Merge branch 'european-modelling-hubs:main' into main

.github/data-storage/changes_db.json

@@ -1 +1,50 @@
-{}
+{
+    "fjordhest": [
+        {
+            "model": "ensemble",
+            "changes": [
+                "model-output/fjordhest-ensemble/2024-03-27-fjordhest-ensemble.csv"
+            ]
+        }
+    ],
+    "ISI": [
+        {
+            "model": "AriABCaster",
+            "changes": [
+                "model-output/ISI-AriABCaster/2024-03-27-ISI-AriABCaster.csv"
+            ]
+        },
+        {
+            "model": "GenLogModelAri",
+            "changes": [
+                "model-output/ISI-GenLogModelAri/2024-03-27-ISI-GenLogModelAri.csv"
+            ]
+        },
+        {
+            "model": "EpiNowARI",
+            "changes": [
+                "model-output/ISI-EpiNowARI/2024-03-27-ISI-EpiNowARI.csv"
+            ]
+        }
+    ],
+    "ECDC": [
+        {
+            "model": "ARI2MA",
+            "changes": [
+                "model-output/ECDC-ARI2MA/2024-03-27-ECDC-ARI2MA.csv"
+            ]
+        },
+        {
+            "model": "norrsken_blue",
+            "changes": [
+                "model-output/ECDC-norrsken_blue/2024-03-27-ECDC-norrsken_blue.csv"
+            ]
+        },
+        {
+            "model": "norrsken_green",
+            "changes": [
+                "model-output/ECDC-norrsken_green/2024-03-27-ECDC-norrsken_green.csv"
+            ]
+        }
+    ]
+}

.github/data-storage/target_db.json

@@ -1 +1,8 @@
-{}
+{
+    "ARI incidence": {
+        "changes": [
+            "target-data/FluID/snapshots/2024-03-22-ARI_incidence.csv",
+            "target-data/ERVISS/snapshots/2024-03-22-ARI_incidence.csv"
+        ]
+    }
+}

model-metadata/ECDC-ARI2MA.yml

@@ -3,8 +3,9 @@ model_name: ARIMA model with seasonality
 team_abbr: ECDC
 model_abbr: ARI2MA
 model_contributors:
-  - name: Lydia Champezou
+  - name: ECDC Mathematical Modelling Team 
     affiliation: European Centre for Disease Prevention and Control
-    email: lydia.champezou@ecdc.europa.eu
+    email: modelling@ecdc.europa.eu
 team_model_designation: primary
+data_inputs: data provided by RespiCast
 methods: An ARIMA model with seasonality and with sqrt transformed data

model-metadata/ECDC-norrsken_blue.yml

@@ -3,9 +3,10 @@ model_name: Norrsken Piecewise Linear Bayesian Model
 team_abbr: ECDC
 model_abbr: norrsken_blue
 model_contributors:
-  - name: Rene Niehus
+  - name: ECDC Mathematical Modelling Team
     affiliation: European Centre for Disease Prevention and Control
-    email: rene.niehus@ecdc.europa.eu
+    email: modellingh@ecdc.europa.eu
 team_model_designation: primary
+data_inputs: ECDC ERVISS
 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

@@ -3,9 +3,10 @@ model_name: Norrsken Piecewise Linear Bayesian Model
 team_abbr: ECDC
 model_abbr: norrsken_green
 model_contributors:
-  - name: Rene Niehus
+  - name: ECDC Mathematical Modelling Team
     affiliation: European Centre for Disease Prevention and Control
-    email: [email protected]
+    email: [email protected]
+data_inputs: ECDC ERVISS
 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."

supporting-files/forecasting_weeks.csv

@@ -1,10 +1,16 @@
 origin_date,horizon,submission_round,is_latest,target_end_date
-2024-03-20,-1,14,True,2024-03-03
-2024-03-20,0,14,True,2024-03-10
-2024-03-20,1,14,True,2024-03-17
-2024-03-20,2,14,True,2024-03-24
-2024-03-20,3,14,True,2024-03-31
-2024-03-20,4,14,True,2024-04-07
+2024-03-27,-1,15,True,2024-03-10
+2024-03-27,0,15,True,2024-03-17
+2024-03-27,1,15,True,2024-03-24
+2024-03-27,2,15,True,2024-03-31
+2024-03-27,3,15,True,2024-04-07
+2024-03-27,4,15,True,2024-04-14
+2024-03-20,-1,14,False,2024-03-03
+2024-03-20,0,14,False,2024-03-10
+2024-03-20,1,14,False,2024-03-17
+2024-03-20,2,14,False,2024-03-24
+2024-03-20,3,14,False,2024-03-31
+2024-03-20,4,14,False,2024-04-07
 2024-03-13,-1,13,False,2024-02-25
 2024-03-13,0,13,False,2024-03-03
 2024-03-13,1,13,False,2024-03-10

