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 (Frontiers in Public Health, 2021)
Background: The outbreak of the 2019 novel coronavirus disease (COVID-19) has been raging around the world for more than one 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 the COVID-19 in the 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 1st, 2020 to August 5th, 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 the COVID-19 cases. It can potentially aid policymakers in making prevention plans and guide healthcare managers to allocate health care resources reasonably.