Predicting COVID-19 cases using Bidirectional LSTM on multivariate time series
This work addresses the need for accurate disease forecasting to aid policy makers during the COVID-19 pandemic, but it is incremental as it builds on existing deep learning techniques.
The paper tackled forecasting COVID-19 cases by grouping countries with similar demographics and using a Bidirectional LSTM on multivariate time series, resulting in improved accuracy over state-of-the-art methods as validated in Qatar.
Background: To assist policy makers in taking adequate decisions to stop the spread of COVID-19 pandemic, accurate forecasting of the disease propagation is of paramount importance. Materials and Methods: 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 cases data for each clustered countries enriched with data related to the lockdown measures are fed to the Bidirectional LSTM to train the forecasting model. Results: We validate the effectiveness of the proposed approach by studying the disease outbreak in Qatar. Quantitative evaluation, using multiple evaluation metrics, shows that the proposed technique outperforms state-of-art forecasting approaches. Conclusion: Using data of multiple countries in addition to lockdown measures improve accuracy of the forecast of daily cumulative COVID-19 cases.