The Truth Unmasked

The COVID-19 Pandemic has changed the world we live in. Mundane activities now present challenges and risks to exposure. Consequently, there are a plethora of questions as to how we can safely move about our everyday lives. 

Studies have shown that after a traumatic event, like the COVID-19 Pandemic, human behaviour is permanently altered. There is an ambiguity attached to everyday activities as people begin to navigate through their familiar environments with a new socialization etiquette. In spite of the spread slowing, the risk of infection when engaging actively in communities is still very prevalent. How can we beings expect to navigate this new environment with safety and certainty?  

To effectively mitigate risk of exposure, we need to be informed of the new risk factors and risk levels that exist in their various everyday environments. The composition, challenges, and scenarios of each environment are unique and complex. As a result, the emergent behaviours and movement patterns people will exhibit in each of the environments will be influenced by the distinct variables in each setting. Results of emergent behaviours and human movement are frequently unexpected and not entirely possible to predict  using conventional techniques. The only way to fully discover what will happen in varying environments during the COVID-19 Echo period is through experiencing the many outcomes. 

However, alongside experiencing the outcomes is risk of infection and in the worst cases, death. How are citizens supposed to assess risk and make informed decisions when the only way to form an outcome is through experience? 

Our answer at RWI is Synthetics, intelligent recreations of real-world entities, including people, that behave, react and respond to each other and their environment just like their real-world counterparts, something that we call Synthetic Intelligent Environments. 

We created a Synthetic Intelligence Environment specifically to gain insight into how diverse human behaviours and movements intersect with every possible variable in an environment on the walk to work in this COVID era. We have the capability to investigate the interaction of environmental variables with human behaviour and movement, and see what emerges as people navigate the world. This Synthetic Environment enables us to explore outcomes without having to physically experience them, along with figuring out ways to influence and optimize outcomes.

The outcomes we experience in our Synthetic Intelligent Environments provide quantifiable answers to questions we all have as we navigate the COVID-19 Pandemic. RWI has set out to inform safety - supplying answers and knowledge to people around how to proceed safely, as social distancing measures are eased. 

As a starting point, we conducted an experiment to measure the difference masks can make in exposure rates if individuals wear masks during their morning commute. We began by creating a Synthetic Environment of Downtown Edmonton and added a diverse group of 500 individuals walking to work over a 40 minute period. We then ran multiple scenarios in our Synthetic Environment varying the total number of individuals wearing masks.

Results

The experiment shows that when the whole group wears masks on the way to work that exposure is dramatically reduced. The masked group has less than 10% exposure on average to the group without masks, as shown in the graph on the far right.

In any 10 second period, on average, the masked group has an effective exposure of less than a quarter second every 10 seconds.  Whereas, the unmasked group has exposure generally between 2 and 5 seconds.

For the entire trip to the workplace, approximately a 3 minute journey when traffic is light, the masked group has less than 5 seconds of effective exposure on average. Furthermore, the masked group around 30 seconds at midline are exposed for 1 minute on average at max.  This maximum average correlates to periods of congestion associated with the arrival of trains.

Background and Design

People and Movement

The overall experiment walks a group of 500 through downtown Edmonton from parking and transit to the workplace over the course of 40 minutes during the morning commute.

The movement model is like that of moving people, with each person and group navigating their way to work while attempting to distance from surrounding people through the regular turns, constraints, walk lights, and other obstacles encountered on the walk to work.

The group represents a group typical to a workplace: each with an age, gender, health, movement capability, and even a name.

Masks

Various masks have different effectiveness and are presented in a group of people in varying proportions. The type of mask an individual wears is important, as well as the varying proportions of these different mask types present in the group of 500 individuals.

Another essential factor is the ability to wear and fit the mask optimally.  The knowledge, ability, and willingness of a given individual affects the ability of the mask to filter out pathogens, including COVID.

The table and chart below shows the proportion of the types of masks and a distribution range of the effectiveness of the mask by type.  Our masked group was outfitted with masks of these types and in this proportion.

The Measures

As the group moves, we track the number of seconds that each individual spends within 2 meters of another individual.

For the ‘no mask’ scoring, this time is counted on par;  each second that individuals spend within 2 meters of each other scores an exposure second for both individuals.  For another example, if 5 individuals are within 2 meters of one another, they each score 5 person-seconds of exposure.

For the ‘mask’ scoring, this person-second score is reduced by a function of the effectiveness of the masks, as a multiple.  For instance, if both people have a 50% effectiveness mask, the score is reduced by 0.5 for the first mask, and then again by 0.5 for the second mask.

These are the two outputs of the experiment: (1) the person-seconds of exposure and (2) the mask-reduced person-second score.

This measure is taken 5 times per second, and the results are shown by seconds.

References

If you are curious to learn more about masks and whether your home-made one is protecting you, visit: https://smartairfilters.com/en/blog/category/coronavirus/

https://journals.scholarsportal.info/details/19326203/v05i0002/nfp_mmoteotsonia.xml

https://royalsocietypublishing.org/doi/full/10.1098/rsif.2011.0537

https://oem.bmj.com/content/75/6/446.abstract

Image: https://bit.ly/2ZDmihu