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Related Experiment Video

Updated: Jul 12, 2025

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses

Published on: November 10, 2023

A novel bidirectional LSTM deep learning approach for COVID-19 forecasting.

Nway Nway Aung¹, Junxiong Pang^2,3, Matthew Chin Heng Chua⁴

¹Institute of Systems Science, National University of Singapore, 25 Heng Mui Keng Terrace, Singapore, 119615, Singapore. nwaynwayaung.lily@gmail.com.

Scientific Reports

|October 20, 2023

Summary

A deep-learning model accurately forecasts daily COVID-19 cases 14 days in advance using historical data. This Bidirectional Long-Short Term Memory (Bi-LSTM) approach shows promise for pandemic prediction, even with fewer variables.

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Area of Science:

Epidemiology
Data Science
Computational Biology

Background:

COVID-19 caused significant global morbidity and mortality.
Accurate forecasting of COVID-19 cases is crucial for public health response.
Early pandemic stages presented unique challenges for predictive modeling.

Purpose of the Study:

To develop and validate a deep-learning model for forecasting daily COVID-19 cases.
To assess the model's performance in the early stages of the pandemic across 190 countries.
To compare the deep-learning model's accuracy against a classical ARIMA model.

Main Methods:

Utilized a Bidirectional Long-Short Term Memory (Bi-LSTM) deep-learning architecture.
Trained models on daily confirmed cases, reproduction number, policy measures, mobility, and flight data from January 2020 to January 2021.
Forecasted new daily COVID-19 cases 14 days in advance for 190 countries.

Main Results:

The Bi-LSTM models demonstrated comparable accuracy to each other and outperformed the ARIMA model in total absolute percentage error.
Median Mean Absolute Error (MAE) was 157 and 150 for the two Bi-LSTM models, respectively.
Countries with higher case numbers and more infection waves generally had more accurate forecasts.

Conclusions:

A deep-learning approach using Bi-LSTM architecture effectively forecasts COVID-19 cases in the early pandemic.
Open-source data can be leveraged for robust epidemiological predictions.
Model accuracy may be maintained with a reduced set of input variables.