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A Novel Matrix Profile-Guided Attention LSTM Model for Forecasting COVID-19 Cases in USA.
Liu, Qian; Fung, Daryl L X; Lac, Leann; Hu, Pingzhao.
  • Liu Q; Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, MB, Canada.
  • Fung DLX; Department of Computer Science, University of Manitoba, Winnipeg, MB, Canada.
  • Lac L; Department of Statistics, University of Manitoba, Winnipeg, MB, Canada.
  • Hu P; Department of Computer Science, University of Manitoba, Winnipeg, MB, Canada.
Front Public Health ; 9: 741030, 2021.
Article in English | MEDLINE | ID: covidwho-1485127
ABSTRACT

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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study / Reviews Limits: Humans Country/Region as subject: North America Language: English Journal: Front Public Health Year: 2021 Document Type: Article Affiliation country: Fpubh.2021.741030

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study / Reviews Limits: Humans Country/Region as subject: North America Language: English Journal: Front Public Health Year: 2021 Document Type: Article Affiliation country: Fpubh.2021.741030