the effective reproduction number linked to a local super-spreading event in June 2020, whereas our model does not estimate a spike but reduced dark figures of infections. During the second wave of infections, classical estimates suggest a flattening trend of infections following the "lockdown light" in November 2020, while our results indicate that infections continued to rise until the "second lockdown" in December 2020. This observation is associated with more stringent testing criteria introduced concurrently with the "lockdown light", which is reflected in subsequently increasing dark figures of infections estimated by our model. In light of progressive vaccinations, shifting the focus from modelling confirmed cases to reported deaths with the possibility to incorporate effective infection fatality rates might be of increasing relevance for the future surveillance of the pandemic.

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Section: Discussionsupporting

confidence: 88%

Section: Discussionmentioning

confidence: 73%

Estimating the Course of the COVID-19 Pandemic in Germany via Spline-based Hierarchical Modelling of Death Counts

Wistuba

Mayr

Staerk

2021

Preprint

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“… 2021 ; Schneble et al. 2021 ). Nonetheless, the raw number of COVID-related fatalities can also be subject to interpretative issues and biases due to underreporting and misclassification.…”

Section: Introductionmentioning

confidence: 99%

On assessing excess mortality in Germany during the COVID-19 pandemic

Kauermann

Höhle

2022

AStA Wirtsch Sozialstat Arch

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Coronavirus disease 2019 (COVID-19) is associated with a very high number of casualties in the general population. Assessing the exact magnitude of this number is a non-trivial problem, as relying only on officially reported COVID-19 associated fatalities runs the risk of incurring in several kinds of biases. One of the ways to approach the issue is to compare overall mortality during the pandemic with expected mortality computed using the observed mortality figures of previous years. In this paper, we build on existing methodology and propose two ways to compute expected as well as excess mortality, namely at the weekly and at the yearly level. Particular focus is put on the role of age, which plays a central part in both COVID-19-associated and overall mortality. We illustrate our methods by making use of age-stratified mortality data from the years 2016 to 2020 in Germany to compute age group-specific excess mortality during the COVID-19 pandemic in 2020.

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“…Incidence indicators measure the number of individuals with a particular condition, related with the epidemic, recorded during a given period. They can be referred to different time periods; in particular, in the Italian Civil Protection Department dataset ( https://github.com/pcm-dpc/COVID-19 ), daily incidence counts are available for the following indicators positives, which are classified into hospitalized (either in regular wards or in ICU) isolated-at-home tested deceased recovered/discharged The number of positives available from official reporting, however, underestimates the true number of cases since there exists a vast proportion of asymptomatic or mildly symptomatic patients, among all infected individuals, who are not detected [ 1 – 4 ]. Complex methods are then required to provide reasonable forecasts of the epidemic evolution [ 5 – 8 ] and avoid unreliable predictions, which make people worry (on this point, see e.g.…”

Section: Introductionmentioning

confidence: 99%

Estimating COVID-19-induced excess mortality in Lombardy, Italy

et al. 2022

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We compare the expected all-cause mortality with the observed one for different age classes during the pandemic in Lombardy, which was the epicenter of the epidemic in Italy. The first case in Italy was found in Lombardy in early 2020, and the first wave was mainly centered in Lombardy. The other three waves, in Autumn 2020, March 2021 and Summer 2021 are also characterized by a high number of cases in absolute terms. A generalized linear mixed model is introduced to model weekly mortality from 2011 to 2019, taking into account seasonal patterns and year-specific trends. Based on the 2019 year-specific conditional best linear unbiased predictions, a significant excess of mortality is estimated in 2020, leading to approximately 35000 more deaths than expected, mainly arising during the first wave. In 2021, instead, the excess mortality is not significantly different from zero, for the 85+ and 15–64 age classes, and significant reductions with respect to the 2020 estimated excess mortality are estimated for other age classes. Supplementary Information The online version contains supplementary material available at 10.1007/s40520-021-02060-1.

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A statistical model for the dynamics of COVID‐19 infections and their case detection ratio in 2020

Cited by 18 publications

References 40 publications

Estimating the Course of the COVID-19 Pandemic in Germany via Spline-based Hierarchical Modelling of Death Counts

Estimating the Course of the COVID-19 Pandemic in Germany via Spline-based Hierarchical Modelling of Death Counts

On assessing excess mortality in Germany during the COVID-19 pandemic

Estimating COVID-19-induced excess mortality in Lombardy, Italy

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