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Journal article
Comparative study of a mathematical epidemic model, statistical modeling, and deep learning for COVID-19 forecasting and management
Socio-economic planning sciences, Vol.80, 101249
03/2022
PMCID: PMC8800166
PMID: 35125526
Web of Science ID: WOS:000776213900002
Abstract
The COVID-19 pandemic has caused a global crisis with 47,209,305 confirmed cases and 1,209,505 confirmed deaths worldwide as of November 2, 2020. Forecasting confirmed cases and understanding the virus dynamics is necessary to provide valuable insights into the growth of the outbreak and facilitate policy-making regarding virus containment and utilization of medical resources. In this study, we applied a mathematical epidemic model (MEM), statistical model, and recurrent neural network (RNN) variants to forecast the cumulative confirmed cases. We proposed a reproducible framework for RNN variants that addressed the stochastic nature of RNN variants leveraging z-score outlier detection. We incorporated heterogeneity in susceptibility into the MEM considering lockdowns and the dynamic dependency of the transmission and identification rates which were estimated using Poisson likelihood fitting. While the experimental results demonstrated the superiority of RNN variants in forecasting accuracy, the MEM presented comprehensive insights into the virus spread and potential control strategies.
Details
- Title
- Comparative study of a mathematical epidemic model, statistical modeling, and deep learning for COVID-19 forecasting and management
- Publication Details
- Socio-economic planning sciences, Vol.80, 101249
- Resource Type
- Journal article
- Publisher
- Elsevier
- Copyright
- © 2022 Elsevier Ltd. All rights reserved.
- Identifiers
- WOS:000776213900002; 99381798267506600
- Academic Unit
- Center for Cybersecurity and AI; Hal Marcus College of Science and Engineering
- Language
- English