当前位置: X-MOL 学术Biom. J. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
A statistical model for the dynamics of COVID-19 infections and their case detection ratio in 2020
Biometrical Journal ( IF 1.7 ) Pub Date : 2021-08-10 , DOI: 10.1002/bimj.202100125
Marc Schneble 1 , Giacomo De Nicola 1 , Göran Kauermann 1 , Ursula Berger 2
Affiliation  

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.

中文翻译:

2020 年 COVID-19 感染动态及其病例检出率的统计模型

由于不断变化的检测能力、不同的检测策略以及不断变化的潜在感染数量本身,2019 年冠状病毒病 (COVID-19) 感染的病例检出率随时间而变化。本说明显示了一种量化这些动态的方法,方法是联合模拟报告的检测到的具有非致命和致命结果的 COVID-19 感染的数量。所提出的方法还允许探索实际感染数量的时间发展,包括检测到和未检测到的感染数量,从而揭示感染动态。我们通过分析 2020 年的德国数据来举例说明我们的方法,仅使用自大流行开始以来可用的数据。我们的建模方法可用于量化不同测试策略的效果,可视化病例检测率随时间变化的动态,
更新日期:2021-08-10
down
wechat
bug