Five Ways Big Data is Transforming Epidemics
Big data and analytical tools have transformed every industry from healthcare to retail to government, and now are transforming the way epidemics are approached.
Infectious diseases inflict a tremendous human and economic toll. The Zika virus alone could cost Latin America and the Caribbean up to $18 billion according to the United Nations.
When it comes to epidemics, we as a society suffer from a lack of timely data, disparate datasets that are difficult to collate, and a shortage of people with computational backgrounds who are involved in epidemic planning, mitigation, and response.
However, the data science revolution is allowing society to overcome these challenges, and epidemics can now be more effectively monitored, modeled and mitigated. In this article, I will outline five ways big data analytics are transforming epidemics.
Faster, cheaper genome sequencing is producing massive quantities of big data, which allows for powerful analytics into how microbes mutate while an outbreak unfolds in real-time. One big challenge for outbreak response is that genetic data are not available quickly enough. Often this is because of sample collection and testing delays, lack of collaboration and reporting tools, or holding data for publication in scientific literature. These barriers are now breaking down with the advent of Nextstrain, a tool that allows for sharing and tracking of genome sequences in real-time to improve outbreak response. Having these data available and analyzed more quickly helps track the source, evolution, and possibility of epidemic risk.
The proliferation of mobile devices means it is now possible to track how people move and better understand the path of an infectious disease. For example, GPS coordinates derived from cell phone data in West Africa allowed experts to track contacts of Ebola cases, which in turn helped inform where to focus preventive measures, as well as contain the spread. This type of tracking is useful not only for piecing together what is going on during an outbreak, but it also can help us predict how diseases could move in future outbreaks and understand what interventions would be most effective.
The abundance of data from social media has been mined to gain insight into the timing and geography of disease spread, such as seasonal influenza in the U.S. For example, researchers have used Twitter to better predict when the flu season will peak. Researchers have also applied natural language processing algorithms on social media data to perform sentiment analysis on topics such as likelihood of vaccination and level of fear felt during epidemics. These analyses can help to target control measures, public health messaging, and can also help to estimate economic impacts of fear-induced behavioral changes, such as avoidance of public places.
