Research Article
Expanding eVision’s Granularity of Influenza Forecasting
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@INPROCEEDINGS{10.1007/978-3-030-70569-5_14, author={Navid Shaghaghi and Andres Calle and George Kouretas and Supriya Karishetti and Tanmay Wagh}, title={Expanding eVision’s Granularity of Influenza Forecasting}, proceedings={Wireless Mobile Communication and Healthcare. 9th EAI International Conference, MobiHealth 2020, Virtual Event, November 19, 2020, Proceedings}, proceedings_a={MOBIHEALTH}, year={2021}, month={7}, keywords={Flu trend prediction Google Trends Health care technology Influenza incident rate forecasting Long Short-Term Memory (LSTM) neural networks Medical machine learning}, doi={10.1007/978-3-030-70569-5_14} }- Navid Shaghaghi
Andres Calle
George Kouretas
Supriya Karishetti
Tanmay Wagh
Year: 2021
Expanding eVision’s Granularity of Influenza Forecasting
MOBIHEALTH
Springer
DOI: 10.1007/978-3-030-70569-5_14
Abstract
According to the United States’ Center for Disease Control and Prevention (CDC) between 39 and 56 million people in the US suffered from Influenza Like Illnesses (ILI) in the 2019-20 flue season. From which, 410 to 740 thousand were hospitalized and 24 to 62 thousand succumbed to the disease. Therefore, the existence of an early warning mechanism that can alert pharmaceuticals, healthcare providers, and governments to the trends of the influenza season well in advance, would serve as a significant step in helping combat this communicable disease and reduce mortality from it.
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