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Seasonal influenza is a sometimes surprisingly impactful disease, causingthousands of deaths per year along with much additional morbidity. Timelyknowledge of the outbreak state is valuable for managing an effective response.The current state of the art is to gather this knowledge using in-personpatient contact. While accurate, this is time-consuming and expensive. This hasmotivated inquiry into new approaches using internet activity traces, based onthe theory that lay observations of health status lead to informative featuresin internet data.  These approaches risk being deceived by activity traces having acoincidental, rather than informative, relationship to disease incidence; toour knowledge, this risk has not yet been quantitatively explored. We evaluatedboth simulated and real activity traces of varying deceptiveness for influenzaincidence estimation using linear regression.  We found that deceptiveness knowledge does reduce error in such estimates,that it may help automatically-selected features perform as well or better thanfeatures that require human curation, and that a semantic distance measurederived from the Wikipedia article category tree serves as a useful proxy fordeceptiveness. This suggests that disease incidence estimation models shouldincorporate not only data about how internet features map to incidence but alsoadditional data to estimate feature deceptiveness. By doing so, we may gain onemore step along the path to accurate, reliable disease incidence estimationusing internet data. This capability would improve public health by decreasingthe cost and increasing the timeliness of such estimates.

Estimating influenza incidence using search query deceptiveness and generalized ridge regression