From 9b65939b768802764653a15128205d57fef7076b Mon Sep 17 00:00:00 2001
From: Li Shandross <57642277+lshandross@users.noreply.github.com>
Date: Thu, 9 Jan 2025 11:02:50 -0500
Subject: [PATCH] Update UMass-trends_ensemble.yml

---
 model-metadata/UMass-trends_ensemble.yml | 12 +-----------
 1 file changed, 1 insertion(+), 11 deletions(-)

diff --git a/model-metadata/UMass-trends_ensemble.yml b/model-metadata/UMass-trends_ensemble.yml
index 8167fe55..2b123c53 100644
--- a/model-metadata/UMass-trends_ensemble.yml
+++ b/model-metadata/UMass-trends_ensemble.yml
@@ -4,16 +4,6 @@ model_name: "Ensemble of baseline models with trends"
 model_abbr: "trends_ensemble"
 model_version: "1.0"
 model_contributors: [
-  {
-    "name": "Nutcha Wattanachit",
-    "affiliation": "UMass Amherst",
-    "email": "nwattanachit@schoolph.umass.edu"
-  },
-  {
-    "name": "Aaron Gerding",
-    "affiliation": "UMass Amherst",
-    "email": "agerding@umass.edu"
-  },
   {
     "name": "Nick Reich",
     "affiliation": "UMass Amherst",
@@ -34,7 +24,7 @@ website_url: "https://github.com/reichlab/trendsEnsemble"
 license: "CC-BY-4.0"
 citation: "citation"
 team_funding: "funding"
-designated_model: true
+designated_model: false
 methods: "Equally weighted ensemble of simple time-series baseline models."
 data_inputs: "Weekly incident flu hospitalizations"
 methods_long: "Equally weighted ensemble of simple time-series baseline models. Each baseline model calculates first differences of incidence in recent weeks. These differences are sampled and then added to the most recently observed incidence. Variations on this method include (a) including the first differences and the negative of these differences to enforce symmetry, resulting in a flat-line forecast, (b) varying the number of time-units in the past for computing the first differences (3 or 4 weeks) to focus on capturing recent trends, and (c) using the original time-series or a variance-stabilizing transformation of it, e.g. square-root. Additionally, the resulting predictive distributions are truncated so that any predicted samples computed to be less than zero are truncated to be zero."