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Title: Estimating influenza incidence using search query deceptiveness and generalized ridge regression

Abstract

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 additionalmore » 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.« less

Authors:
ORCiD logo [1]; ORCiD logo [2];  [3];  [3]; ORCiD logo [1]
+ Show Author Affiliations
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  2. Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Univ. of Colorado, Boulder, CO (United States)
  3. Minnetonka Public Schools, MN (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1597332
Report Number(s):
LA-UR-18-24467
Journal ID: ISSN 1553-7358
Grant/Contract Number:  
89233218CNA000001; 2016-0595-ECR
Resource Type:
Accepted Manuscript
Journal Name:
PLoS Computational Biology (Online)
Additional Journal Information:
Journal Name: PLoS Computational Biology (Online); Journal Volume: 15; Journal Issue: 10; Journal ID: ISSN 1553-7358
Publisher:
Public Library of Science
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; Biological Science; Information Science

Citation Formats

Priedhorsky, Reid, Daughton, Ashlynn Rae, Barnard, Martha, O'Connell, Fiona, and Osthus, David Allen. Estimating influenza incidence using search query deceptiveness and generalized ridge regression. United States: N. p., 2019. Web. doi:10.1371/journal.pcbi.1007165.
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Priedhorsky, Reid, Daughton, Ashlynn Rae, Barnard, Martha, O'Connell, Fiona, & Osthus, David Allen. Estimating influenza incidence using search query deceptiveness and generalized ridge regression. United States. doi:10.1371/journal.pcbi.1007165.
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Priedhorsky, Reid, Daughton, Ashlynn Rae, Barnard, Martha, O'Connell, Fiona, and Osthus, David Allen. Tue . "Estimating influenza incidence using search query deceptiveness and generalized ridge regression". United States. doi:10.1371/journal.pcbi.1007165. https://www.osti.gov/servlets/purl/1597332.
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@article{osti_1597332,
title = {Estimating influenza incidence using search query deceptiveness and generalized ridge regression},
author = {Priedhorsky, Reid and Daughton, Ashlynn Rae and Barnard, Martha and O'Connell, Fiona and Osthus, David Allen},
abstractNote = {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.},
doi = {10.1371/journal.pcbi.1007165},
journal = {PLoS Computational Biology (Online)},
number = 10,
volume = 15,
place = {United States},
year = {2019},
month = {10}
}
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DOI:  10.1371/journal.pcbi.1007165
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    Even a good influenza forecasting model can benefit from internet-based nowcasts, but those benefits are limited
    journal, February 2019

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