A Novel Matrix Profile-Guided Attention LSTM Model for Forecasting COVID-19 Cases in USA
Background: The outbreak of the novel coronavirus disease 2019 (COVID-19) has been raging around the world for more than 1 year. Analysis of previous COVID-19 data is useful to explore its epidemic patterns. Utilizing data mining and machine learning methods for COVID-19 forecasting might provide a...
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doaj-1473432066e1416984b2ccc1183d0a242021-10-07T06:35:21ZengFrontiers Media S.A.Frontiers in Public Health2296-25652021-10-01910.3389/fpubh.2021.741030741030A Novel Matrix Profile-Guided Attention LSTM Model for Forecasting COVID-19 Cases in USAQian Liu0Qian Liu1Qian Liu2Daryl L. X. Fung3Leann Lac4Pingzhao Hu5Pingzhao Hu6Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, MB, CanadaDepartment of Computer Science, University of Manitoba, Winnipeg, MB, CanadaDepartment of Statistics, University of Manitoba, Winnipeg, MB, CanadaDepartment of Computer Science, University of Manitoba, Winnipeg, MB, CanadaDepartment of Statistics, University of Manitoba, Winnipeg, MB, CanadaDepartment of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, MB, CanadaDepartment of Computer Science, University of Manitoba, Winnipeg, MB, CanadaBackground: The outbreak of the novel coronavirus disease 2019 (COVID-19) has been raging around the world for more than 1 year. Analysis of previous COVID-19 data is useful to explore its epidemic patterns. Utilizing data mining and machine learning methods for COVID-19 forecasting might provide a better insight into the trends of COVID-19 cases. This study aims to model the COVID-19 cases and perform forecasting of three important indicators of COVID-19 in the United States of America (USA), which are the adjusted percentage of daily admitted hospitalized COVID-19 cases (hospital admission), the number of daily confirmed COVID-19 cases (confirmed cases), and the number of daily death cases caused by COVID-19 (death cases).Materials and Methods: The actual COVID-19 data from March 1, 2020 to August 5, 2021 were obtained from Carnegie Mellon University Delphi Research Group. A novel forecasting algorithm was proposed to model and predict the three indicators. This algorithm is a hybrid of an unsupervised time series anomaly detection technique called matrix profile and an attention-based long short-term memory (LSTM) model. Several classic statistical models and the baseline recurrent neural network (RNN) models were used as the baseline models. All models were evaluated using a repeated holdout training and test strategy.Results: The proposed matrix profile-assisted attention-based LSTM model performed the best among all the compared models, which has the root mean square error (RMSE) = 1.23, 31612.81, 467.17, mean absolute error (MAE) = 0.95, 26259.55, 364.02, and mean absolute percentage error (MAPE) = 0.25, 1.06, 0.55, for hospital admission, confirmed cases, and death cases, respectively.Conclusion: The proposed model is more powerful in forecasting COVID-19 cases. It can potentially aid policymakers in making prevention plans and guide health care managers to allocate health care resources reasonably.https://www.frontiersin.org/articles/10.3389/fpubh.2021.741030/fullCOVID-19 forecastingLSTM modelsmatrix profileattention mechanismepidemiological indicators |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Qian Liu Qian Liu Qian Liu Daryl L. X. Fung Leann Lac Pingzhao Hu Pingzhao Hu |
spellingShingle |
Qian Liu Qian Liu Qian Liu Daryl L. X. Fung Leann Lac Pingzhao Hu Pingzhao Hu A Novel Matrix Profile-Guided Attention LSTM Model for Forecasting COVID-19 Cases in USA Frontiers in Public Health COVID-19 forecasting LSTM models matrix profile attention mechanism epidemiological indicators |
author_facet |
Qian Liu Qian Liu Qian Liu Daryl L. X. Fung Leann Lac Pingzhao Hu Pingzhao Hu |
author_sort |
Qian Liu |
title |
A Novel Matrix Profile-Guided Attention LSTM Model for Forecasting COVID-19 Cases in USA |
title_short |
A Novel Matrix Profile-Guided Attention LSTM Model for Forecasting COVID-19 Cases in USA |
title_full |
A Novel Matrix Profile-Guided Attention LSTM Model for Forecasting COVID-19 Cases in USA |
title_fullStr |
A Novel Matrix Profile-Guided Attention LSTM Model for Forecasting COVID-19 Cases in USA |
title_full_unstemmed |
A Novel Matrix Profile-Guided Attention LSTM Model for Forecasting COVID-19 Cases in USA |
title_sort |
novel matrix profile-guided attention lstm model for forecasting covid-19 cases in usa |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Public Health |
issn |
2296-2565 |
publishDate |
2021-10-01 |
description |
Background: The outbreak of the novel coronavirus disease 2019 (COVID-19) has been raging around the world for more than 1 year. Analysis of previous COVID-19 data is useful to explore its epidemic patterns. Utilizing data mining and machine learning methods for COVID-19 forecasting might provide a better insight into the trends of COVID-19 cases. This study aims to model the COVID-19 cases and perform forecasting of three important indicators of COVID-19 in the United States of America (USA), which are the adjusted percentage of daily admitted hospitalized COVID-19 cases (hospital admission), the number of daily confirmed COVID-19 cases (confirmed cases), and the number of daily death cases caused by COVID-19 (death cases).Materials and Methods: The actual COVID-19 data from March 1, 2020 to August 5, 2021 were obtained from Carnegie Mellon University Delphi Research Group. A novel forecasting algorithm was proposed to model and predict the three indicators. This algorithm is a hybrid of an unsupervised time series anomaly detection technique called matrix profile and an attention-based long short-term memory (LSTM) model. Several classic statistical models and the baseline recurrent neural network (RNN) models were used as the baseline models. All models were evaluated using a repeated holdout training and test strategy.Results: The proposed matrix profile-assisted attention-based LSTM model performed the best among all the compared models, which has the root mean square error (RMSE) = 1.23, 31612.81, 467.17, mean absolute error (MAE) = 0.95, 26259.55, 364.02, and mean absolute percentage error (MAPE) = 0.25, 1.06, 0.55, for hospital admission, confirmed cases, and death cases, respectively.Conclusion: The proposed model is more powerful in forecasting COVID-19 cases. It can potentially aid policymakers in making prevention plans and guide health care managers to allocate health care resources reasonably. |
topic |
COVID-19 forecasting LSTM models matrix profile attention mechanism epidemiological indicators |
url |
https://www.frontiersin.org/articles/10.3389/fpubh.2021.741030/full |
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