eVision: Epidemic Forecasting on COVID-19
Abstract: Vaccination is the primary strategy to prevent COVID-19 illness and hospitalization. However, supplies are scarce and due to the regional mutations of the virus, new vaccines or booster shots will need to be administered potentially regularly. Hence, the prediction of the rate of growth of COVID-19 cases is paramount to ensuring the ample supply of vaccines as well as for local, state, and federal government measures to ensure the availability of hospital beds, supplies, and staff. eVision is an epidemic forecaster aimed at combining Machine Learning (ML) - in the form of a Long Short-Term Memory (LSTM) Recursive Neural Network (RNN) - and search engine statistics, in order to make accurate predictions about the weekly number of cases for highly communicable diseases. By providing eVision with the relative popularity of carefully selected keywords searched via Google along with the number of positive cases reported from the US Centers for Disease Control and Prevention (CDC) and/or the World Health Organization (WHO) the model can make highly accurate predictions about the trend of the outbreak by learning the relationship between the two trends. Thus, in order to predict the trend of the outbreak in a specific region, eVision is provided with a weekly count of the number of COVID-19 cases in a region along with statistics surrounding common symptom search phrases such as “loss of smell” and “loss of taste” that have been searched on Google in that region since the start of the pandemic. eVision has, for instance, been able to achieve an accuracy of %89 for predicting the trend of the COVID-19 outbreak in the United States
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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eVision: Epidemic Forecasting on COVID-19 ; volume:7 ; number:2 ; year:2021 ; pages:839-842 ; extent:4
Current directions in biomedical engineering ; 7, Heft 2 (2021), 839-842 (gesamt 4)
- Creator
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Shaghaghi, Navid
Calle, Andres
Kouretas, George
Mirchandani, Jaidev
Castillo, Michael
- DOI
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10.1515/cdbme-2021-2214
- URN
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urn:nbn:de:101:1-2022101214073592313569
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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15.08.2025, 7:27 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Shaghaghi, Navid
- Calle, Andres
- Kouretas, George
- Mirchandani, Jaidev
- Castillo, Michael
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