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model-metadata/ECDC-norrsken_blue.yml

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+team_name: European Centre for Disease Prevention and Control
+model_name: Norrsken Piecewise Linear Bayesian Model
+team_abbr: ECDC
+model_abbr: norrsken_blue
+model_contributors:
+  - name: Rene Niehus
+    affiliation: European Centre for Disease Prevention and Control
+    email: [email protected]
+team_model_designation: primary
+methods: "A Bayesian piece-wise square-root-linear model fit."
+methods_long: "Model consists of 1) scaling of the data (convert to square-root-scale), 2) assigning data points into groups such that neighbouring data points belong to the same group, and simultaneously in Stan 3) fitting piece-wise intercept-slope models in each group, 4) regularizing the difference between neighbouring slopes to be close to 0, 5) generating a future slope based on the last slope plus fitted noise."

model-metadata/ECDC-norrsken_green.yml

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+team_name: European Centre for Disease Prevention and Control
+model_name: Norrsken Piecewise Linear Bayesian Model
+team_abbr: ECDC
+model_abbr: norrsken_green
+model_contributors:
+  - name: Rene Niehus
+    affiliation: European Centre for Disease Prevention and Control
+    email: [email protected]
+team_model_designation: primary
+methods: "A Bayesian piece-wise log-linear model fit."
+methods_long: "Model consists of 1) scaling of the data (convert to log-scale), 2) assigning data points into groups such that neighbouring data points belong to the same group, and simultaneously in Stan 3) fitting piece-wise intercept-slope models in each group, 4) regularizing the difference between neighbouring slopes to be close to 0, 5) generating a future slope based on the last slope plus fitted noise."