Stopping the Zika virus: The potential of big data, analytics

By Daniel Teachey, SAS Insights Editor

Tracking the spread of disease is one of the most powerful tools for health care officials. This information can help practitioners in a region – or around the world – prepare for an outbreak and mitigate its spread as much as possible.

However, the goal for health care organizations is to go from reacting to anticipating the spread of diseases. With analytics, that’s becoming more of a reality. More information, available sooner, can improve health outcomes and save lives.

By analyzing data from thousands of points around the world, compounds can be developed to target the specific proteins that enable the virus to thrive.

Today the focus is on the Zika virus, spread primarily through the bite of the Aedes mosquito. As of March 2016, Zika is present across Africa, Asia and the Americas, with the heaviest number of cases in Brazil. The World Health Organization (WHO) declared the Zika virus a public health emergency that could affect 4 million people in the next year as it spreads across the Americas. At the same point, more than 80 cases of travel-related Zika have been confirmed in the US, according to the Centers for Disease Control and Prevention(CDC).

As the world scrambles to fight the outbreak, analysts at SAS are investigating how data mining technology can track diseases like the Zika virus and help create a different type of response to global disease outbreaks. Knowledge about the disease and how it's spreading is more readily available than ever before. And the more you know about how the disease is spreading and where it’s likely to go next, the more effectively you can mobilize resources and develop strategies to combat it.

“From a technological standpoint, we already have everything we need to leverage big data to quickly and effectively develop vaccines for new viruses such as Zika,” said Bernard Marr, author of Big Data: Using SMART Big Data, Analytics and Metrics To Make Better Decisions And Improve Performance (Wiley, January 2015). “Ebola showed us that every aspect of a virus’ behavior and characteristics can be isolated and identified. Now what we need are platforms and systems to get this data into the hands of those who can develop solutions before a public health emergency develops.”

This type of predictive analysis requires multiple, disparate data sources. The value of bringing together diverse data communities has already been shown in initiatives such as Project Data Sphere, which tackles cancer, and, which shares anonymized results of clinical trials to support third-party research. Rapid and unexpected discoveries occur when stakeholders are invited to take part in open, collaborative projects involving massive, shared datasets and big data analytics.

Think of the possibilities. Analytics experts join forces with local health agencies, the CDC, the WHO, the academic research community and vaccine makers. This collaboration would bring together diverse data – and perspectives – to support analytical insights that are not possible when viewing data sources in isolation.

The data at the core of this initiative could include lab data from those being screened for Zika and other diseases, clinical trial data, data from surveillance activities and provider networks, and even social media trends. All of these can play a role in fighting the spread of the outbreak. Tying it all together could help speed up the process of developing a vaccine. By analyzing data from thousands of points around the world, compounds can be developed to target the specific proteins that enable the virus to thrive.

“We’re not talking about solving Zika tomorrow or the next day, but we are talking about bringing all the relevant knowledge together into a central place,” said Jamie Powers, a SAS health care industry consultant. “This collaborative, analytics-driven approach would have implications not only for fighting the spread of Zika but also for getting ahead of future outbreaks. I would envision a future where, when the next big outbreak is coming, we have the infrastructure in place to rapidly identify and respond.”

Scientists look at computer in a lab

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