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<https://discovery.ucl.ac.uk/id/eprint/10074319> <http://purl.org/ontology/bibo/abstract> "A considerable body of research has demonstrated that online\r\nsearch 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\r\nhistorical disease rates are required for training a model. However,\r\nfor many geographical regions this information is either sparse or\r\nnot available due to a poor health infrastructure. It is these regions\r\nthat have the most to benefit from inferring population health statistics from online user search activity. To address this issue, we\r\npropose a statistical framework in which we first learn a supervised model for a region with adequate historical disease rates, and\r\nthen transfer it to a target region, where no syndromic surveillance\r\ndata exists. This transfer learning solution consists of three steps:\r\n(i) learn a regularized regression model for a source country, (ii)\r\nmap the source queries to target ones using semantic and temporal similarity metrics, and (iii) re-adjust the weights of the target\r\nqueries. It is evaluated on the task of estimating influenza-like illness (ILI) rates. We learn a source model for the United States, and\r\nsubsequently transfer it to three other countries, namely France,\r\nSpain and Australia. Overall, the transferred (unsupervised) models\r\nachieve strong performance in terms of Pearson correlation with\r\nthe ground truth (> .92 on average), and their mean absolute error\r\ndoes not deviate greatly from a fully supervised baseline."^^<http://www.w3.org/2001/XMLSchema#string> .
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