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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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SpatialWavePredict: a tutorial-based primer and toolbox for forecasting growth trajectories using the ensemble

Gerardo Chowell1,2, Amna Tariq3, Sushma Dahal4

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

BMC Medical Research Methodology
|June 7, 2024
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Summary
This summary is machine-generated.

A new toolbox, SpatialWavePredict, offers accessible forecasts for infectious disease dynamics using a spatial wave sub-epidemic model. This user-friendly tool aids in understanding disease spread and informing public health strategies.

Keywords:
Complex epidemic patternsDynamic growth modelEnsemble modelMATLAB toolboxReal-time forecastingSpatial wave sub-epidemic wave model

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Area of Science:

  • Epidemiology
  • Mathematical Biology
  • Computational Science

Background:

  • Dynamical mathematical models are complex for non-experts.
  • Spatial wave sub-epidemic models offer superior short-term forecasting for infectious diseases.
  • Existing models struggle to capture diverse wave dynamics.

Purpose of the Study:

  • Introduce SpatialWavePredict, a user-friendly MATLAB toolbox.
  • Enable characterization and forecasting of spatial wave sub-epidemic models.
  • Provide accessible tools for scientists, policymakers, and students.

Main Methods:

  • Utilizes an ensemble spatial wave sub-epidemic model based on ordinary differential equations.
  • Aggregates multiple asynchronous growth processes and overlapping sub-epidemics.
  • Employs parametric bootstrapping for uncertainty quantification and prediction intervals.

Main Results:

  • The toolbox forecasts time-series trajectories with rich epidemic wave dynamics.
  • An ensemble strategy enhances forecasting performance.
  • Functions assess forecasting performance, estimation, error structures, and horizons.

Conclusions:

  • Developed the first comprehensive toolbox for spatial wave sub-epidemic modeling.
  • Facilitates policymakers in guiding containment strategies and assessing interventions.
  • Demonstrates functionality with COVID-19 data and includes a tutorial video.