Hospital-acquired infections are an immense problem. “During the COVID-19 pandemic, many infections occurred in hospitals. Not only did this put vulnerable patients in danger, but many facilities experienced major staffing problems because lots of their employees were ill at the same time,” says Onicio Batista Leal Neto.
Leal Neto was until recently Senior Researcher in the Systems Security Group at the Department of Computer Science at ETH Zurich and is now starting as Assistant Research external pageProfessor for Digital Epidemiologycall_made at the University of Arizona. He and other researchers wanted to find out how to better identify outbreaks of infection and prevent the infections from spreading. To be able to interrupt the chains of infection in a targeted manner, it is necessary to understand the interactions between people. An individual’s social network shows the possible routes along which an infection could spread.
Measurement accuracy improves preparedness
In the Wearable Proximity Platform project, computer scientists and epidemiologists from ETH Zurich, EPFL, ISI Foundation and the ETH spinoff 3db Access, developed a proximity tracking system that can measure distance to evaluate an exposure risk to an infection such as namely in a hospital environment. The technological heart of the system takes the form of special badges. These work by combining 3db Access’s UWB (ultra-wideband) radio technology with the integrated software and the experience of the SocioPatterns co-operation led by the ISI Foundation. ISI is an interdisciplinary European research centre near Turin. Over the last decade, this collaboration has carried out measurements of human proximity networks in a variety of contexts relevant to the spread of infectious diseases, spanning schools, hospitals, social gatherings and low-resource rural settings worldwide.
“When it comes to mapping infection networks, a more precise distance measurement could make a decisive difference, if not for COVID, then at least for many other diseases,” explains Leal Neto. During the COVID-19 pandemic, for example, it was established that a coronavirus infection is very likely to be passed on if an encounter lasts at least 15 minutes and takes place at a distance of less than 150 centimetres. Similar to the SwissCovid app during the pandemic, the new system therefore has the potential to improve the detection of chains of infection based on contact duration and distance.
