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  3. Machine Learning And Probabilistic Approaches For Forecasting Covid-19 Transmission And Cases.
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  3. Machine Learning And Probabilistic Approaches For Forecasting Covid-19 Transmission And Cases.

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Machine Learning and Probabilistic Approaches for Forecasting COVID-19 Transmission and Cases.

Md Sakhawat Hossain1,2, Ravi Goyal3, Natasha K Martin3

  • 1Department of Public Health Sciences, Clemson University, Clemson, SC, USA.

Medrxiv : the Preprint Server for Health Sciences
|July 16, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a machine learning framework for forecasting COVID-19 reproductive numbers and case counts. The ensemble model, combining spatial smoothing, significantly improved prediction accuracy compared to existing methods.

Keywords:
COVID-19Effective reproductive numberForecastingInfectious disease modelingMachine learning

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Area of Science:

  • Epidemiology
  • Machine Learning
  • Biostatistics

Background:

  • Accurate forecasting of COVID-19 transmission is crucial for public health.
  • Existing methods like EpiNow2 provide valuable estimates but can be improved.

Purpose of the Study:

  • To develop and evaluate a machine learning framework for predicting the effective reproductive number (Rt) and COVID-19 case counts.
  • To enhance forecasting accuracy and robustness at the county level in South Carolina.

Main Methods:

  • Developed a probabilistic forecasting framework using machine learning models (regression, Random Forest, XGBoost).
  • Integrated initial Rt estimates from EpiNow2 with spatial smoothing.
  • Utilized a probabilistic Poisson model for case count predictions.
  • Employed an ensemble approach combining multiple models.

    • Main Results:

    • The ensemble model consistently outperformed EpiNow2 in forecasting Rt and case counts across 7, 14, and 21-day horizons.
    • Achieved a median percentage agreement (PA) of 94.4% for 7-day Rt forecasts in the first period, compared to 87.0% for EpiNow2.
    • Demonstrated improved stability and performance in case count forecasting.

    Conclusions:

    • Combining spatial smoothing with ensemble machine learning models significantly enhances epidemic forecasting accuracy and robustness.
    • The developed framework offers a more reliable tool for public health decision-making regarding COVID-19.