Boxplots illustrating the impact of surveillance conditions on forecast accuracy

Boxplots illustrating the impact of surveillance conditions on forecast accuracy

<p>(Each box displays the 25th, 50th (median), and 75th percentiles of the WIS). (A) Forecasts stratified by the direction of the target surveillance trend—“Up”, “Flat”, or “Down”. (B) Forecasts stratified by the magnitude of the target, categorized as “High”, “Medium”, or “Low”.</p>...

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Bibliographic Details
Main Author: Jingjing Tang (419372) (author)
Other Authors: Aaron Rumack (4592206) (author), Bryan Wilder (747661) (author), Roni Rosenfeld (19137) (author)
Published: 2025
Subjects:
Medicine
Neuroscience
Biotechnology
Developmental Biology
Biological Sciences not elsewhere classified
Information Systems not elsewhere classified
tentative surveillance values
public health reporting
improves computational efficiency
given estimation date
generates distributional forecasts
finalized surveillance values
early reporting stages
weekly dengue
time forecasting
situational awareness
seriously degrade
scalable solution
rf ),
like illness
insurance claims
forecasting accuracy
existing methods
data revisions
daily covid
confirmed cases
available revisions
antigen tests
19 data
100x compared
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