Fifth Blog Post
Networks and Epidemic Models
Keeling, M. J., & Eames, K. T. (2005). Networks and epidemic models. Journal of the Royal Society Interface, 2(4), 295-307.
This journal is written by Matt J. Kneeling and Ken T.D Eames. It begins by discussing the standard epidemic theory, which is a mathematical theory that I am familiar with from other journals. This journal quotes another journal that I have read, which I thought was pretty cool. Specifically, this talks about the SIR model and SIS model. The SIR model is used within diseases that result in lifelong immunity, like measles; whereas the SIS model is used for diseases where repeat infections are common, like sexually transmitted diseases. These models are based on the assumption that the population mixes at random, where each individual has an equal chance of coming into contact with another individual. This is not truly accurate, however, because individuals typically have a certain set of individuals that they are most likely to come into contact with. The random-mixing assumption is avoiding by assigning individuals to a finite set of contacts with which they can infect/become infected by. This seems to be more accurate. This journal then goes on to describe the standard network theory, something I was not already aware of. This section also cites another journal that I have read. The journal states how it is important to determine a complete mixing network, but that this is rather time consuming. It is also very difficult to sample an entire population, and even if the entire population is sampled, not everyone will be willing to give up truthful information. It is also to important which contacts are capable of disease transfer, something that is also difficult to measure. Different infections require different levels of contact. The journal uses the example of the flu versus something like an STD. This is something that seems obvious, but something that I overlooked. The journal then goes on to discuss main techniques that have been used to gather network information, and then describe each in detail. These techniques include infection tracing, complete contact tracing, and diary-based studies. It then goes on to discuss different types of networks which include random networks, lattices, small-world networks, spatial networks, scale-free networks, and exponential random graph models. These networks are defined by how individuals are distributed in space and how connections are formed. At the end of the journal, it discusses the future and how future advances will help determine networks. A specific example is the future of GPS tracking. Due to GPS, it may be possible in the future to accurately track people’s movement. This would make it possible to build a full networks for airborne diseases. I think this is really cool.