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StatModPredict: A user-friendly R-Shiny interface for fitting and forecasting with statistical models.

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Summary
This summary is machine-generated.

StatModPredict is an R-Shiny dashboard that makes advanced statistical time series forecasting accessible without programming. It empowers students and professionals to forecast epidemic trajectories and analyze time series data effectively.

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Area of Science:

  • Epidemiology
  • Public Health
  • Biostatistics

Background:

  • Statistical time series models are crucial for public health forecasting but require extensive programming knowledge, limiting accessibility.
  • Students, professionals, and policymakers often lack the programming skills to utilize these powerful forecasting tools.

Purpose of the Study:

  • To present StatModPredict, an R-Shiny dashboard designed to provide accessible and intuitive forecasting analysis.
  • To enable users with limited programming experience to conduct robust forecasting using various statistical models.

Main Methods:

  • StatModPredict integrates auto-regressive integrated moving average (ARIMA), generalized linear models (GLM), generalized additive models (GAM), and Meta's Prophet model.
  • The dashboard supports real-time forecasting, retrospective analysis, model fitting, evaluation, visualization, and comparison of results.
  • Users can customize parameters, upload external forecasts for comparison, and analyze time series data with editable figures.

Main Results:

  • The R-Shiny dashboard, StatModPredict, successfully lowers programming barriers for time series forecasting.
  • Demonstration using US annual HIV case data showcases the dashboard's utility for real-world forecasting applications.
  • The tool facilitates exploration and use by diverse user groups, including students and public health professionals.

Conclusions:

  • StatModPredict democratizes access to sophisticated forecasting tools, promoting hands-on learning and broader application across disciplines.
  • The open-source interface supports any field utilizing time series data, enhancing epidemic trajectory forecasting and data analysis.
  • By eliminating technical hurdles, StatModPredict fosters wider adoption and potential user contributions to forecasting methodologies.