Honey Bee Epidemics
As we learned in Chapter 21 and on Tuesday, we can drastically improve the prediction of network behavior through the friendship paradox and the structure of networks. Most network behavior regarding information, sickness, etc. can be described with the S-shaped adoption curve. By using the friendship paradox and other indications of network structure to target central nodes, we can move the adoption curve to the left when graphed as a function of time. In other words, we can detect the beginning of the spike in slope of the S-shaped curve at a significantly earlier point then if we use a random sample. In order to find nodes that are more central in the network, researchers can have groups list their friends, and find the intersection of peoples’ lists. This is useful to help predict epidemics faster.
The most common — and possibly most useful — application of this idea is to predict sicknesses, viruses, and epidemics in society. However, especially on a college campus like Stanford, there are many more entertaining applications. For instance, the idea of predicting network behavior earlier by finding more central nodes can be applied to the concept of fashion. By looking at the leaders of the fashion industry, who are constantly pioneering new looks, we can predict the popularity of items much faster. We can also apply this to drug use. By focusing our attention to central nodes in the network of drug users, we can predict new drugs and develop precautionary measures. Likewise, we can do the same thing with STDs. Of the network of people who are sexually active, we can monitor those who are the most sexually active to predict STD outbreaks earlier.
One somewhat abstract application of epidemic monitoring does not involve humans at all, actually. In recent years, we have become aware of the declining honey bee population. Many describe it as colony collapse disorder. However, if scientists we able to model the network of honey bees, which can roughly be done through general population and location metrics, they could predict the areas in which the population will suffer. In simpler terms, had/have scientists modeled honey bees and thought of their population as a network, they could target more populated regions of honey bees and monitor their health. By observing the decline in population of central nodes/groups within the honey bee network, scientists could have a better understanding of the causes, timing, and general order of honey bee decline. This could help mitigate the effects of climate change and could be applied to many different animals for the promotion of a healthy ecosystem. By doing so, scientists could bypass the ethical questions involved with the modeling of human networks and potential exploitation of information.
http://sos-bees.org/causes/
