Name
Title | ||
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Estimating influenza incidence using search query deceptiveness and generalized ridge regression. |
Abstract | ||
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Author summary While often considered a minor infection, seasonal flu kills many thousands of people every year and sickens millions more. The more accurate and up-to-date public health officials' view of what the seasonal outbreak is, the more effectively the outbreak can be addressed. Currently, this knowledge is based on collating information on patients who enter the health care system. This approach is accurate, but it's also expensive and slow. Researchers hope that new approaches based on examining what people do and share on the internet may work more cheaply and quickly. Some internet activity, however, has a history of correspondence with disease activity, but this relationship is coincidental rather than informative. For example, some prior work has found a correspondence between zombie-related social media messages and the flu season, so one could plausibly build accurate flu estimates using such messages that are then fooled by the appearance of a new zombie movie. We tested flu estimation models that incorporate information about this risk of deception, finding that knowledge of deceptiveness does indeed produce more accurate estimates; we also identified a method to estimate deceptiveness. Our results suggest that estimation models used in practice should use information about both how inputs maps to disease activity and also what the potential of each input to be deceptive is. This may get us one step closer to accurate, reliable disease estimates based on internet data, which would improve public health by making those estimates faster and cheaper. Seasonal influenza is a sometimes surprisingly impactful disease, causing thousands of deaths per year along with much additional morbidity. Timely knowledge of the outbreak state is valuable for managing an effective response. The current state of the art is to gather this knowledge using in-person patient contact. While accurate, this is time-consuming and expensive. This has motivated inquiry into new approaches using internet activity traces, based on the theory that lay observations of health status lead to informative features in internet data. These approaches risk being deceived by activity traces having a coincidental, rather than informative, relationship to disease incidence; to our knowledge, this risk has not yet been quantitatively explored. We evaluated both simulated and real activity traces of varying deceptiveness for influenza incidence 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 than features that require human curation, and that a semantic distance measure derived from the Wikipedia article category tree serves as a useful proxy for deceptiveness. This suggests that disease incidence estimation models should incorporate not only data about how internet features map to incidence but also additional data to estimate feature deceptiveness. By doing so, we may gain one more step along the path to accurate, reliable disease incidence estimation using internet data. This capability would improve public health by decreasing the cost and increasing the timeliness of such estimates. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1371/journal.pcbi.1007165 | PLOS COMPUTATIONAL BIOLOGY |
DocType | Volume | Issue |
Journal | 15 | 10 |
ISSN | Citations | PageRank |
1553-734X | 1 | 0.41 |
References | Authors | |
8 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Reid Priedhorsky | 1 | 416 | 36.85 |
| Ashlynn R. Daughton | 2 | 7 | 3.39 |
| Martha Barnard | 3 | 1 | 0.41 |
| Fiona O'Connell | 4 | 1 | 0.41 |
| Dave Osthus | 5 | 4 | 2.53 |