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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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SubEpiPredict: A tutorial-based primer and toolbox for fitting and forecasting growth trajectories using the ensemble

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  • 1Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA.

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Summary

This study introduces SubEpiPredict, a MATLAB toolbox for ensemble n-sub-epidemic modeling. It offers powerful forecasting for complex epidemic dynamics, aiding public health policy and research.

Keywords:
ForecastingMATLABModel evaluationPerformance metricsPhenomenological modelsSub-epidemicsn-Sub-epidemic model

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Area of Science:

  • Epidemiology
  • Computational Biology
  • Data Science

Background:

  • Complex epidemic dynamics, including resurgences and multiple peaks, pose forecasting challenges.
  • Previous work established the power of ensemble n-sub-epidemic modeling for capturing intricate temporal patterns.

Purpose of the Study:

  • To introduce SubEpiPredict, a user-friendly MATLAB toolbox for fitting and forecasting epidemic time series data.
  • To provide a detailed description of the ensemble n-sub-epidemic modeling framework and its application.
  • To demonstrate the toolbox's utility using publicly available COVID-19 death data.

Main Methods:

  • Utilizes an ensemble n-sub-epidemic modeling framework to integrate sub-epidemics.
  • Incorporates model fitting, forecasting, and performance evaluation using metrics like the weighted interval score (WIS).
  • Constructs ensemble forecasts from top-ranking models.

Main Results:

  • The SubEpiPredict toolbox facilitates the characterization of complex epidemic patterns.
  • Demonstrates effective forecasting of time series data, including COVID-19 deaths.
  • Provides a practical tool for users without extensive coding backgrounds.

Conclusions:

  • SubEpiPredict offers a powerful and accessible solution for epidemic modeling and forecasting.
  • The toolbox supports informed decision-making for policymakers and researchers.
  • Ensemble n-sub-epidemic modeling provides robust insights into epidemic temporal dynamics.