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

TL;DR

This paper introduces a deep learning model using Bidirectional LSTM and clustering to improve COVID-19 case forecasts across countries, incorporating demographic, socioeconomic, and lockdown data for better accuracy.

Contribution

The study presents a novel combination of clustering and Bidirectional LSTM for multivariate time series forecasting of COVID-19 cases, outperforming existing methods.

Findings

The approach accurately predicts COVID-19 cases in Qatar.

Inclusion of lockdown and socioeconomic data enhances forecast accuracy.

The method outperforms state-of-the-art forecasting techniques.

Abstract

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.