An Epidemic Avian Influenza Prediction Model Based on Google Trends

Yi      Lu; Shuo      Wang; Jianying      Wang; Guangya       Zhou; Qiang      Zhang; Xiang      Zhou; Bing      Niu; Qin      Chen; Kuo-Chen      Chou

doi:10.2174/1570178615666180724103325

Abstract

The occurrence of epidemic avian influenza (EAI) not only hinders the development of a country's agricultural economy, but also seriously affects human beings’ life. Recently, the information collected from Google Trends has been increasingly used to predict various epidemics. In this study, using the relevant keywords in Google Trends as well as the multiple linear regression approach, a model was developed to predict the occurrence of epidemic avian influenza. It was demonstrated by rigorous cross-validations that the success rates achieved by the new model were quite high, indicating the predictor will become a very useful tool for hospitals and health providers.

Keywords: Google trends, H5N2, avian influenza, prediction model, multiple line regression model, Chou’s intuitive metrics.

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DOI https://dx.doi.org/10.2174/1570178615666180724103325	Print ISSN 1570-1786
Publisher Name Bentham Science Publisher	Online ISSN 1875-6255

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An Epidemic Avian Influenza Prediction Model Based on Google Trends

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