The World Health Organization has declared the Zika Virus a public health emergency that could affect four million people in the next year as it spreads across the Americas.
Big data and analytics have played a role in containing previous viral outbreaks such as Ebola, Dengue fever and seasonal flu, and lessons learned there are undoubtedly being put to use in the fight against Zika. However while statistical modeling of vast, real time datasets is becoming ingrained across healthcare and emergency response, the support infrastructure needed to put these initiatives to work at ground level is lagging behind, experts believe.
Big data research has dramatically sped up the development of new flu vaccines. By analyzing the results of thousands of tests at institutions around the world, compounds can be developed to target the specific proteins that are found to enable the virus to grow. Big data is also used by epidemiologists to track the spread of outbreaks.
The Google Flu Trends project, launched in 2008, was one of the first to show how search engine data could be used to predict flu trends. While it often achieved high levels of accuracy (correlating with US CDC reporting of flu outbreaks at a rate of 97%), its tendency for occasionally making wildly inaccurate predictions is often attributed to its reliance on one primary (and volatile) source of data. Search engine queries, it has been established, are often influenced by other factors such as media reporting or public concern over other illness not related to flu.
Jamie Powers, consultant to the healthcare industry with SAS, says that “This highlights why multiple, disparate data sources are needed for this type of predictive analysis. We need to connect vaccine-makers, the CDC and other national and state public health agencies, even health providers, before an outbreak is identified.”
To tackle a virus like Zika, a rich and varied source of data from clinical trials, surveillance activities and provider networks could be used to more accurately predict developments. Lessons learned during the Ebola outbreak, which peaked in 2014, indicate that the infrastructure for putting analytics-derived insights to work in the field of viral outbreaks is currently not yet in place, particularly in the developing world.
Powers cites solutions in other areas of healthcare – such as Project Datasphere aimed at tackling cancer– that can deliver results when stakeholders are invited to take part in open, collaborative projects involving big datasets.
Viruses like Zika, which spring on us so fast, do raise people’s attention to the issue. The public health surveillance systems is already in place, but it takes too long to do the analysis and report back and it is hard for vaccine developers to access the data from different sources to accelerate their drug development process. Powers believes “The idea of a platform where we can analyze multiple different data sources, alongside all of the stakeholders – crowd sourcing solutions – could be the way we can do this.”
In fact – the problems facing the industry are identical to those faced by most other industries attempting to get to grips with big data. There’s plenty of data – more than anyone could ever use. The challenge is identifying the right data and getting it in the hands of the right people who can design and implement solutions.
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. Ebola showed us that every aspect of a virus’s 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 article was written by Bernard Marr from Forbes and was legally licensed through the NewsCred publisher network.