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Development of a Decision Support System for Predicting the Evolution of Epidemics Using Open-Source Software Tools.

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  • 1Department of Nursing, National and Kapodistrian University of Athens, Athens, Greece.

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This study developed a COVID-19 forecasting system for Greece using machine learning. The ARIMA model demonstrated the highest accuracy in predicting epidemic trends, offering reliable insights.

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Area of Science:

  • Computational epidemiology
  • Data science
  • Public health informatics

Background:

  • Accurate epidemic trend prediction is crucial for public health response.
  • Machine learning offers powerful tools for analyzing complex epidemiological data.
  • Open-source software facilitates accessible development of predictive systems.

Purpose of the Study:

  • To develop and evaluate a Decision Support System (DSS) for predicting COVID-19 epidemic trends in Greece.
  • To compare the performance of five distinct machine learning algorithms for epidemic forecasting.
  • To identify the most accurate forecasting model for informing public health strategies.

Main Methods:

  • Utilized Open-Source software and machine learning algorithms for trend prediction.
  • Employed COVID-19 data from OurWorldData.org (early 2020–December 2022).
  • Assessed Linear Regression, Back Propagation (BP), Long Short-Term Memory (LSTM), ARIMA, and Prophet models.
  • Evaluated model accuracy using correlation, Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE).

Main Results:

  • The Autoregressive Integrated Moving Average (ARIMA) model was identified as the most effective forecasting algorithm.
  • Comparative analysis demonstrated the ARIMA model's superior predictive reliability for Greek COVID-19 trends.
  • The developed DSS showed significant potential for accurate epidemic forecasting.

Conclusions:

  • The ARIMA model provides a reliable approach for predicting COVID-19 trends in Greece.
  • The Decision Support System offers valuable insights for public health decision-making.
  • Findings have implications for the broader application of machine learning in epidemic forecasting.