# How much will masks reduce coronavirus transmission rate R0?

Posted In: Health

#### It depends on their effectiveness and how many people wear them

R0 is the transmission rate which is defined as the average number of cases that are expected to be produced from a single case in an uninfected population. R0 is dependent on a number of different factors that include transmissibility of a disease (how infectious it is), the amount of social contact and the duration of social contact.

A baseline level of social contact is related to the population density (how often you come into contact with other people) and social distancing (limiting gatherings, not going in to work or school, etc) will reduce the amount of social contact with different people. Given what we know about coronavirus and its transmission, the amount of “contact” can also be influenced by mask wearing. This interactive graph shows the effect of mask wearing and effectiveness on reducing R0 even further.

This graph is a work-in-progress so please feel free to provide suggestions and feedback on issues of scientific concepts as well as for improvements in conveying the concepts/ideas.

##### Methodology

R0 values for different regions and population densities are estimated from Youyang Gu’s machine learning model for spread in Feb and early-March (i.e. before social distancing and mask wearing).

Baseline R0,baseline based on population density – R0 value ranges from about 6 in very high density places like New York City with lots of transit use where you are in close contact with other people for long periods of time to 2 in rural areas with much less contact.

Social distancing factor (SDF) – this is simply a reduction on the baseline R0 based on the amount of social distancing (ranges from 100% (no social distancing) to 33% (high levels of social distancing). This is a reduction in the amount of time and number of people the average person is exposed to compared to baseline levels.

Percent wearing masks (Kmaskfreq) – is simply the percentage of people wearing masks (varies from 0% to 100%). This parameter is shown on the y-axis.

The formula for effective Reffective is:

$R_\mathit{eff}=R_0,baseline \times SDF \times (1-K_{mask\mathit{eff}} \times K_{maskfreq})^2$

where $R_\mathit{eff}$ is the final average transmission value, $R_0,baseline$ is the $R_0$ value based on the population density, SDF is the social distancing factor, $K_{mask\mathit{eff}}$ is the average mask effectiveness and $K_{maskfreq}$ is the percentage of people wearing masks. The squared parameter on the right side of the equation is essentially the average reduction in transmission that is likely due to mask usage and is from a preprint from Howard et al.

As you move up and to the right of the graph, mask use and effectiveness become very high and the transmission of coronavirus declines significantly. If you hover over the graph (on a desktop) or click on the graph (on mobile) you will see a popup that shows the Reff value that results. The lower the Reff value is the better as it dramatically affects the rate of transmission. High numbers will lead to explosive exponential growth while values below 1.0 will eventually reduce coronavirus transmissions to near 0.

For example at R0 of 6 and no social distancing or mask usage, one initial case can lead to approximately 56,000 cases in only 30 days. Whereas an Reff of 0.5 will only lead to a total of ~1 additional case in 30 days.

Please let me know in the comments if you have any questions or suggestions on how the tool works, is structured or presented.

Source and Tools:
The reduction in R0 due to mask effectiveness and usage based on a model from a preprint from Howard et al. Baseline R0 are from Youyang Gu’s machine learning model. Calculations are done in javascript and visualization is done with the open source Plotly javascript graphing library.