Epidemiological Networks

Social networks play a foundational role in modern epidemiological analysis. By mapping associations between people and assigning degrees of closeness within social circles, one can draw meaningful conclusions about how diseases propagate in communities. With the current Zika epidemic causing worldwide panic, Google has been actively recruiting volunteer data scientists and engineers to help map the spread of the virus. Partnering with UNICEF, Google is in the process of rolling out an open source platform to help visualize potential outbreaks. As of yet, there hasn’t been an established calculation of R-naught, an infectivity constant that estimates the contagiousness of a disease.

It’s worth noting that Google has also provided a one-million dollar grant for UNICEF to aid in vaccination and disease prevention research. At the present, no vaccine exists, and while reports optimistically proclaim the development of a vaccine in as little as 4 months, this is not a realistic estimate. A data driven solution combined with selective use of insecticides will likely be the fastest way to curtail the spread of this disease.

I think this an extremely fascinating applications of network theory; I would be interested to see what further epidemiological insights other tech companies, such as Facebook, can provide to better understand the nature of communicable disease. Zika presents an additional layer of complexity in its modeling. Unlike illnesses that can be spread by direct contact, aerosolized droplets, or fecal-oral transfer, Zika is an ARBO-viral pathogen that requires an insect vector. For this, temperature, humidity, and environment are all factors that need to be considered. I’m curious if a predator/prey model can be overlaid on a social network to better model the spread of Zika within the mosquito vectors as it relates to Zika prevalence in their human hosts.



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