Introduction

This dashboard is part of the epiENGAGE project, a CDC-funded collaboration involving over 20 research labs and public health departments dedicated to developing science-based tools and training programs that help local agencies detect, forecast, and respond to outbreaks more effectively.

This particular dashboard is designed for the Flu Metrocast hub, a national platform enabling collaborative, city-level forecasting of influenza-related emergency department visits and hospital admissions.

View The Latest Forecasts

Current Metro Areas

  • Texas Metro Areas
    • Target: Percentage of ED visits due to flu
    • Data Source : CDC’s Emergency Department Visits for Viral Respiratory Illness
    • Target areas: These metro areas include both the named city and surrounding counties:
      • Austin: Bastrop, Burnet, Lee, Llano, Travis, Williamson
      • Houston: Austin, Chambers, Fort Bend, Harris, Liberty, Montgomery, San Jacinto, Waller
      • Dallas: Collin, Dallas, Ellis, Hopkins, Hunt, Kaufman, Rains, Rockwall
      • El Paso: Culberson, El Paso, Hudspeth, Loving
      • San Antonio: Atascosa, Bandera, Bexar, Frio, Gonzales, Guadalupe, Kendall, La Salle, McMullen, Medina, Wilson, Zavala

Models Included

  • epiENGAGE-GBQR : A machine learning method that combines gradient boosting and quantile regression to predict conditional quantiles
  • epiENGAGE-INFLAenza : A spatial time-series model that uses the R-INLA package for estimating forecast posterior distributions.
  • epiENGAGE-Copycat : A pattern matching model that matches growth rate trends to historic growth rate curves.
  • Epiforecasts-dyngam : A Bayesian hierarchical GAM with a univariate AR(1) process
  • epiENGAGE-baseline : simple time series model as a reference model
  • epiENGAGE-ensemble_mean : An equally weighted mean ensemble that takes the mean at each quantile level of all eligible forecasts
  • epiENGAGE-log_norm : Linear pool (a.k.a. distributional mixture) ensemble of quantile forecast submissions.