A comparison of model forecasting metrics across all four models included within the dashboard, and for the top-ranked, second-ranked, weighted, and unweighted ensemble <i>n</i>-sub-epidemic models.

A comparison of model forecasting metrics across all four models included within the dashboard, and for the top-ranked, second-ranked, weighted, and unweighted ensemble <i>n</i>-sub-epidemic models.

<p>A comparison of model forecasting metrics across all four models included within the dashboard, and for the top-ranked, second-ranked, weighted, and unweighted ensemble <i>n</i>-sub-epidemic models.</p>

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Bibliographic Details
Main Author:	Amanda Bleichrodt (15236509) (author)
Other Authors:	Amelia Phan (22008747) (author), Ruiyan Luo (565244) (author), Alexander Kirpich (816896) (author), Gerardo Chowell (151380) (author)
Published:	2025
Subjects:	Medicine Inorganic Chemistry Biological Sciences not elsewhere classified Information Systems not elsewhere classified eliminating programming barriers retrospective model analysis retrospective forecasting efforts intuitive forecasting applications time series data supports robust real limited programming experience four popular models including students training forecast epidemic trajectories students training model fit epidemic ’ time forecasting forecasting tools forecasting options video tutorial statmodpredict </ statistical models shiny dashboard public health promote hands policymakers seeking policymakers aiming performance alongside meta ’ many disciplines including fitting gam ), foster contributions forecast metrics facilitates exploration detailed demonstration desired output dashboard ’ broaden access
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