Abstract
Search volumes from Google Trends over clear-defined temporal and spatial scales were reported beneficial in predicting influenza or disease outbreak. Recent studies showed Wiener Model shares merits of interpretability, implementation, and adaptation to nonlinear fluctuation in terms of real-time decoding. Previous work reported Google Trends effectively predicts death-related trends for the continent economy, yet whether it applies to the island economy is unclear. To this end, a framework of the mortality-related model for a developed island economy Taiwan was built based on potential death causes from Google Trends, aiming to provide new insights into death-related online search behavior at a population level. Our results showed estimated trends based on the Wiener model significantly correlated to actual trends, outperformed those with multiple linear regression and seasonal autoregressive integrated moving average. Meanwhile, apart from that involved all possible features, two other sets of feature selecting strategies were proposed to optimize pre-trained models, either by weights or waveform periodicity of features, resulting in estimated death-related dynamics along with spectrums of risk factors. In general, high-weight features were beneficial to both “die” and “death”, whereas features that possessed clear periodic patterns contributed more to “death”. Of note, normalization before modeling improved decoding performances.
Highlights
Search volumes from Google Trends over clear-defined temporal and spatial scales were reported beneficial in predicting influenza or disease outbreak
Araz et al showed that the additional use of Google Trends search query data improved the performance of the linear regression models by comparing the root means square errors (RMSEs)[10]
Mavragani et al used search query data from Google Trends, forecasting AIDS prevalence in the United States with the AIDS-related search terms, which supported the conclusion of past findings that Google Trends data are valid and valuable for the analysis and forecasting of human behavior towards health topics[17]
Summary
Search volumes from Google Trends over clear-defined temporal and spatial scales were reported beneficial in predicting influenza or disease outbreak. Previous work reported Google Trends effectively predicts death-related trends for the continent economy, yet whether it applies to the island economy is unclear. To this end, a framework of the mortality-related model for a developed island economy Taiwan was built based on potential death causes from Google Trends, aiming to provide new insights into death-related online search behavior at a population level. Google Trends, unlike the wiki-based data-logs, enables finer spatial segmentations with clearer-defined temporal scales, was successively implemented to predict seasonal influenza and/or dengue fever in several c ountries[10,11,12]. Methods include support vector regression[19], autoregressive-integrated moving average m odel[20], ensemble m ethods[21], phenomenological models[22], and deep learning a lgorithm[23], etc., which may integrate with signal processing technique and/or optimization algorithm, were applied to trend predictions; e.g., Fahad Shabbir Ahmad et al predicted mortality in paralytic ileus patients using electronic health records with a hybrid machine learning framework[24]
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