Forecasting of hospitalizations for COVID-19: A hybrid intelligence approach for Disease X research

Abstract

Background

The COVID-19 pandemic underscores the necessity for proactive measures against emerging diseases, epitomized by WHO's “Disease X.” Among the myriad of indicators tracking COVID-19 progression, the count of hospitalized patients assumes a pivotal role. This metric facilitates timely responses from government agencies, enabling proactive allocation and management of medical resources.

Objective

In this study, we introduce a novel hybrid intelligent approach, the EMD&LSTM-ARIMA model.

Method: This model integrates three techniques: Empirical Mode Decomposition (EMD) to decompose the data into intrinsic mode functions, Long Short-Term Memory (LSTM) neural network for capturing long-term dependencies and nonlinear relationships, and the Auto-Regressive Integrated Moving Average (ARIMA) model for handling linear trends and time series forecasting. We verify its high predictive power and utility through training and forecasting COVID-19 hospitalizations in the UK, Canada, Italy, and Japan.

Results

Our analysis reveals that all forecasted error rates remain below 10%, with Mean Absolute Percentage Error (MAPE) values obtained for these four countries as 2.30%, 3.33%, 1.63%, and 2.89%, respectively.

Conclusion

Our proposed EMD&LSTM-ARIMA model demonstrates robust forecasting performance, particularly for COVID-19 hospitalization data.