Corona Virus With a Network Based Explanation
https://www.pnas.org/content/117/37/22684
After learning about how epidemics spread and the different models present to explain disease spread, this article was very interesting. It talks about R0 and how this value being greater than or less than 0 effect how rapidly disease spreads. Additionally, the paper notes that quarantining, wearing mask, social distancing are all ways to reduce the R0 value. Finally, it introduces the Susceptible–Infected–Recovered (SIR) Model and explains how current covid situations can be attributed to this model. However, it does comment that there are parts where reality does not match with the SIR Model. In basic epidemic models, the infection growth rate is usually not linear, while the covid data seems to have some extended linear areas. A simple explanation could be a limiting capacity of availability of test kits. If the daily number of tests is limited and assuming a fixed ratio of confirmed cases per test, linear growth in the number of positively tested would be the consequence.
This article was super interesting to read since it directly ties into what we are learning. In class, we went over the SIR Model and examples of how disease such as covid or the flu spreads through a network perspective. Now I get why covid protocol and other things are implemented!