Author: Sarah F. McGough; Michael A. Johansson; Marc Lipsitch; Nicolas A. Menzies
Title: Nowcasting by Bayesian Smoothing: A flexible, generalizable model for real-time epidemic tracking Document date: 2019_6_7
ID: 6kq0ptlg_34
Snippet: Beyond supporting real-time disease tracking by public health officials, NobBS can complement existing disease forecast efforts by providing more accurate nowcasts to forecasting teams in the place of real-time reporting underestimates. For example, teams participating in the Centers for Disease Control and Prevention Epidemic Prediction Initiative (https://predict.cdc.gov) challenges (e.g. FluSight) use initial surveillance data for forecasting .....
Document: Beyond supporting real-time disease tracking by public health officials, NobBS can complement existing disease forecast efforts by providing more accurate nowcasts to forecasting teams in the place of real-time reporting underestimates. For example, teams participating in the Centers for Disease Control and Prevention Epidemic Prediction Initiative (https://predict.cdc.gov) challenges (e.g. FluSight) use initial surveillance data for forecasting because it is the most upto-date data available (22) . NobBS can help account for later revisions to these data and therefore improve prospective estimates as well.
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