Update optimize test

testing/manual/optimize.md

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+## PyCIEMSS Optimize
+Please go through __every__ step of the test scenario.\
+Report any issues into GitHub: [open an issue](https://github.com/DARPA-ASKEM/terarium/issues/new?assignees=&labels=bug%2C+Q%26A&template=qa-issue.md&title=%5BBUG%5D%3A+).
+
+### Note
+Note: sampling combinations in PyCIEMSS can result in numerical instability, when this happens, you can:
+- Retry the simulation, or
+- Fiddle with the parameter distribution ranges, make the intervals larger or smaller
+
+### 1. Begin test
+1. Login to https://app.staging.terarium.ai using the test account
+    ```
+    email: qa@test.io
+    password: askem-quality-assurance
+    ```
+2. Create, or open, project named `QA [Your Name]`
+3. Create a workflow named `Optimize Test`
+
+### 2. Upload assets
+1. Use/upload the _SEIRHD_ model from [google drive](https://drive.google.com/drive/folders/1bllvuKt6ZA1vc36AW3Xet4y6ZAnwnaVN)
+2. Use/upload the _LA county_ dataset from [google drive](https://drive.google.com/drive/folders/1bllvuKt6ZA1vc36AW3Xet4y6ZAnwnaVN)
+
+### 3. Model setup
+1. Create a default configuration with the `Configure model` operator
+2. Calibrate the model with the dataset with the `Calibrate` operator.
+
+### 4. Masking start time optimization
+1. Create a Masking Policy operation with the `Intervention Policy` operator.
+
+#### 4.a Static intervention
+1. Set _NPI mult_ to `0.5` starting at _time_ `61` 
+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`. 
+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. Hospitalizations optimization
+1. Create a Hospitalizations Policy operation with the `Intervention Policy` operator.
+
+#### 5.a Static intervention
+1. Set _NPI mult_ to `0.5` starting at _time_ `118`
+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 (minimal reduction in transmission). 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`