#OpenData for surveillance: Flu and fires

Monday, March 19, 2018

I'm always intrigued to see analysts use traditional data (particularly government and/or open data) in novel ways, especially for monitoring or surveillance for health and human rights. Two such analyses crossed my feed last week. The first, a model built by an epidemiological team at the University of Iowa, used CDC influenza surveillance data combined with readings from an IoT-style thermometer called Kinsa to beef up predictions of flu transmission patterns:
Smart devices and mobile apps have the potential to reshape public health alerts and responses, health care industry analysts and researchers told The Bee. Over in San Francisco, for instance, the staff of smart thermometer maker Kinsa were receiving temperature readings that augured the surge of flu patients in emergency rooms there.
Computational researcher Aaron Miller worked with an epidemiological team at the University of Iowa to assess the feasibility of using Kinsa data to forecast the spread of flu. He said the team first built a model using surveillance data from the CDC and used it to forecast the spread of influenza. Then the team created a model where they integrated the data from Kinsa along with that from the CDC.

“We got predictions that were ... 10 to 50 percent better at predicting the spread of flu than when we used CDC data alone,” Miller said. “Potentially, in the future, if you had granular information from the devices and you had enough information, you could imagine doing analysis on a really local level to inform things like school closings.”
Setting aside my own unease with the "wild, wild west" nature of data collection by Internet of Things things, it was nice to see the study authors reinforce the value of CDC surveillance data (and not going the way of Google Flu):
Miller does not think, though, that these new sources of data have yet made the traditional CDC surveillance channels obsolete.

“When it comes to studying things like infectious diseases, the challenge is when we’re just getting things like temperature readings,” Miller said. “Because we’re not working with microbiological specimens, we can’t confirm that what we’re looking at is influenza. ... We went through a number of procedures in the paper to try to validate that what we were looking at influenza, but at the end of the day, we can’t necessarily confirm that.”
Unfortunately, the picture is not nearly as rosy for this analysis of arson in DRC described in the New York Times:
The clashes between the Hema and Lendu communities — on the eastern side of the Ituri province, bordering Uganda — started in December and escalated in early February.

Historically, these distant conflicts have been difficult to analyze. But new technologies allow us to investigate them in close to real time.

I immediately collected active-fire data from NASA — thermal anomalies, or hot spots, that are recorded daily. It showed dozens of fires on the densely forested mountain ridge and along the shoreline of Lake Albert, one of the African Great Lakes between Congo and Uganda.
Google and other online mapping platforms often show only blurry satellite images, or have no location names for remote areas such as the small fishing villages around Lake Albert. This makes it difficult to find places where people live. To deal with this challenge, I exported residential data from the online mapping site Openstreetmap.

I then overlaid the NASA data with this new data in Google Earth to look for recorded fires that were in or near populated places.
The author goes on to explain that they used this data in conjunction with traditional "shoe-leather reporting" to tell the story "in the most powerful and visual way." I have really appreciated that journalists have begun accompanying their reporting with pieces that explain their methodology in detail. One silver lining amid all the tempest over "fake news."

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