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Influenza Forecasting

Authors:

Navid Shaghaghi,

Andres Calle,

George KouretasAuthors Info & Claims

COMPASS '20: Proceedings of the 3rd ACM SIGCAS Conference on Computing and Sustainable Societies

Pages 339 - 341

https://doi.org/10.1145/3378393.3402286

Published: 01 July 2020 Publication History

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Abstract

In the 2018-19 influenza season, between 37.4 and 42.9 million people in the United States experienced flu like symptoms. From that number, 431 to 647 thousand were hospitalized and 36400 to 61200 (most of them children and seniors) succumbed to the disease. Due to the annual mutation of the very many strands of the flu virus, new vaccines must be developed and administered every flu season. Therefore, the prediction of the rate of growth in reported infection cases of each strand of the flu is paramount to ensuring the correct supply of vaccines per strand. A great tool for making future predictions using existing data is Machine learning - specifically Neural Networks. eVision (Epidemic Vision) is a software using Long Short-Term Memory (LSTM) neural networks under research and development by Santa Clara University's EPIC (Ethical, Pragmatic, and Intelligent Computing) and Bioinnovation & Design labs to predict the trend of influenza cases throughout the flu season using data from the CDC, WHO, and Google Trends in order to help pharmaceuticals decide on the ramping up or down of their development of tester kits, vaccines, and medicines weeks in advance.

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Cited By

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Syal DZuluaga JShaghaghi N(2024)eVision: Respiratory Syncytial Virus (RSV) Seasonal Trend Forecasting2024 16th Biomedical Engineering International Conference (BMEiCON)10.1109/BMEiCON64021.2024.10896280(1-6)Online publication date: 21-Nov-2024
https://doi.org/10.1109/BMEiCON64021.2024.10896280
Zuluaga JCastillo MSyal DCalle AShaghaghi N(2022)eVision: Forecasting the spread of Tuberculosis in India with Deep Learning2022 International Conference on Computational Science and Computational Intelligence (CSCI)10.1109/CSCI58124.2022.00296(1663-1668)Online publication date: Dec-2022
https://doi.org/10.1109/CSCI58124.2022.00296
Shaghaghi NKamdar YHuang RCalle AMirchandani JCastillo M(2022)Attempts at Enhancing eVision’s Influenza Forecasting Using Social Media2022 14th Biomedical Engineering International Conference (BMEiCON)10.1109/BMEiCON56653.2022.10012095(1-5)Online publication date: 10-Nov-2022
https://doi.org/10.1109/BMEiCON56653.2022.10012095
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Influenza Forecasting

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COMPASS '20: Proceedings of the 3rd ACM SIGCAS Conference on Computing and Sustainable Societies

June 2020

359 pages

ISBN:9781450371292

DOI:10.1145/3378393

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Published: 01 July 2020

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COMPASS '20: ACM SIGCAS Conference on Computing and Sustainable Societies

June 15 - 17, 2020

Ecuador

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Syal DZuluaga JShaghaghi N(2024)eVision: Respiratory Syncytial Virus (RSV) Seasonal Trend Forecasting2024 16th Biomedical Engineering International Conference (BMEiCON)10.1109/BMEiCON64021.2024.10896280(1-6)Online publication date: 21-Nov-2024
https://doi.org/10.1109/BMEiCON64021.2024.10896280
Zuluaga JCastillo MSyal DCalle AShaghaghi N(2022)eVision: Forecasting the spread of Tuberculosis in India with Deep Learning2022 International Conference on Computational Science and Computational Intelligence (CSCI)10.1109/CSCI58124.2022.00296(1663-1668)Online publication date: Dec-2022
https://doi.org/10.1109/CSCI58124.2022.00296
Shaghaghi NKamdar YHuang RCalle AMirchandani JCastillo M(2022)Attempts at Enhancing eVision’s Influenza Forecasting Using Social Media2022 14th Biomedical Engineering International Conference (BMEiCON)10.1109/BMEiCON56653.2022.10012095(1-5)Online publication date: 10-Nov-2022
https://doi.org/10.1109/BMEiCON56653.2022.10012095
Shaghaghi NKarishetti SMa N(2021)Interplay of Influenza A/B Subtypes and COVID-192021 4th International Conference on Bio-Engineering for Smart Technologies (BioSMART)10.1109/BioSMART54244.2021.9677858(1-5)Online publication date: 8-Dec-2021
https://doi.org/10.1109/BioSMART54244.2021.9677858
Shaghaghi NCalle AKouretas GKarishetti SWagh T(2021)Expanding eVision’s Granularity of Influenza ForecastingWireless Mobile Communication and Healthcare10.1007/978-3-030-70569-5_14(227-243)Online publication date: 21-Feb-2021
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Pan ZNguyen HAbu-gellban HZhang Y(2020)Google Trends Analysis of COVID-19 Pandemic2020 IEEE International Conference on Big Data (Big Data)10.1109/BigData50022.2020.9377852(3438-3446)Online publication date: 10-Dec-2020
https://doi.org/10.1109/BigData50022.2020.9377852
Shaghaghi NCalle AQian Y(2020)eVision: Influenza Forecasting Using CDC, WHO, and Google Trends Data2020 IEEE / ITU International Conference on Artificial Intelligence for Good (AI4G)10.1109/AI4G50087.2020.9311072(38-45)Online publication date: 21-Sep-2020
https://doi.org/10.1109/AI4G50087.2020.9311072

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