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

Schneble, Marc; Nicola, Giacomo de; Kauermann, Göran und Berger, Ursula (2021): A statistical model for the dynamics of COVID-19 infections and their case detection ratio in 2020. In: Biometrical Journal, Bd. 63, Nr. 8: S. 1623-1632

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.

• Dokumententyp: Zeitschriftenartikel
• Fakultät: Medizin > Institut für Medizinische Informationsverarbeitung, Biometrie und Epidemiologie
• Themengebiete: 600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin und Gesundheit
• Sprache: Englisch