The simple formula to determine if your office is safe from COVID-19

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Although the majority of remote workers appear to be happy staying that way, cubicles, and boardrooms are likely somewhere in our future.

Even among those eager to return to on-location operations, safety remains a primary concern.

While pharmaceutical companies race to develop COVID-19 therapeutics, employees are keeping an eye out for frameworks that define a safe office space.

Recently, Jose-Luis Jimenez, who is a professor in the Department of Chemistry & Biochemistry at the University of Colorado and a Fellow of the Cooperative Institute for Research in the Environmental Sciences, designed a mathematical risk model premised by this very consideration.

A more accessible version of the model than the one linked above can be assessed via National Geographic. Both are relatively easy to use and can be applied to offices, classrooms, dining establishments, and even recreational indoor gatherings.

“The model is kept simple so that it can be understood and changed easily. The goal is to get the order-of-magnitude of the effects quickly, and to explore the trends,” the researchers wrote. “Several parameters are uncertain and have been estimated based on current knowledge. Alternative estimates can be entered to explore their effect in the results.”

Here’s how it works.

First, find out the percentage of infected individuals in your population with the CDC’s weekly surveillance report.

“This depends on the state of the pandemic in a given region and time period, as well as the dynamics of the disease and its infectivity in different types of cases, which are not known very precisely,” the authors add.

Next, adjust population risk based on the protection offered by the type of mask being employed in a given area.

0%-“For N95 masks that have an exhalation valve. Most of the air is exhausted through the valve, and there is little filtering”

23%-“For face shields worn without a mask. This is a guess, since the one study available is for inhalation, not for emission. But it makes sense that efficiency would be low, due to limited inertia of exhaled particles under normal breathing or talking.”

50%-“the default value for the general population, with a variety of types of masks (cloth, surgical) and also a variation on how well they are worn”

65%-“surgical masks from Milton”

90%- “For N95 masks (KN95, FF2). If well fitted and worn their efficiency for the large particles that most likely contain the viruses is 99% or more. However, we use a lower value for their use in the community in the real world, since most people are not fitted, and they are not worn perfectly and can have leaks. 90% may even be optimistic in that situation.”

After you’ve determined the effectiveness of your masks, enter the number of times you’ll be entering the space you’re assessing, the square feet of space per person, and lastly the effectiveness of the masks worn by other parties in the space.

It may seem overly intuitive, but the information needed to complete these calculations will likely be more readily available as office re-openings become more feasible.

Dr. Jimenez’s team provided similar parameters to gauge risks associated with public transportation as well.

According to the researchers, an ideal subway/ bus commute is “well-ventilated and contains “minimal talking and movement.”

For offices, the researchers define a safe space as one “with very low airflow and moderate talking and movement.”

“People are thought to be contagious mostly the week around the onset of symptoms, so that has to be taken into account in the estimates. Also, there are a fraction of undetected contagious cases (asymptomatic / presymptomatic), which will increase transmission. Plus one would hope that a major fraction of the cases that are in quarantine or a hospital and not transmitting the disease much,” the authors conclude. “The uncertainty on the fraction of contagious individuals in the community is one more reason why the absolute risk values will be uncertain, but the relative risks will still be robust.”

With the help of John FayElizabeth Albright, William Pan, and Dr. Jimenez’s initial model, Prasad Kasibhatla of Duke University developed a probabilistic framework in order to identify COVID-19 risks associated with traditional school attendance.