Woman wearing a mask uses a public touch screen to check in at an airport

Touch screens transmit less disease the more they’re touched

You’ve probably used many public touch-screen interfaces, to withdraw cash at ATMs, check-in at airports, and in numerous other places. As we’ve all learned during the past 18 months, they can be prime opportunities to transmit disease. But new research has found a surprising result: in some cases, they’re less germy if they’re touched more.

“It was an interesting result that seemed surprising at first,” says Andrew Di Battista, senior ultrasound research scientist at Ultraleap, a UK-based company that makes touch-free displays and interfaces, and first author on a paper describing the research, published in Royal Society Open Science.

“However, once you consider the full scenario it makes intuitive sense. Essentially, once a TUI (touchscreen user interface) has been contaminated there is a fixed number of pathogens available to ‘infect’ other users.

“The next couple of people to use the screen will pick up most of the available pathogens (particularly if they have to touch the screen at a higher rate). As a result, the risk to these individuals goes up with higher touch rates, while simultaneously having the effect of shielding subsequent users.”

The researchers, who are based at Ultraleap and the University of Cyprus, used computer simulations to examine the risk of disease transmission from touch screen interfaces.

“The model is meant to work as a framework where you set certain parameters, run the simulation and watch what happens,” says Di Battista.

“It turns out that TUIs have some nice simplifying features – the glass/non-porous surface correlates well with laboratory results from the literature involving touch deposit rates, pathogen survival times, etc.”

They examined the model’s sensitivity with a simulation of touch screens at one location, changing factors like disease infectivity, cleaning rate, and the rate of people touching the screen. They then ran a simulation based on data from check-in and baggage drop screens at Heathrow Airport in the UK, focusing on cleaning rate and comparing use of the screens to a non-touch alternative.

The simulations were used to predict the changes in the reproduction number. The reproduction number, or R, is the number of people expected to become infected by someone carrying a disease. A disease with an R value of 2.0 means that one person carrying it infects two other people, on average. This number varies for diseases depending on how transmissible they are, and how much opportunity there is to transmit – an area with lots of people in close contact yields higher R values than one with more space, for instance.

The researchers found several predictable results: timing of use on the TUIs makes a difference to the R value, as pathogens rarely survive for a long time without a host, for instance. High cleaning rate of screens is also associated with low transmission.

But surprisingly, the model suggested that multiple screen touches did the same thing as cleaning the screen. In a high-touch scenario, if an infected person used the screen and deposited pathogens, the next one or two users would pick all those germs up, removing them from the screen and preventing further transmission.

“Overall, the R value goes down because this is proportional to the total number of people ‘infected’ in the simulation,” says Di Battista, “but this is only because the risk to those unlucky initial one or two users after contamination goes up.

“So perhaps the R value doesn’t quite fully express all the risk.”

Di Battista says the simulation could be used to examine other high-touch public devices, like keyboards, but these can be harder to predict because they’re made of a more diverse group of materials than touch screens, and they’re handled in different ways.

Next, the researchers are planning to refine their touch-screen model, and see if they can use it to predict more complicated touch-screen interactions.

“One of the things we would like to implement is the model’s ability to estimate cross-contamination, ie pathogens picked up from one surface onto fingers/hands that get re-deposited onto the next touched surface,” says Di Battista.

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