Forecasting COVID-19 new cases using NBEATS deep learning and mobility data

Author First name, Last name, Institution

• Amril Nazir, Zayed University, Abu Dhabi Campus
• Mohammad Shorfuzzaman, Alfaisal University
• Muhammad Lujaini Lotfi
• Firuz Kamalov, Canadian University Dubai
• Sufian Badawi, Al-Balqa Applied University
• Maen Takruri, American University of the Middle East
• Abdul Halim Jallad, United Arab Emirates University

Document Type

Article

Source of Publication

Plos One

Publication Date

6-29-2026

Abstract

COVID-19 is a highly contagious disease transmitted primarily through human contact. Therefore, understanding population mobility is essential for predicting COVID-19 case trends. In this paper, we propose a novel deep learning approach for forecasting new COVID-19 cases using a neural architecture called Neural Basis Expansion Analysis for Interpretable Time Series (N-BEATS). The N-BEATS model effectively handles long input sequences and large output horizons without information loss or increased computational complexity. We compare the performance of N-BEATS with a state-of-the-art benchmark model, LSTM-Markov, across four major countries: the United States, the United Kingdom, Russia, and Brazil. Three distinct COVID-19 datasets from Google, Apple, and Our World in Data (OWID) were used in this study. Incorporating Google and Apple mobility data as covariates enhances both the accuracy and interpretability of the N-BEATS model. Our results show that N-BEATS consistently outperforms LSTM-Markov across all datasets and countries, consistently yielding lower Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). Furthermore, the N-BEATS model with covariates outperforms its counterpart without covariates, indicating that mobility data provide substantial value for forecasting new COVID-19 cases. Overall, this study demonstrates the effectiveness of the N-BEATS architecture in capturing pandemic dynamics and offers valuable insights for policymakers and public health officials in managing future outbreaks.

DOI Link

10.1371/journal.pone.0350264

ISSN

1932-6203

Publisher

Public Library of Science (PLoS)

Volume

21

Issue

6

First Page

e0350264

Disciplines

Computer Sciences | Medicine and Health Sciences

Keywords

Interpretability (0.94) | Benchmark (surveying) (0.72) | Deep learning (0.71) | Computer science (0.7) | Artificial intelligence (0.65) | Machine learning (0.64) | Mean squared error (0.59) | Population (0.48) | Time series (0.44) | Artificial neural network (0.44) | Data mining (0.43) | Covariate (0.42) | Mean absolute error (0.33) | Value (mathematics) (0.32) | Mean absolute percentage error (0.32) | Backpropagation (0.3) | Big data (0.29) | Statistics (0.29) | Data modeling (0.28) | Recurrent neural network (0.27) | Elastic net regularization (0.26) | Domain (mathematical analysis) (0.26) | Pandemic (0.26) | Baseline (sea) (0.26) | Deep neural networks (0.25) | Predictive modelling (0.25)

Scopus ID

105044553846

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Recommended Citation

Nazir, Amril; Shorfuzzaman, Mohammad; Lotfi, Muhammad Lujaini; Kamalov, Firuz; Badawi, Sufian; Takruri, Maen; and Jallad, Abdul Halim, "Forecasting COVID-19 new cases using NBEATS deep learning and mobility data" (2026). All Works. 8250.
https://zuscholars.zu.ac.ae/works/8250

Indexed in Scopus

yes

Open Access

yes

Open Access Type

Gold: This publication is openly available in an open access journal/series