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Related Experiment Videos

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

Amril Nazir1, Mohammad Shorfuzzaman2, Muhammad Lujaini Lotfi3

  • 1Department of Information Systems and Technology Management, Zayed University, Abu Dhabi, United Arab Emirates.

Plos One
|June 29, 2026
PubMed
Summary

Related Concept Videos

Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:

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This summary is machine-generated.

This study introduces a deep learning model, Neural Basis Expansion Analysis for Interpretable Time Series (N-BEATS), for accurate COVID-19 case forecasting. N-BEATS, enhanced with mobility data, outperforms existing models in predicting pandemic trends.

Area of Science:

  • Epidemiology
  • Computational Biology
  • Machine Learning

Background:

  • COVID-19 transmission is linked to human mobility.
  • Accurate forecasting of COVID-19 cases is crucial for public health interventions.
  • Existing models may have limitations in handling complex time-series data and long-term predictions.

Purpose of the Study:

  • To propose and evaluate a novel deep learning approach, N-BEATS, for forecasting COVID-19 cases.
  • To assess the impact of incorporating population mobility data on forecasting accuracy and interpretability.
  • To compare the performance of N-BEATS against a state-of-the-art benchmark model (LSTM-Markov).

Main Methods:

  • Utilized the N-BEATS deep learning architecture for time-series forecasting.
  • Incorporated Google and Apple mobility data as covariates.

Related Experiment Videos

  • Compared N-BEATS with LSTM-Markov using COVID-19 datasets from the US, UK, Russia, and Brazil.
  • Evaluated model performance using Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE).

    • Main Results:

    • N-BEATS consistently outperformed LSTM-Markov across all tested datasets and countries.
    • The N-BEATS model achieved lower RMSE and MAPE values compared to the benchmark.
    • Incorporating mobility data as covariates significantly improved the accuracy and interpretability of the N-BEATS model.
    • Mobility data were shown to provide substantial value for forecasting new COVID-19 cases.

    Conclusions:

    • The N-BEATS architecture is effective in capturing pandemic dynamics for COVID-19 case forecasting.
    • Population mobility data are valuable covariates for enhancing the accuracy of epidemiological forecasts.
    • The findings offer insights for policymakers and public health officials in managing infectious disease outbreaks.