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.
As reported in the [ACM Special Interest Group in Computers and Society (SIGCAS) 2020 Computers and Sustainable Societies (COMPASS)], [IEEE Technology and Engineering Management Society (TEMS) 2020 International Conference on Artificial Intelligence for Good (AI4G)], and [IEEE Global Humanitarian Technology Conference (GHTC) 2020] Long Short-Term Memory (LSTM) neural networks are utilized by Santa Clara University’s EPIC (Ethical, Pragmatic, and Intelligent Computing) and BioInnovation & Design laboratories for continued research and development of an eVision (Epidemic Vision) machine learning tool to predict the trend of influenza cases throughout the flu season.
There we reported eVision’s success in making 3, 7, and 14 weeks in advance predictions for the 2018–2019 United States flu season with 88.11%, 88%, and 74.18% accuracy respectively and delineated future steps of expanding eVision’s granularity by 1) adding state level predictions in order to enhance national predictions and 2) utilizing metropolitan area keyword trends to improve both state level and national predictions. This resulted in the improvement of the model’s accuracy to 90.38%, 91.43%, and 81.74% for 3, 7, and 14 weeks in advance predictions respectively. This paper is to report on the methodology of obtaining these improved results.
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Acknowledgment
Many thanks are due to Ben Dorty, the New-Technology Senior Director at Cepheid Inc. for inspiring the project, and supporting it throughout development. Also to Prashanth Asuri, director of SCU’s BioInnovation and Design Lab for obtaining financial support from Cepheid Inc. And to the School of Engineering’s Frugal Innovation Hub as well as the Departments of Bio Engineering, Computer Science & Engineering, and Mathematics & Computer Science for their continued support of the eVision project. And lastly to other eVision team members: Yash Kamdar and Ron Huang, as well as past eVision team members: Yuhang Qian, Anika Shahi, Liying Liang, and Meghan McGinnis for their hard work on research, data collection, and software development in the earlier stages of the project.
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Shaghaghi, N., Calle, A., Kouretas, G., Karishetti, S., Wagh, T. (2021). Expanding eVision’s Granularity of Influenza Forecasting. In: Ye, J., O'Grady, M.J., Civitarese, G., Yordanova, K. (eds) Wireless Mobile Communication and Healthcare. MobiHealth 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 362. Springer, Cham. https://doi.org/10.1007/978-3-030-70569-5_14
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