Institute for Hygiene and Public Health, University Hospital Bonn, D-53127 Bonn, Germany.
Department of Geography, University of Bonn, D-53115 Bonn, Germany.
Int J Environ Res Public Health. 2023 Jan 18;20(3):1809. doi: 10.3390/ijerph20031809.
Nosocomial outbreaks require quick epidemiological clarification of possible chains of infection, since the pathogen usually has a head start that has to be caught up. Identification of people and areas at risk is crucial for efficient confinement. This paper describes a concept which can be applied to healthcare settings. The application skips the time-consuming and imperfect reconstruction of direct and indirect contacts. Indoor mobility of people and devices are instead measured precisely, and the mobility history is used to construct a spatio-temporal 'landscape of infection'. This landscape allows for the calculation of a modelled 'contamination landscape' (CL) adding location-based prolongation of infectivity. In that way, the risk per person can be derived in case of an outbreak. The CL concept is extremely flexible and can be adapted to various pathogen-specific settings. The combination of advanced measurements and specific modelling results in an instant list of possible recipients who need to be examined directly. The modelled, pathogen-specific parameters can be adjusted to get as close as possible to the results of mass screenings.
医院感染暴发需要快速进行流行病学调查，以明确可能的传播链，因为病原体通常具有先发优势，必须加以控制。确定高危人群和区域对于有效的隔离至关重要。本文描述了一种可应用于医疗保健环境的概念。该应用程序跳过了直接和间接接触的耗时且不完美的重建。取而代之的是，精确测量人员和设备的室内移动情况，并使用移动历史构建时空“感染景观”。该景观允许计算建模的“污染景观”(CL)，并添加基于位置的传染性延长。这样，在发生暴发时可以计算出每个人的风险。CL 概念非常灵活，可以适应各种特定病原体的设置。先进的测量和特定的建模结果相结合，可以立即生成可能需要直接检查的受检者列表。可以调整特定病原体的建模参数，以尽可能接近大规模筛查的结果。
