A Novel Matrix Profile-Guided Attention LSTM Model for Forecasting COVID-19 Cases in USA

A Novel Matrix Profile-Guided Attention LSTM Model for Forecasting COVID-19 Cases in USA

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Background: The outbreak of the novel coronavirus disease kicker pro comp 10 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 sophie allport bee curtains 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.