The interest in using pre-diagnostic data for the early detection of disease outbreaks, has evolved in interesting ways in the last 10 years. In the early 2000s, I was involved in an effort to explore the potential of non-traditional data sources, such as over-the-counter pharmacy sales and web searches on medical websites, which might give earlier signs of a disease outbreak than confirmed diagnostic data (lab tests, doctor diagnoses, etc.). The pre-diagnostic data sources that we looked at were not only expected to have an earlier footprint of the outbreak compared to traditional diagnostic data, but they were also collected at higher frequency (typically daily) compared to the weekly or even less frequent diagnostic data, and were made available with much less lag time. The general conclusion was that there indeed was potential in improving the detection time using such data (and for that we investigated and developed adequate data analytic methods). Evaluation was based on simulating outbreak footprints, which is a challenge in itself (what does a flu outbreak look like in pharmacy sales?), and on examining past data with known outbreaks (where there is often no consensus on the outbreak start date) — for papers on these issues see here.
we can accurately estimate the current level of
weekly influenza activity in each region of the United States, with a reporting
lag of about one day. (also published in Nature)
|Blue= Google flu estimate; Orange=CDC data. From google.org/flutrends/about/how.html|
What can you do if you have an early alert of a disease outbreak? the information can be used for stockpiling medicines, vaccination plans, providing public awareness, preparing hospitals, and more. Now comes the interesting part: recently, there has been criticism of the Google Flu Trends claims, saying that “while Google Flu Trends is highly correlated
with rates of [Influenza-like illness], it has a lower correlation with actual influenza tests positive”. In other words, Google detects not a flu outbreak, but rather a perception of flu. Does this means that Google Flu Trends is useless? Absolutely not. It just means that the goal and the analysis results must be aligned more carefully. As the Popular Mechanics blog writes:
Google Flu Trends might, however, provide some unique advantages precisely because it is broad and behavior-based. It could help keep track of public fears over an epidemic
Aligning the question of interest with the data (and analysis method) is related to what Ron Kenett and I call “Information Quality“, or “the potential of a dataset to answer a question of interest using a given data analysis method“. In the early disease detection problem, the lesson is that diagnostic and pre-diagnostic data should not just be considered two different data sets (monitored perhaps with different statistical methods), but they also differ fundamentally in terms of the questions they can answer.