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Table 1 Characteristics of nine teams that competed in the Predict the 2013–14 Influenza Season Challenge

From: Results from the centers for disease control and prevention’s predict the 2013–2014 Influenza Season Challenge

Team Digital Data source Model type Regional forecasta Brief descriptiond
A Wikipedia mechanisticb Yes Susceptible-Exposed-Infected-Recovered (SEIR) model using data assimilation to probabilistically fit models to ILINet data
B Twitter mechanistic Yes SEIR model initialized with current Twitter and ILINet data
C Google Flu Trends; Twitter statisticalc Yes Utilized method of analogues, Kalman filtering, Poisson regression, and an ensemble method averaging the results of the three models to forecast ILINet
D Google Flu Trends statistical Yes Utilized empirical Bayes model and a spatio-temporal likelihood function
E Google Flu Trends; Twitter statistical Yes Utilized multiplicative time series model
F Google Flu Trends mechanistic Yes Susceptible-infected-recovered-susceptible (SIRS) model initialized with Google Flu Trends data and data assimilation methods
G Twitter statistical No Extrapolation of filtered Twitter data
H Google Flu Trends; HealthMap; Twitter mechanistic Yes Statistical models used to make short term forecasts and agent based models combined with mean field models with non-linear optimization techniques used to output long term forecasts.
I Twitter statistical Yes Utilized time series model and method of analogues
  1. aYes denotes forecast for ≥1 region (for all weeks)
  2. bIncludes models that incorporate compartmental modeling like Susceptible-Exposed-Infected-Recovered [SEIR] models
  3. cIncludes models like time series analysis and generalized linear models
  4. dAdditional information on methodology and results for select teams available in references [3438]