# Forecasting mortality using Google trend

## TL;DR

This study explores using Google Trends data and Wiener Cascade Models to forecast mortality causes in the US, highlighting the importance of search query periodicity and feature importance for accurate predictions.

## Contribution

It introduces a novel approach combining Google Trends and wavelet analysis with Wiener Cascade Models for mortality forecasting.

## Key findings

- Search terms with higher weight and annual periodicity improve forecasting accuracy.
- Predictors with higher weight are more valuable than just periodicity for mortality prediction.
- Using selected features enhances the decoding of mortality-related search patterns.

## Abstract

In this paper, the motility model for the developed country, which 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 the decoding trend, the features importance, and the accuracy of the decoding patterns. Three scenarios regard predictors include the use of all 19 features, the top ten most periodic predictors, or the ten predictors with 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.

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Source: https://tomesphere.com/paper/1901.09692