期刊
IEEE TRANSACTIONS ON CYBERNETICS
卷 52, 期 11, 页码 11977-11989出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2021.3120967
关键词
Predictive models; COVID-19; Data models; Mathematical models; Uncertainty; Logic gates; Recurrent neural networks; Coronavirus disease 2019 (COVID-19); dynamic behavioral model; hybrid model; long short-term memory (LSTM); spread prediction
类别
资金
- School of Civil and Environmental Engineering, University of New South Wales (UNSW Sydney)
The study introduces a hybrid model combining LSTM neural network and dynamic behavioral models to accurately predict the regional spread of COVID-19, outperforming traditional LSTM models. By considering multiple factors, the model enhances prediction accuracy and replicates system behaviors accurately even under uncertainty.
To accurately predict the regional spread of coronavirus disease 2019 (COVID-19) infection, this study proposes a novel hybrid model, which combines a long short-term memory (LSTM) artificial recurrent neural network with dynamic behavioral models. Several factors and control strategies affect the virus spread, and the uncertainty arising from confounding variables underlying the spread of the COVID-19 infection is substantial. The proposed model considers the effect of multiple factors to enhance the accuracy in predicting the number of cases and deaths across the top ten most-affected countries at the time of the study. The results show that the proposed model closely replicates the test data, such that not only it provides accurate predictions but it also replicates the daily behavior of the system under uncertainty. The hybrid model outperforms the LSTM model while accounting for data limitation. The parameters of the hybrid models are optimized using a genetic algorithm for each country to improve the prediction power while considering regional properties. Since the proposed model can accurately predict the short-term to medium-term daily spreading of the COVID-19 infection, it is capable of being used for policy assessment, planning, and decision making.
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