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      Modeling Zika Virus Spread in Colombia Using Google Search Queries and Logistic Power Models

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      bioRxiv

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          Abstract

          Public health agencies generally have a small window to respond to burgeoning disease outbreaks in order to mitigate the potential impact. There has been significant interest in developing forecasting models that can predict how and where a disease will spread. However, since clinical surveillance systems typically publish data with a lag of two or more weeks, there is a need for complimentary data streams that can close this gap. We examined the usefulness of Google Trends search data for analyzing the 2016 Zika epidemic in Colombia and evaluating their ability to predict its spread. We calculated the correlation and the time delay between the reported case data and the Google Trends data using variations of the logistic growth model, and showed that the data sets were systematically offset from each other, implying a lead time in the Google Trends data. Our study showed how Internet data can potentially complement clinical surveillance data and may be used as an effective early detection tool for disease outbreaks.

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          Author and article information

          Contributors
          (View ORCID Profile)
          (View ORCID Profile)
          Journal
          bioRxiv
          July 09 2018
          Article
          10.1101/365155
          15a31bad-7f52-4579-8aca-6b89b93a5bf4
          © 2018
          History

          Evolutionary Biology,Medicine
          Evolutionary Biology, Medicine

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