Update optimize.md (#4467)

testing/manual/optimize.md

@@ -23,36 +23,30 @@ Note: sampling combinations in PyCIEMSS can result in numerical instability, whe
 ### 3. Model setup
 1. Create a default configuration with the `Configure model` operator
 2. Calibrate the model with the dataset with the `Calibrate` operator.
+   Mapping the model variable: Culmative_cases with the dataset variable cases
+                               deceased with deaths
+                               timestamp with Timestamp
 
 ### 4. Masking start time optimization
 1. Create a Masking Policy operation with the `Intervention Policy` operator.
+2. Set _NPI mult_ to `0.5` starting at _time_ `61` 
 
 #### 4.a Static intervention
-1. Set _NPI mult_ to `0.5` starting at _time_ `61` 
+1. Connect the calibrated configuration from step 3 and the intervention from step 4 to an optimize node.
 2. Optimize intervention: set _H_ to `< 20 000` in all time points in `95%` of simulated outcomes. 
-3. Find a `new start time` for _NPI mult_ **upper** bound (how long we can delay masking). Start time `60`, end time `150`, initial guess `61`. 
+3. Find a `new start time` for _NPI mult_ `upper bound` (how long we can delay masking). Start time `60`, end time `150`, initial guess `61`. 
 4. Optimization settings: end time `150`, maxiter `3` max eval `30`
-
-#### 4.b Dynamic intervention
-1. Same as above but with a dynamic intervention 
-2. replace 4.a.1 with _NPI mult_ to `0.5` when _H_ `> 16 000`.
+5. This should succeed with a new start time around 60 to 75 range (may vary due to calibration results as well as optimize run)
 
 ### 5. Hospitalizations optimization
 1. Create a Hospitalizations Policy operation with the `Intervention Policy` operator.
 
 #### 5.a Static intervention
 1. Create an intervention for _NPI mult_, set its value to `0.5`, and starting time to `118`
 2. Optimize intervention: set _H_ to `< 20 000` in all time points in `95%` of simulated outcomes.
-3. Find a `new value` for _NPI mult_ **upper** bound (minimal reduction in transmission). Using the following guidelines: Min value = `.0002` intial guess `.5` max = `.9996`
+3. Find a `new value` for _NPI mult_ `upper bound` (minimal reduction in transmission). Using the following guidelines: Min value = `.0002` intial guess `.5` max = `.9996`
 4. Optimization settings: end time `150`, maxiter `3` max eval `30`
 
 #### 5.b Dynamic intervention
 1. Same as above but with a dynamic intervention
 2. replace 5.a.1 with _NPI mult_ to `0.5` when _H_ `> 16 000`.
-
-### 6. Vaccinations optimization
-1. Create a Vaccinations operation with the `Intervention Policy` operator.
-2. Set _r_sv_ to `20 000` starting at _time_ `61`
-3. Optimize intervention: set _H_ to `< 13 000` in all time points in `95%` of simulated outcomes.
-4. Find a `new start time` for _r_sv_ **lower** bound (minimal increase in daily vaccinations). Min value = `10 000` intial guess `20 000` max = `90 000`
-5. Optimization settings: end time `150`, maxiter `3` max eval `30`