Author: Sangeeta Bhatia; Britta Lassmann; Emily Cohn; Malwina Carrion; Moritz U.G. Kraemer; Mark Herringer; John Brownstein; Larry Madoff; Anne Cori; Pierre Nouvellet
Title: Using Digital Surveillance Tools for Near Real-Time Mapping of the Risk of International Infectious Disease Spread: Ebola as a Case Study Document date: 2019_11_15
ID: jwesa12u_17
Snippet: The model performance varied depending on the country and the phase of the epidemic, defined as "growing", "declining", and "stable" depending on R t estimates (see Methods). In general, the model performance was best in the stable phase with 66.7% of the observations contained in the 95% forecasts interval (versus 40.2% and 30.8% in the declining and growing phases respectively, SI Table 1 ). However the forecast uncertainty was largest in the s.....
Document: The model performance varied depending on the country and the phase of the epidemic, defined as "growing", "declining", and "stable" depending on R t estimates (see Methods). In general, the model performance was best in the stable phase with 66.7% of the observations contained in the 95% forecasts interval (versus 40.2% and 30.8% in the declining and growing phases respectively, SI Table 1 ). However the forecast uncertainty was largest in the stable phase and smallest in the growing phase ( Fig 3B) . Importantly, in the growing phase the model tended to over-predict while under-predicting in the declining phase ( Fig 3A) .
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