ArticleBiometrical journal. Biometrische Zeitschrift2021
A statistical model for the dynamics of COVID-19 infections and their case detection ratio in 2020.
Article in Biometrical journal. Biometrische Zeitschrift, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Estimating the effect of self-protection on transmission dynamics of SARS-CoV-2 in Germany in 2021: a modelling study.BMC public health · 2026Article
- Effect of vaccinations and school restrictions on the spread of COVID-19 in different age groups in Germany.Infectious Disease Modelling · 2024Article
- Automation, organizational ambidexterity and the stability of employee relations: new tensions arising between corporate entrepreneurship, innovation management and stakeholder management.The Journal of technology transfer · 2023Article
- Deaths during the first year of the COVID-19 pandemic: insights from regional patterns in Germany and Poland.BMC public health · 2023Article
- A novel method to monitor COVID-19 fatality rate in real-time, a key metric to guide public health policy.Scientific reports · 2022Article
- Advancing Precision Vaccinology by Molecular and Genomic Surveillance of Severe Acute Respiratory Syndrome Coronavirus 2 in Germany, 2021.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2022Article
- Estimating the course of the COVID-19 pandemic in Germany via spline-based hierarchical modelling of death counts.Scientific reports · 2022Article
- Estimating COVID-19-induced excess mortality in Lombardy, Italy.Aging clinical and experimental research · 2022Article
- A statistical model for the dynamics of COVID-19 infections and their case detection ratio in 2020.Biometrical journal. Biometrische Zeitschrift · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The case detection ratio of coronavirus disease 2019 (COVID-19) infections varies over time due to changing testing capacities, different testing strategies, and the evolving underlying number of infections itself. This note shows a way of quantifying these dynamics by jointly modeling the reported number of detected COVID-19 infections with nonfatal and fatal outcomes. The proposed methodology also allows to explore the temporal development of the actual number of infections, both detected and undetected, thereby shedding light on the infection dynamics. We exemplify our approach by analyzing German data from 2020, making only use of data available since the beginning of the pandemic. Our modeling approach can be used to quantify the effect of different testing strategies, visualize the dynamics in the case detection ratio over time, and obtain information about the underlying true infection numbers, thus enabling us to get a clearer picture of the course of the COVID-19 pandemic in 2020.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.