on April 22nd, 2015 No Comments
Want to know if bad air has sparked an asthma epidemic in your neighborhood? Well, you’ll have to wait several weeks using traditional epidemiologic methods, a time lag that makes prompt response efforts quite tricky.
Or, perhaps you can just check out your Twitter feed.
A team led by Sudha Ram, PhD, at the University of Arizona found that a model that aggregates Twitter data, Google searches, air quality data and asthma-related emergency room visits can predict outbreaks with 70 percent precision. It’s big data in action.
As Ram comments in a press release:
The CDC gets reports of emergency department visits several weeks after the fact, and then they put out surveillance maps. With our new model, we can now do this in almost real time, so that’s an important public health surveillance implication.
With that information, hospitals could beef up their staff and health care workers could reach out to at-risk populations.
In the future, Ram said she plans to examine diseases with greater geographic and temporal variability such as chronic obstructive pulmonary disease (COPD) and diabetes. Her research was published in a special issue of the Institute of Electrical and Electronics Engineers Journal of Biomedical and Health Informatics.
Previously: Advice for young doctors: Embrace Twitter, Mining Twitter to identify cases of foodborne illness and Text messages about asthma could help children breathe easier
Via MedCity News
Photo by Wikimedia