Update README.md

README.md

@@ -17,6 +17,8 @@ For the primary target, teams will submit quantile nowcasts and forecasts of the
 
 There are standard software packages to convert from dates to epidemic weeks and vice versa (e.g. [MMWRweek](https://cran.r-project.org/web/packages/MMWRweek/) and [lubridate](https://lubridate.tidyverse.org/reference/week.html) for R and [pymmwr](https://pypi.org/project/pymmwr/) and [epiweeks](https://pypi.org/project/epiweeks/) for Python). 
 
+Note: Since hospitalization admission data for the preceding week will be provided on the Wednesday deadline, weekly hospital admission targets with a horizon of -1 will not be scored in summary evaluations nor included in visualizations. However, teams are  encouraged to submit targets with a horizon of -1 to aid in detecting potential calibration issues. 
+
 **Sample trajectories:**
 
 In addition to the quantile forecasts for incident hospital admissions, this season teams may submit samples for 0- to 3- week ahead forecasts. We use the term “model task” below to refer to a prediction for a specific horizon, location, and reference date. For teams submitting samples, the FluSight hub will require exactly 100 samples for each model task. We request that samples only be submitted when they are structured into temporally connected samples across horizons (i.e., samples should not be submitted that are solely drawn from the distribution of quantile forecasts). In particular, a common sample ID (specified in the ‘output_type_id’ field) will be used in multiple rows of the submission file with values of target date.  
@@ -31,7 +33,7 @@ The objective of the optional rate trend target is to characterize the trajector
 
 Rate-trend categories are defined by binning state-level changes in weekly hospital admission incidence on a rate scale (counts per 100k people). A change is defined as the difference between the finalized reported weekly hospitalization rates in the EW ending on the target end date and the baseline EW ending **one week** prior to the reference date. At the time that nowcasts and forecasts are generated, this baseline week will be the most recent week for which the official data released on healthdata.gov include reported hospital admission values for at least some days (see Figures 1 and 2). Let $t$ denote the reference date and $y_s$ denote the finalized hospitalization rate in units of counts/100k population on the week ending on date $s$. The change in hospitalization rates at a weekly horizon $h$ is rate_change = $y_{t+h*7} - y_{t}$ . 
 
-The date ranges used in these calculations are illustrated in an example in Table 2. Corresponding count changes are based on state-level population sizes (i.e., count_change = rate_change*state_population / 100,000). See the locations.csv file in [auxiliary-data/](https://github.com/cdcepi/FluSight-forecast-hub/tree/main/auxiliary-data) for the population sizes that will be used to calculate rates.
+The date ranges used in these calculations are illustrated in an example in [Table 2](https://github.com/cdcepi/FluSight-forecast-hub/tree/main/model-output). Corresponding count changes are based on state-level population sizes (i.e., count_change = rate_change*state_population / 100,000). See the locations.csv file in [auxiliary-data/](https://github.com/cdcepi/FluSight-forecast-hub/tree/main/auxiliary-data) for the population sizes that will be used to calculate rates.
 
 Rate thresholds separating categories of change (e.g., separating "stable" trends from "increase" trends) will be the same across states, but are translatable into counts using the state's population size (see locations.csv, in the auxiliary-data subfolder of this repository). Any week pairs with a difference of fewer than 10 hospital admissions will be classified as having a "stable" trend.  Specific rate-difference thresholds for changes have been developed for each prediction horizon, based on past distributions observed in FluSurv-NET and HHS-Protect. These are provided below in the model-outputs directory README file.
 
@@ -43,6 +45,8 @@ Teams may also submit probability forecasts for peak week. Peak week will be def
 
 The objective of these seasonal targets is to provide actionable and intuitive forecasts for public health decision makers. These targets will characterize the season as a whole and predict the intensity and timing of the highest severity segment of the season to expand public health utility. 
 
+All forecast targets will be evaluated based on reported values for weekly influenza admissions in the [NHSN Hospital Respiratory Dataset](https://data.cdc.gov/Public-Health-Surveillance/Weekly-Hospital-Respiratory-Data-HRD-Metrics-by-Ju/mpgq-jmmr/about_data) provided on healthdata.gov (commonly referred to as HHS Protect). As such, forecasting teams are encouraged to consider uncertainty in values as they are reported in real time. We emphasize that predictions of the rate trend targets will be evaluated based on changes in reported hospitalization rates based on finalized data at the end of the season.
+
 If you have questions about this season’s FluSight Collaboration, please reach out to Rebecca Borchering, Sarabeth Mathis, and the CDC FluSight team ([email protected]).  
 
 ## Acknowledgments