We hear all the time about innovative and interesting things that big data can be used for, but it’s rare we actually get to experience it for ourselves. With recent events, however, that may be changing.
Medical information — or big data — extracted from health records, Internet resources, social media and even some other sources is being used to combat infectious diseases and deadly outbreaks. This is extremely important, because in the past, physical information such as laboratory test results and public health records have been the focus. However, there are some disadvantages with using traditional information.
First, it’s expensive to produce, and it wastes a lot of paper. Additionally, due to the nature of the medium, it can come with some serious time lags. The information has to be printed, then the materials must be sent to wherever they’re needed physically.
This is not the case with digital information and big data streams. The information is available almost instantly, in real-time, and it’s relatively inexpensive to produce and share.
All of this — and more — was covered by a group of scientists from the National Institutes of Health, where they published a report in The Journal of Infectious Diseases.
Cecile Viboud, Ph.D., and co-editor of the report says the “ultimate goal” is to be able to “forecast the size, peak or trajectory of an outbreak weeks or months in advance in order to better respond to infectious disease threats.”
There are several ways they are doing this, the most important of which we’ll discuss below.
Mapping infectious diseases
Not surprisingly, outbreaks are common in hospitals and places where there are a concentrated number of ill people. These kinds of contamination events are known as HAIs or “healthcare associated infections.” HAIs account for more than 700,000 unnecessary infections each year and result in 75,000 in-hospital deaths.
One benefit of using big data to track such events is that we can more accurately map the spread of infectious diseases like those that occur from HAIs. Traditional risk maps become outdated rather quickly because diseases and outbreaks spread fast. A digital map with accurately geo-positioned data will allow us to see the full breadth of an outbreak and make predictions as well.
During an outbreak, a disease can make rapid shifts — namely in how it spreads and to where. Evolving, interactive maps can give everyone a complete picture of the impact an outbreak has on an area or population, and present the information in a way that allows scientists to predict where the disease will spread.
A simulation data management system for outbreaks already exists, called epiDMS. These kinds of large-scale computational models rely heavily on big data to make accurate predictions and, as we become better at collecting big data, we’re likely to see even better epidemic modeling in the future.
One way this kind of modeling will be helpful in the future is likely to be in the understanding and monitoring of drug-resistant diseases.
Monitor superbugs, resistance and immunities
With drug-resistant bacteria becoming an ever-increasing worry for many people, it’s not difficult to understand why we’d want to develop big data modeling systems to monitor resistance to a highly infectious disease or even locate those with natural immunities.
This would allow scientists to come up with a cure much faster and prevent the spread of a disease on a wider scale.
ResistanceOpen, an online tool created by US and Canadian scientists, can be used to monitor and identify antibiotic resistance at a regional level. It analyzes publicly available data to create reports about antibiotic resistant superbugs in an area. Scientists and medical professionals can use the collected data to identify problematic areas for certain outbreaks.
But it could also be applied to a number of other properties. Imagine being able to find those immune to a disease or ailment before it has any significant impact. Again, this would allow for a cure or vaccine to be generated much faster.
Disease evolution data through surveillance
In Europe, there exists a surveillance system called Influenzanet, which uses online data to gather info from volunteers that self-report symptoms they experience. The tool can be used to get a better understanding for various outbreaks and see how they evolve over time.
For instance, European Union member states are currently adapting the Influenza tool to track diseases like Zika, Salmonella, E. Coli and more.
The crowdsourced data provides a much better understanding of how diseases affect certain groups of people. It also allows scientists to do things like map out geographical risk charts, which we discussed above.
Big data is critical
While these are just a few examples of how big data is being used to combat serious outbreaks or diseases, it’s easy to see how critical the technology is. It will be interesting to see how the industry evolves over time, specifically when it comes to medical data and public health records.
Ultimately, it should help us to prepare for potential issues or to take action when something does happen.