target-data/ERVISS/latest-ARI_incidence.csv

@@ -1,4 +1,8 @@
 location,truth_date,year_week,value
+BE,2024-03-10,2024-W10,1185.3
+BE,2024-03-03,2024-W09,1167.1
+BE,2024-02-25,2024-W08,1512.2
+BE,2024-02-18,2024-W07,1296.2
 BE,2024-02-11,2024-W06,1584.2
 BE,2024-02-04,2024-W05,2033.2
 BE,2024-01-28,2024-W04,1800.4
@@ -488,6 +492,7 @@ BE,2014-10-26,2014-W43,1514.0
 BE,2014-10-19,2014-W42,1441.3
 BE,2014-10-12,2014-W41,1493.7
 BE,2014-10-05,2014-W40,1407.5
+BG,2024-03-17,2024-W11,906.4
 BG,2024-03-10,2024-W10,724.8
 BG,2024-03-03,2024-W09,909.7
 BG,2024-02-25,2024-W08,989.3
@@ -1044,6 +1049,7 @@ CY,2014-10-26,2014-W43,6000.0
 CY,2014-10-19,2014-W42,4900.0
 CY,2014-10-12,2014-W41,4600.0
 CY,2014-10-05,2014-W40,5000.0
+CZ,2024-03-17,2024-W11,1027.5
 CZ,2024-03-10,2024-W10,1080.0
 CZ,2024-03-03,2024-W09,1182.9
 CZ,2024-02-25,2024-W08,1251.7
@@ -1536,6 +1542,7 @@ CZ,2014-10-26,2014-W43,844.1
 CZ,2014-10-19,2014-W42,830.6
 CZ,2014-10-12,2014-W41,820.0
 CZ,2014-10-05,2014-W40,796.5
+EE,2024-03-17,2024-W11,192.7
 EE,2024-03-10,2024-W10,192.1
 EE,2024-03-03,2024-W09,217.4
 EE,2024-02-25,2024-W08,234.3
@@ -2019,6 +2026,8 @@ EE,2014-10-26,2014-W43,245.7
 EE,2014-10-19,2014-W42,242.0
 EE,2014-10-12,2014-W41,245.7
 EE,2014-10-05,2014-W40,290.2
+FI,2024-03-17,2024-W11,2099.6
+FI,2024-03-10,2024-W10,2156.5
 FI,2024-03-03,2024-W09,2284.2
 FI,2024-02-25,2024-W08,2270.4
 FI,2024-02-18,2024-W07,2274.8
@@ -2375,8 +2384,9 @@ FI,2015-10-25,2015-W43,1477.6
 FI,2015-10-11,2015-W41,7012.1
 FI,2015-10-04,2015-W40,6625.3
 FI,2015-05-03,2015-W18,6123.8
-FR,2024-03-10,2024-W10,191.2
-FR,2024-03-03,2024-W09,179.9
+FR,2024-03-17,2024-W11,160.9
+FR,2024-03-10,2024-W10,166.4
+FR,2024-03-03,2024-W09,177.5
 FR,2024-02-25,2024-W08,241.3
 FR,2024-02-18,2024-W07,299.1
 FR,2024-02-11,2024-W06,401.7
@@ -2525,11 +2535,12 @@ FR,2020-08-23,2020-W34,18.0
 FR,2020-08-16,2020-W33,16.7
 FR,2020-08-09,2020-W32,17.6
 FR,2020-08-02,2020-W31,20.6
-DE,2024-03-10,2024-W10,1413.9
-DE,2024-03-03,2024-W09,1539.0
-DE,2024-02-25,2024-W08,1817.0
-DE,2024-02-18,2024-W07,1785.0
-DE,2024-02-11,2024-W06,2078.0
+DE,2024-03-17,2024-W11,1397.0
+DE,2024-03-10,2024-W10,1434.0
+DE,2024-03-03,2024-W09,1546.0
+DE,2024-02-25,2024-W08,1807.9
+DE,2024-02-18,2024-W07,1775.0
+DE,2024-02-11,2024-W06,2074.0
 DE,2024-02-04,2024-W05,2174.0
 DE,2024-01-28,2024-W04,2033.9
 DE,2024-01-21,2024-W03,1772.9
@@ -3018,6 +3029,7 @@ DE,2014-10-26,2014-W43,1189.9
