Another virus? I’m so Zika it.
Social networks have recently been used to track epidemics and infectious diseases. Justine Blanford and Jay Logan collaborated with Ann Jolly to track social connections and geographic locations of people that may be at high risk for diseases. They found that there were certain locations prone to more risk than others. Their findings helped health care workers find the best locations to provide healthcare and set up outreach sites. Their procedure and findings are discussed in detail here. A similar study was undertaken buy Nicholas Chistakis in 2010, who used social networks to predict epidemics.
The fact that the Ebola outbreak began in Guinea, West Africa, and the Zika virus was first found in Uganda and made its way all the way to the United States illustrates Nicholas Chrsitakis’ idea that social networks play a large part in understanding epidemics. For years, individuals have studied social networks in an effort to understand the world around them. However, as NPR discussed in this article, Nicholas Christakis and his colleague James Fowler decided to use social networks not only to understand the world around them, but to find solutions to exiting problems through social networks.
In his TED talk (linked here) in June, 2010, Nicholas Christakis made the case that by knowing the location of a node on a network, one is more likely to know if the node is to catch a disease or not. Consider the following network:
If one was trying to hear a new juicy piece of gossip, would they rather be node A or B? Node A. This is because node A has more connections or “friends” and therefore would be more likely to have new information. Just like rumors or gossip are more likely to spread to node A, diseases are also more likely to spread to node A before they spread to node B. Therefore if one was trying to refrain from catching a disease, they should want to be Node B rather than node A because node B has fewer connections to catch a disease from.
Based on this reasoning, Christakis and Fowler conducted a study at Harvard College in 2009. According to the friendship paradox “most people have fewer friends than their friends have, on average.” This means that if you ask individuals on the periphery of a social network who their friends are, and ask those friends who their friends are, you are likely to move to the center of a social network. At Harvard, Christakis and Fowler took a random group of students and asked them who their friends are. Then, they closely observed both the initial group of random students and also their friends to see who caught the H1N1 virus and who didn’t. In accordance with what they hypothesized, the group of friends caught the virus a total of 16 DAYS before the initial random group of friends. This proved that a random group of individuals have fewer friends than their friends. Hence, it is likely that the friends have more connections and are more likely to catch the disease.
By mapping social networks using data from phone companies or social media sites, epidemiologists can track the rise of an epidemic earlier, and begin preventive measures sooner so that fewer people are effected. As Justine Blanford, Jay Logan and Ann Jolly’s research illustrated, social networks can also be used to find where the most efficient outreach sites are for health care workers.