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.
Keywords: contamination landscape; hospital acquired infections (HAI); infection chain; nosocomial outbreaks.