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Updated: Nov 4, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

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

Ahmed Ben Said¹, Abdelkarim Erradi², Hussein Ahmed Aly²

¹Computer Science and Engineering Department, College of Engineering, Qatar University, 2713, Doha, Qatar. abensaid@qu.edu.qa.

Environmental Science and Pollution Research International

|May 27, 2021

Summary

This summary is machine-generated.

This study introduces a deep learning model for COVID-19 case forecasting. By clustering countries and using a bidirectional Long Short-Term Memory network, it improves prediction accuracy for pandemic management.

Keywords:

Bi-LSTM COVID-19 Clustering Cumulative cases

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Last Updated: Nov 4, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

Area of Science:

Epidemiology
Data Science
Public Health

Background:

Accurate forecasting of COVID-19 spread is crucial for effective policy decisions.
Existing forecasting methods may not fully capture complex influencing factors.

Purpose of the Study:

To develop and validate a novel deep learning approach for forecasting cumulative COVID-19 cases.
To enhance prediction accuracy by incorporating country-specific similarities and lockdown data.

Main Methods:

Utilized a bidirectional Long Short-Term Memory (Bi-LSTM) network for multivariate time series forecasting.
Employed K-means clustering to group countries based on demographic, socioeconomic, and health indicators.
Integrated lockdown measure data with cumulative case data for model training.

Main Results:

The proposed Bi-LSTM model demonstrated superior performance in forecasting COVID-19 cases.
Validation using Qatar's outbreak data from December 2020 showed the model's effectiveness.
Quantitative evaluation confirmed outperformance against state-of-the-art forecasting techniques.

Conclusions:

The integrated deep learning approach offers a more accurate method for COVID-19 propagation forecasting.
Clustering countries and incorporating policy data improves the reliability of epidemiological predictions.
This technique can aid policymakers in making informed decisions to control the pandemic.