Understanding mask wearing trends using diffusion
Source 1: https://www.cdc.gov/coronavirus/2019-ncov/your-health/need-to-know.html
Source 2: https://www.pnas.org/content/117/36/21851
For the past 9 months or so, one of the most important topics discussed around the world has been how to slow the spread of Covid-19. As we learned in the epidemic module of this course, there are two key ways to control the spread of a pandemic. One way is to reduce “k”, or the number of people that an infected individual interacts with. The other way is by reducing “p”, or the probability that a person gets infected given they were in contact with an infected individual. The CDC has introduced several guidelines to help slow the pandemic, and each guideline seeks to either reduce “p” or reduce “k”.
The primary guideline that the CDC has on their website (Source 1) which seeks to reduce “k” is to “avoid large gatherings”. However, if a person has symptoms of Covid-19 or tested positive, they are also instructed to stay home and isolate themselves from their family. While this is dramatic and can be hard to get used to, getting “k” to as close to 0 as possible for infected individuals is incredibly important in slowing the spread of Covid-19. For healthy people, however, most of the guidelines are directed at reducing “p”. Guidelines that seek to reduce “p” include wearing masks in public, maintaining a distance of at least 6 feet from others, and washing hands often. What’s interesting about both the guidelines that seek to reduce “k” and the guidelines that seek to reduce “p” is that the speed in which these policies are adopted seems to partially depend on cultural pressure.
The study linked in Source 2, for example, found that independent of policy, people who wear masks perceive each other more positively, and people who do not wear masks are socially punished. What’s interesting about this study is that it suggests that mask wearing, and perhaps other Covid-19 prevention measures, can be analyzed through the lens of network diffusion. Once a certain number of people that a person interacts with start to wear masks, they too will likely start to wear masks in order to avoid social punishment. This can easily cause a ripple effect that results in the majority of a community wearing masks. However, more isolated communities will have to have a certain number of individuals independently decide to wear masks. For example, earlier on in the pandemic, there were several places where many did not interact with enough people who wore masks, so they did not have to worry about social punishment as much as those in other parts of the country. As a result, those individuals did not wear masks themselves.
In the midst of the Covid-19 pandemic, networks concepts can play an important role in making all kinds of predictions and observations to help us better understand patterns of the virus. While networks can obviously be used to estimate how quickly a virus will spread based on “k” and “p” values, other networks concepts can be utilized. For example, as discussed in this blog post, the adoption of virus slowing techniques can be better understood on a community level since people will be more inclined to adopt such policies if others around them do too.