Developing mortality surveillance systems using Google trend: A pilot study

Fu-Chun Yeh and Chien-Hung Yeh

Physica A: Statistical Mechanics and its Applications, 2019, vol. 527, issue C

Abstract: In this paper, the mortality model for the developed country, which the United State possesses the largest economy in the world and thus serves as an ideal representation, is investigated. Early surveillance of the causes of death is critical which can allow the preparation of preventive steps against critical disease such as dengue fever. Studies reported that some search queries, especially those diseases related terms on Google Trends are essential. To this end, we include either main cause of death or the extended or the more general terminologies from Google Trends to decode the mortality related terms using the Wiener Cascade Model. Using time series and Wavelet scalogram of search terms, the patterns of search queries are categorized into different levels of periodicity. The results include (1) the decoding trend, (2) the features importance, and (3) the accuracy of the decoding patterns. Three scenarios regard predictors include the use of (1) all 19 features, (2) the top ten most periodic predictors, or (3) the ten predictors with the highest weighting. All search queries spans from December 2013–December 2018. The results show that search terms with both higher weight and annual periodic pattern contribute more in forecasting the word “die”; however, only predictors with higher weight are valuable to forecast the word “death”.

Keywords: Google trends; Wiener Cascade Model; Cause of death; Mortality surveillance; Big data; Decode (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:527:y:2019:i:c:s0378437119306818

DOI: 10.1016/j.physa.2019.121125

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