| Summary: | 碩士 === 國立臺灣科技大學 === 電機工程系 === 105 === Accurate tracking the outbreak of an infectious disease, like influenza, helps Public Health to make timely and significant decisions that could calm the fear of people and save lives. A traditional disease caring system based on confirmed cases reports an outbreak typically with at least one-week lag. Therefore, some surveillance systems by monitoring indirect signals about influenza have been proposed to provide a faster unearthing. The volume of those signals is huge and could be pick out from social networks or searching databases. Yahoo and Google, the top two internet search providers who own those Big Data had fired researches about disease tracking ever. In this study, we first draw out the huge influenza signals from CDC (Central Disease Control, Taiwan) database, Google Trends database and King Net database. Then, the linear and nonlinear analyses between three databases are investigated. We found a high correlation existed between series drawn from three databases in years (2011-2016) under survey regardless of linear or nonlinear analysis. Furthermore, we proposed a nonlinear tracking model to capture changes in this epidemic trend, and we can detect the outbreak of influenza more early in years with heavy infectious. These results prove that the signals exposed on networks can provide rich material to trend events of human society.
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