 DE,2014-10-19,2014-W42,1123.0
 DE,2014-10-12,2014-W41,1217.0
 DE,2014-10-05,2014-W40,946.9
+HU,2024-03-17,2024-W11,2027.8
 HU,2024-03-10,2024-W10,2402.8
 HU,2024-03-03,2024-W09,2600.4
 HU,2024-02-25,2024-W08,2952.0
@@ -3072,6 +3084,7 @@ HU,2022-10-30,2022-W43,2007.4
 HU,2022-10-23,2022-W42,2115.2
 HU,2022-10-16,2022-W41,2166.0
 HU,2022-10-09,2022-W40,2130.6
+LV,2024-03-17,2024-W11,1097.0
 LV,2024-03-10,2024-W10,1297.6
 LV,2024-03-03,2024-W09,1340.1
 LV,2024-02-25,2024-W08,1419.6
@@ -3395,6 +3408,7 @@ LV,2014-10-26,2014-W43,1212.5
 LV,2014-10-19,2014-W42,1066.3
 LV,2014-10-12,2014-W41,1167.4
 LV,2014-10-05,2014-W40,1149.5
+LT,2024-03-17,2024-W11,985.6
 LT,2024-03-10,2024-W10,1064.3
 LT,2024-03-03,2024-W09,1128.0
 LT,2024-02-25,2024-W08,1224.6
@@ -3872,8 +3886,9 @@ LT,2014-10-26,2014-W43,588.7
 LT,2014-10-19,2014-W42,567.8
 LT,2014-10-12,2014-W41,580.5
 LT,2014-10-05,2014-W40,540.6
-LU,2024-03-10,2024-W10,18200.0
-LU,2024-03-03,2024-W09,15600.0
+LU,2024-03-17,2024-W11,16400.0
+LU,2024-03-10,2024-W10,20100.0
+LU,2024-03-03,2024-W09,15400.0
 LU,2024-02-25,2024-W08,20200.0
 LU,2024-02-18,2024-W07,37800.0
 LU,2024-02-11,2024-W06,16900.0
@@ -5698,7 +5713,8 @@ SK,2014-10-26,2014-W43,1645.0
 SK,2014-10-19,2014-W42,1591.6
 SK,2014-10-12,2014-W41,1709.6
 SK,2014-10-05,2014-W40,1732.1
-SI,2024-03-10,2024-W10,1508.6
+SI,2024-03-17,2024-W11,1516.0
+SI,2024-03-10,2024-W10,1434.9
 SI,2024-03-03,2024-W09,1656.2
 SI,2024-02-25,2024-W08,1863.5
 SI,2024-02-18,2024-W07,2174.5
@@ -6191,14 +6207,15 @@ SI,2014-10-26,2014-W43,957.8
 SI,2014-10-19,2014-W42,1115.3
 SI,2014-10-12,2014-W41,1057.2
 SI,2014-10-05,2014-W40,1030.7
-ES,2024-03-10,2024-W10,565.1
-ES,2024-03-03,2024-W09,543.8
+ES,2024-03-17,2024-W11,571.4
+ES,2024-03-10,2024-W10,567.7
+ES,2024-03-03,2024-W09,543.9
 ES,2024-02-25,2024-W08,564.2
 ES,2024-02-18,2024-W07,546.9
 ES,2024-02-11,2024-W06,558.1
 ES,2024-02-04,2024-W05,564.9
 ES,2024-01-28,2024-W04,604.4
-ES,2024-01-21,2024-W03,728.5
+ES,2024-01-21,2024-W03,728.4
 ES,2024-01-14,2024-W02,943.3
 ES,2024-01-07,2024-W01,917.2
 ES,2023-12-31,2023-W52,959.9
@@ -6209,7 +6226,7 @@ ES,2023-12-03,2023-W48,682.2
 ES,2023-11-26,2023-W47,615.5
 ES,2023-11-19,2023-W46,573.1
 ES,2023-11-12,2023-W45,558.9
-ES,2023-11-05,2023-W44,434.9
+ES,2023-11-05,2023-W44,435.0
 ES,2023-10-29,2023-W43,472.7
 ES,2023-10-22,2023-W42,458.2
 ES,2023-10-15,2023-W41,398.4