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Modelling disease transmission from touchscreen user interfaces

Andrew Di Battista, Christos Nicolaides, Orestis Georgiou

Preprint posted on October 28, 2020 https://www.biorxiv.org/content/10.1101/2020.10.28.358820v1

Article now published in Royal Society Open Science at http://dx.doi.org/10.1098/rsos.210625

A modern worry: disease transmission and touchscreen user interfaces.

Selected by Mariana De Niz

Categories: epidemiology

Background

Touchscreen User Interfaces (TUIs) are ubiquitous in modern life, from mobile technology, to fast-food menus, banks, airports, shops, and various other contexts. While the fact that touchscreens carry pathogens is not in question, what remains to be established is whether TUIs can transmit enough pathogens to a user to cause infection, and if so, of which disease. Previous work has done fomite-mediated disease transmission modeling, studying factors including pathogen infectivity, survival/persistence on surfaces, finger-to-surface transfer rates, frequency with which people interact with the surface, and how often it is disinfected/cleaned. However, it is clear that models from TUI-mediated disease transmission should consider other factors, such as hand-washing practices, or self-inoculation, whereby users transfer pathogens onto the mucosal membranes after touching contaminated surfaces. In their work, Di Battista et al (1) establish fundamental mathematical parameters that govern fomite-mediated disease transmission, and examine how each can affect disease outcomes. Moreover, they compare in their work pathogen transmission upon use of TUIs or alternative touch-free methods.

Key findings and developments

Basic scenario

  1. For transmission via fomites to occur, an infectious donor must interact with the fomite and deposit a certain amount of pathogens onto its surface.
  2. Pathogens must survive long enough for a sufficient dose to be picked up by the hands/fingers of a susceptible host.
  3. This newly exposed individual must transfer these pathogens onto mucosal regions of their face to become infected.

Assumptions

  1. Individuals use a TUI in sequence (namely, behave as if they were in a queue).
  2. Users touch the same regions of the screen, regardless of the application or screen size.
  3. All touch events are carried out with the fingertips of one main finger.
  4. Individuals do not wash their hands before and after using the interface.
  5. An infectious person is someone with relatively high initial levels of pathogens on their hands at a given time (eg. from coughing or sneezing into one’s hands, or using the toilet without washing the hands afterwards).
  6. Once a susceptible person becomes exposed, assume self-inoculation occurs within 20 minutes after TUI use. The exposed state is transitory: an exposed individual will either self-inoculate or revert back to a susceptible state after TUI use.
  7. Only one single time period is considered (a day) using a 1-minute time-step, without considering incubation periods or recovery rates.
  8. The three main actors are: the pathogens, the network of touchscreens and the network of people, each having its own controlling parameters.

Outcomes

In fomite-mediated transmission, a pseudo-reproduction number (R) can be defined as the number of susceptible people that the fomite infects having been contaminated by an infectious person. Another important metric is the dap between infectious contamination and subsequent susceptible users becoming infected.

Using their model, and the above assumptions, below are the questions the authors aimed to answer with the model. To answer the questions, the authors performed 2 simulations, one using a single TUI, and one using a real-world example with a real location: Terminal 5 of London Heathrow airport.

  1. What is the probability of becoming infected after using a TUI?
  2. On average, how many susceptible individuals could become infected as a direct result of a single infectious user over the course of a day?
  3. Which TUI users are getting infected (what’s the gap between infectious and infected)?
  4. What is the efficacy of frequent cleaning on reducing the probability of infection?

Key results from the simulations

  1. Timing plays an important role.
  2. Increasing the number of locations gives infectious individuals multiple chances to contaminate TUIs and infect other users.
  3. The model implies that the susceptible users who immediately follow an infectious user are most at risk. The next susceptible user is almost doomed to infection while shielding the subsequent users.
  4. In order to use the simulator to model specific diseases, it would be necessary to collate shedding rates and infectious dose information.
  5. The model does not consider re-deposition of pathogens from newly infected individuals. However, the authors consider it is possible to assume that re-deposition would likely increase the overall infection rates in the scenarios tested.
  6. An effective way to mitigate spread via fomites is compulsory handwashing. The authors note the relevance of the paper depends on whether or not a population will maintain stringent hygienic practices.
  7. It seems that cleaning rates of fomites in the order of hundreds of times per day, would be required to have a significant effect on R.
  8. The authors then discuss an alternative and attractive solution, which is self-cleaning antimicrobial surface coatings. However, not all pathogens are significantly affected.
  9. Another alternative are touch-free interfaces.

What I like about this preprint

I liked this paper because hygiene and fomite-mediated pathogen transmission are topics I find very interesting. Especially in our current day and age, the use of surfaces such as mobile phones, computers, as well as touchscreens in other contexts such as ATMs, fast food ordering, queuing systems, etc, is highly widespread. While in general, the effect of touchscreen interfaces on pathogen transmission is a relevant and current topic, in view of the current pandemic, I think it is even more so. I found this work very interesting altogether.

Open questions

  1. Can the model be expanded to increase other interactions and assumptions, for instance, touching “vector” surfaces in-between surfaces, such as mobile phones?
  2. You mention in your discussion that touchscreen cleaning would need to be in the order of 100s of times per day to have an effect on disease transmission. Some places have implemented the use of removable/exchangeable plastic interfaces that are changed every so often. How would a combination of both approaches (cleaning and switching the cover) affect disease transmission?
  3. Can the known parameters for a plethora of pathogens be implemented into your model, to investigate their transmission via TUIs? If solid enough perhaps this can be used in global health situations and epidemiological investigations in the future?

References

  1. Di Battista et al, Modelling disease transmission from touchscreen user interfaces, bioRxiv, 2020.

 

Posted on: 12th December 2020

doi: https://doi.org/10.1242/prelights.26376

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