Predicting COVID-19 cases using bidirectional LSTM on multivariate time series

Abstract

To assist policymakers in making adequate decisions to stop the spread of the COVID-19 pandemic, accurate forecasting of the disease propagation is of paramount importance. This paper presents a deep learning approach to forecast the cumulative number of COVID-19 cases using bidirectional Long Short-Term Memory (Bi-LSTM) network applied to multivariate time series. Unlike other forecasting techniques, our proposed approach first groups the countries having similar demographic and socioeconomic aspects and health sector indicators using K-means clustering algorithm. The cumulative case data of the clustered countries enriched with data related to the lockdown measures are fed to the bidirectional LSTM to train the forecasting model. We validate the effectiveness of the proposed approach by studying the disease outbreak in Qatar and the proposed model prediction from December 1st until December 31st, 2020. The quantitative evaluation shows that the proposed technique outperforms state-of-art forecasting approaches.

Improving performance of deep learning predictive models for COVID-19 by incorporating environmental parameters

“…Passing univariate inputs into these LSTM variants makes the models prone to overfitting, which in turn deteriorates the model performance on the test dataset. For instance, Said et al, (2021) also observed a reduced performance of Bi-LSTM against Basic LSTM in Qatar, where they looked into multivariate time series data enriched with data related to lockdown measures. While there are several LSTM results available, a direct one-to-one comparison with this study would be fallacious due to the varying locations, model architectures and time period considered.…”

Temporal deep learning architecture for prediction of COVID-19 cases in India

“…It is adopted to learn from the framework providing better understanding from the learning context ( Abdollahi et al, 2021 ). As BiLSTM is a multivariate time series it allows multiple time series dependent which can be designed together to predict the correlations along with the series recorded or captured variables varying simultaneously over time period ( Said et al, 2021 ). BiLSTM is a deep learning models for the sequential prediction without much error ( Shahid et al, 2020 ).…”

Predicting COVID‐19 Cases From Atmospheric Parameters Using Machine Learning Approach

“…A novel support vector regression model was developed to predict the spread, growth rate, and end of the COVID‐19 across different countries (Yadav et al., 2020 ). The bidirectional long‐short term memory was also compared with other state of the art forecasting technique and found to be more effective (Said et al., 2021 ). Three artificial neural network algorithms, Radial Basis‐Function, Fuzzy Cluster‐Means, and Non‐linear Autoregressive‐Network with Exogenous Inputs were used to spatial forecast COVID‐19 cases in Iraq (Yahya et al., 2021 ).…”