A quantitative microbial risk assessment for touchscreen user interfaces using an asymmetric transfer gradient transmission mode

The ubiquitous use of public touchscreen user interfaces for commercial applications has created a credible risk for fomite-mediated disease transmission. This paper presents results from a stochastic simulation designed to assess this risk. The model incorporates a queueing network to simulate people flow and touchscreen interactions. It also describes an updated model for microbial transmission using an asymmetric gradient transfer assumption that incorporates literature reviewed empirical data concerning touch-transfer efficiency between fingers and surfaces. In addition to natural decay/die-off, pathogens are removed from the system by simulated cleaning / disinfection and personal-touching rates (e.g. face, dermal, hair and clothing). The dose response is implemented with an exponential moving average filter to model the temporal dynamics of exposure. Public touchscreens were shown to pose a considerable infection risk (similar to 3%) using plausible default simulation parameters. Sensitivity of key model parameters, including the rate of surface disinfection is examined and discussed. A distinctive and important advancement of this simulation was its ability to distinguish between infection risk from a primary contaminated source and that due to the re-deposition of pathogens onto secondary, initially uncontaminated touchscreens from sequential use. The simulator is easily configurable and readily adapted to more general fomite-mediated transmission modelling and may provide a valuable framework for future research.

Automated summary

This paper presents the results of a stochastic simulation that evaluates the risk of fomite-mediated disease transmission through public touchscreen user interfaces. The simulation shows that public touchscreens pose a considerable infection risk, and that key model parameters have an impact on the size of this risk. A distinctive advancement of the simulation is its ability to distinguish between infection risk from a primary contaminated source and that from the re-deposition of pathogens onto initially uncontaminated touchscreens through sequential use.