Borgmann Stefan, Meintrup David, Reimer Kerstin, Schels Helmut, Nowak-Machen Martina
Department of Infectious Diseases and Infection Control, Ingolstadt Hospital, 85049 Ingolstadt, Germany.
Faculty of Engineering and Management, Technische Hochschule Ingolstadt, 85049 Ingolstadt, Germany.
Healthcare (Basel). 2021 Mar 17;9(3):338. doi: 10.3390/healthcare9030338.
SARS-CoV-2 has caused a deadly pandemic worldwide, placing a burden on local health care systems and economies. Infection rates with SARS-CoV-2 and the related mortality of COVID-19 are not equal among countries or even neighboring regions. Based on data from official German health authorities since the beginning of the pandemic, we developed a case-fatality prediction model that correctly predicts COVID-19-related death rates based on local geographical developments of infection rates in Germany, Bavaria, and a local community district city within Upper Bavaria. Our data point towards the proposal that local individual infection thresholds, when reached, could lead to increasing mortality. Restrictive measures to minimize the spread of the virus could be applied locally based on the risk of reaching the individual threshold. Being able to predict the necessity for increasing hospitalization of COVID-19 patients could help local health care authorities to prepare for increasing patient numbers.
严重急性呼吸综合征冠状病毒2(SARS-CoV-2)在全球范围内引发了致命的大流行,给当地医疗系统和经济带来了负担。SARS-CoV-2的感染率以及新型冠状病毒肺炎(COVID-19)的相关死亡率在各国甚至相邻地区之间并不相同。基于自疫情开始以来德国官方卫生当局的数据,我们开发了一种病死率预测模型,该模型可根据德国、巴伐利亚州以及上巴伐利亚州内一个地方社区城市的感染率的局部地理发展情况,正确预测与COVID-19相关的死亡率。我们的数据表明这样一种观点,即当地个体感染阈值一旦达到,可能会导致死亡率上升。基于达到个体阈值的风险,可在当地实施限制性措施以尽量减少病毒传播。能够预测增加COVID-19患者住院治疗的必要性,有助于当地医疗当局为增加的患者数量做好准备。