Using Networks to Understand and Model Epidemiology
https://phys.org/news/2019-10-airborne-disease-diffusion.html
Network theory, as we have learned, has a variety of uses that spans many different disciplines which is why our course is cross listed amongst many departments. The attached article is an example of how understanding network theory can help us in areas like healthcare and epidemiology. A team at Macquarie University is constructing dynamic contact models that can help us understand how airborne diseases spread across a population. While many existing models assume transmission from two nodes must occur with both in the same physical vicinity at the same time, this team’s model accounts for the fact that particles that contain diseases in the air through actions like coughing and sneezing can remain in the air for some time, even after its source person leaves the area. So far, researchers haven’t been able to capture these types of indirect transmissions which hinders that information that a model can portray. While these models are definitely a useful tool, we can clearly see that there are some limitations to them. Over time, we see models becoming more advanced and accurate as researchers find clever ways to incorporate more variables into them.
In class, we saw how networks can be used to represent information in a variety of different fields of study. We saw how networks can help us model traffic flow and social networks amongst people. Graph theory is very useful especially due to our ability to communicate graph networks into computers that can allow us to make incredible predictions in mere seconds. While it is essentially impossible to create models with 100% accuracy, understanding their drawbacks and imperfections is pivotal to utilizing them as a powerful tool in interdisciplinary studies.