Merge pull request #224 from micokoch/source

updating alloscore publication

_data/publications.yml

@@ -1,4 +1,33 @@
-# 2024
+#2025  
+
+# New publications  
+
+# 2024  
+
+- title: 'Evaluating infectious disease forecasts with allocation scoring rules'
+  slug: alloscore
+  authors: Gerding A, Reich NG, Rogers B, Ray EL
+  preprint: https://arxiv.org/abs/2312.16201
+  pdf: /pdfs/publications/alloscore.pdf #[update if pdf becomes open access]
+  year: 2024
+  journal: 'Journal of the Royal Statistical Society Series A: Statistics in Society'
+  github: aaronger/utility-eval-papers
+  doi: 10.1093/jrsssa/qnae136
+  keywords: forecasting, covid-19
+  abstract: >
+    Recent years have seen increasing efforts to forecast infectious disease burdens, with a primary 
+    goal being to help public health workers make informed policy decisions. However, there has been 
+    only limited discussion of how predominant forecast evaluation metrics might indicate the success 
+    of policies based in part on those forecasts. We explore one possible tether between forecasts 
+    and policy: the allocation of limited medical resources so as to minimize unmet need. We use 
+    probabilistic forecasts of disease burden in each of several regions to determine optimal resource 
+    allocations, and then we score forecasts according to how much unmet need their associated 
+    allocations would have allowed. We illustrate with forecasts of COVID-19 hospitalizations in the 
+    U.S., and we find that the forecast skill ranking given by this allocation scoring rule can vary 
+    substantially from the ranking given by the weighted interval score. We see this as evidence that 
+    the allocation scoring rule detects forecast value that is missed by traditional accuracy measures 
+    and that the general strategy of designing scoring rules that are directly linked to policy 
+    performance is a promising direction for epidemic forecast evaluation.
 
 - title: 'Beyond forecast leaderboards: Measuring individual model importance based on contribution to ensemble accuracy'
   slug: model-importance
@@ -141,31 +170,6 @@
     
 # 2023  
 
-- title: 'Evaluating infectious disease forecasts with allocation scoring rules'
-  slug: alloscore
-  authors: Gerding A, Reich NG, Rogers B, Ray EL
-  preprint: https://arxiv.org/abs/2312.16201
-  pdf: https://arxiv.org/pdf/2312.16201
-  year: 2023
-  journal: arXiv
-  github: aaronger/utility-eval-papers
-  #doi: 10.48550/arXiv.2312.16201 [update when published]
-  keywords: forecasting, covid-19
-  abstract: >
-    Recent years have seen increasing efforts to forecast infectious disease burdens, with a primary 
-    goal being to help public health workers make informed policy decisions. However, there has only 
-    been limited discussion of how predominant forecast evaluation metrics might indicate the success
-    of policies based in part on those forecasts. We explore one possible tether between forecasts 
-    and policy: the allocation of limited medical resources so as to minimize unmet need. We use 
-    probabilistic forecasts of disease burden in each of several regions to determine optimal resource 
-    allocations, and then we score forecasts according to how much unmet need their associated 
-    allocations would have allowed. We illustrate with forecasts of COVID-19 hospitalizations in the 
-    US, and we find that the forecast skill ranking given by this allocation scoring rule can vary 
-    substantially from the ranking given by the weighted interval score. We see this as evidence that the 
-    allocation scoring rule detects forecast value that is missed by traditional accuracy measures and 
-    that the general strategy of designing scoring rules that are directly linked to policy performance 
-    is a promising direction for epidemic forecast evaluation.
-
 - title: 'Evaluation of the US COVID-19 Scenario Modeling Hub for informing pandemic response under uncertainty'
   slug: covid_scenario_hub_eval
   authors: Howerton E, Contamin L, Mullany LC, Qin M, Reich NG, ... Viboud C, Lessler J