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    "name": "Dynamic Harmonic Model with ARIMA errors",
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    "description": "Team name: Protea\r\nTeam members: Craig J. McGowan (contact), Alysse J. Kowalski\r\nData source(s): ilinet, google trends, who-nrevss lab data\r\nMethods: A dynamic harmonic regression model is fit separately for each region in \r\n  sequential steps, with Fourier terms to capture seasonality and multiple\r\n  possible covariates. At each sequential step, cross-validated models are fit \r\n  using each of the seasons from 2010/2011 through 2018/2019 and scored using \r\n  CDC single bin scoring rules. First, the number of Fourier terms (ranging \r\n  from 1 to 12) is selected. The non-seasonal ARIMA structure of the error terms\r\n  is selected second, and potential covariates are tested for inclusion last.\r\n  Covariates include influenza virus subtype 6 week rolling average percentage, \r\n  national Google Trends data, and regional Google Trends data (using the most \r\n  populous state in a given HHS region) along with combinations of those \r\n  covariates. Final CV model scores  were examined and the top performing model\r\n  structure was chosen for each location. Forecasts are simulated from iterating\r\n  one-step-ahead preditions for the remainder of the season, and the occurence \r\n  of seasonal targets is calculated from these predictions. Observed values  for\r\n  the current season in the simulation are adjusted for backfill by sampling\r\n  from a distribution of prior observed outcomes for that lag/week combination.\r\n  Predicted probabilities are calculated from the observed values across\r\n  multiple simulations. For making prospective predictions, models were fit\r\n  using only  data from prior seasons, and forecasts were made using data that\r\n  would have  been available at the time of the forecast. In the case of\r\n  covariates, predicted values of the covariates based on prior data were used\r\n  to generate forecasts.  For Google Trends data, no information on backfill is\r\n  available and the data  are assumed to be free of backfill. For influenza\r\n  subtypes, no information on  backfill is available, so we use cumulative\r\n  percentages of each influenza subtype up to a given week in  the season, which\r\n  is less susceptible to backfill  effects than weekly measures.  All code used\r\n  in estimation and prediction is  available at\r\n  https://github.com/craigjmcgowan/FluForecast",
    "home_url": "https://github.com/FluSightNetwork/cdc-flusight-ensemble/tree/master/model-forecasts/real-time-component-models/Protea_Springbok",
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