Big Data is transforming epidemics in these five ways


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.

Better genetic data

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.

Cell phone mobility data

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.

  • By Nita Madhav, Senior Director of Data Science for Metabiota

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