PSI-PROF initial forecast submission - updated

model-metadata/PSI-PROF.yaml

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+schema_version: "https://raw.githubusercontent.com/Infectious-Disease-Modeling-Hubs/schemas/main/v2.0.0/admin-schema.json"
+team_name: "Predictive Science Inc."
+team_abbr: "PSI"
+model_name: "Package for Respiratory Disease Open-source Forecasting"
+model_abbr: "PROF"
+model_version: "1.0"
+model_contributors: [
+  {
+    "name": "Michal Ben-Nun",
+    "affiliation": "Predictive Science Inc",
+    "email": "[email protected]"
+  },
+  {
+    "name": "James Turtle",
+    "affiliation": "Predictive Science Inc",
+    "email": "[email protected]"
+  },
+  {
+    "name": "Pete Riley",
+    "affiliation": "Predictive Science Inc",
+    "email": "[email protected]"
+  }
+]
+website_url: "https://www.predsci.com/usa-flu-hosp/"
+license: "CC-BY-4.0"
+citation: "https://doi.org/10.1371/journal.pcbi.1010375"
+team_funding: "CSTE/CDC: Development of Forecasts and/or Scenario Projections for Influenza to Inform Public Health Decision Making (Cooperative Agreement number NU38OT000297)."
+methods: "We fit and extrapolate an age-stratified SIR compartmental model with four levels of natural immunity as well as infant/elder vaccination."
+data_inputs: "Weekly incident flu hospital admissions from RSV-NET. US Census population totals and age distributions by state. Estimated vaccination rates from the Scenario Modeling Hub-RSV Round 1."
+methods_long: "Model parameters are determined by a three-phase fitting procedure. 1) Pre-pandemic data is used to determine parameters that characterize the pathogen transmission characteristics. 2) 2020-2023 season data are used to estimate the age-stratified number of susceptibles entering the current season. 3) Fit the timing of each model trajectory to the current late-season data and then filter trajectories for best alignment with the entire season. The model uses 14 age strata (highly concentrated in the very young) so that we can properly account for maternally-conferred immunity in infants." 
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