TY  - GEN
UR  - https://doi.org/10.1145/3308558.3313477
PB  - ACM
ID  - discovery10074319
N2  - A considerable body of research has demonstrated that online
search data can be used to complement current syndromic surveillance systems. The vast majority of previous work proposes solutions that are based on supervised learning paradigms, in which
historical disease rates are required for training a model. However,
for many geographical regions this information is either sparse or
not available due to a poor health infrastructure. It is these regions
that have the most to benefit from inferring population health statistics from online user search activity. To address this issue, we
propose a statistical framework in which we first learn a supervised model for a region with adequate historical disease rates, and
then transfer it to a target region, where no syndromic surveillance
data exists. This transfer learning solution consists of three steps:
(i) learn a regularized regression model for a source country, (ii)
map the source queries to target ones using semantic and temporal similarity metrics, and (iii) re-adjust the weights of the target
queries. It is evaluated on the task of estimating influenza-like illness (ILI) rates. We learn a source model for the United States, and
subsequently transfer it to three other countries, namely France,
Spain and Australia. Overall, the transferred (unsupervised) models
achieve strong performance in terms of Pearson correlation with
the ground truth (> .92 on average), and their mean absolute error
does not deviate greatly from a fully supervised baseline.
A1  - Zou, B
A1  - Lampos, V
A1  - Cox, I
T3  - The World Wide Web Conference
CY  - San Francisco, CA, USA
EP  - 2516
Y1  - 2019/05/12/
AV  - public
SP  - 2505
TI  - Transfer learning for unsupervised influenza-like illness models from online search data
N1  - This paper is published under the Creative Commons Attribution 4.0 International
(CC-BY 4.0) license. Authors reserve their rights to disseminate the work on their
personal and corporate Web sites with the appropriate attribution
https://creativecommons.org/licenses/by/4.0/
ER  -