# Difference between revisions of "Creating a STEM Scenario"

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− | A key concept in STEM is the idea of composing models on top of other models. As an example, a (mathematical) model of a disease depends on having denominator data such as population data (for instance how many people reside in Toulouse) | + | A key concept in STEM is the idea of composing models on top of other models. As an example, a (mathematical) model of a disease depends on having denominator data such as population data (for instance how many people reside in Toulouse), spatial data (e.g. cities in France), and how cities are connected via common borders, road transportation networks and air transportation. When something "depends" on something else in STEM, we use nested models to represent such dependencies. A scenario in STEM has a single model (root model if you like) that contains nested models underneath. Deepest down in the model tree you put static data such as graphs containing geographic information (regions with area information and edges representing common borders) and demographic data such as human population counts. This is also where can place "Population Initializers" to create initial populations any type, e.g. animals. On top of this (in the parent model), you can put a mathematical population model describing how the human population changes each time step of the simulation. The population model create the place holders (label and label values) it needs to keep track of the population in each region being modeled, and it can also rescale the population initially if needed. This is useful when the population data in STEM (which typically comes from publicly available census data) is not up-to-date with current numbers. On top of the population model (in its models' parent model), you put mathematical models for diseases. Since a disease depends on the initial population numbers calculated by the associated population model (with matching population identifier, e.g. 'human'), it needs to be higher up in the model tree. Finally, above this or in the scenario itself, you create infectors, inoculators or initializers that determines the initial state of the disease (i.e. now many people to infect and where, or what is the background herd immunity). |

## Revision as of 15:06, 22 March 2012

## Contents |

**Creating a STEM Scenario**

# Create the Project

To create a new STEM project, click on the ‘new project’ icon in the command bar, or from the “New”-menu use the “New -> scenario” command.

*Figure 1: Creating a New STEM Project ("ExperimentalDisease")*.

*Figure 2: Project Explorer*

The ‘Project’ structure thus created under the name ‘ExperimentalDisease’ contains headings for all the components that are necessary to compose a runnable scenario, including a substructure for the scenario definitions themselves.

# Create a Scenario

The Scenario is the basic executable unit in STEM. A scenario requires a number of mandatory components to be executable, while some other control elements are optional and can add extended functionality to a given basic scenario. Under the menu item “New -> scenario”, you can create a new scenario entry for the scenarios section of your new project.

*Figure 3: Scenario Creation*

Upon creation of the scenario, you also select the Solver algorithm you want to use.

The Finite Difference method is a simple algorithm that is useful to quickly run a simulation and verify that everything looks right. However, for real scenarios where data is being collected for analysis, we recommend using the Runge Kutta Adaptive Step Size method because this algorithm is much more accurate. If you use the Runge Kutta method, you also need to specify a "Relative Tolerance" factor in the disease model parameters. The smaller this number the more accurate the calculations will be, but the simulation will also run slower. The default is 0.05 (or 5%), which we recommend lowering if possible to 0.01 or even 0.001. It all depends how much time you can spare running a scenario. There is also a third option DormandPrince853 that's using the Apache commons mathematical library (http://commons.apache.org/math/) numerical integrator. For more information on this solver and what the four parameters do see the documentation on the library web site (http://commons.apache.org/math/userguide/ode.html).

*Figure 4: Project Explorer*

To be made executable, the blank scenario further requires a sequencer and a nested model structure.

# Add model containers for the model hierarchy

A key concept in STEM is the idea of composing models on top of other models. As an example, a (mathematical) model of a disease depends on having denominator data such as population data (for instance how many people reside in Toulouse), spatial data (e.g. cities in France), and how cities are connected via common borders, road transportation networks and air transportation. When something "depends" on something else in STEM, we use nested models to represent such dependencies. A scenario in STEM has a single model (root model if you like) that contains nested models underneath. Deepest down in the model tree you put static data such as graphs containing geographic information (regions with area information and edges representing common borders) and demographic data such as human population counts. This is also where can place "Population Initializers" to create initial populations any type, e.g. animals. On top of this (in the parent model), you can put a mathematical population model describing how the human population changes each time step of the simulation. The population model create the place holders (label and label values) it needs to keep track of the population in each region being modeled, and it can also rescale the population initially if needed. This is useful when the population data in STEM (which typically comes from publicly available census data) is not up-to-date with current numbers. On top of the population model (in its models' parent model), you put mathematical models for diseases. Since a disease depends on the initial population numbers calculated by the associated population model (with matching population identifier, e.g. 'human'), it needs to be higher up in the model tree. Finally, above this or in the scenario itself, you create infectors, inoculators or initializers that determines the initial state of the disease (i.e. now many people to infect and where, or what is the background herd immunity).

By clicking “New -> model”, you can create an empty model container. Because we will need three different models in a nested structure, we need to create three model containers, named ‘geo’, ‘pop’ and ‘dis’ for this example.

*Figure 5: Model Creation*

# Import graphs from the STEM framework

The geographical data and the population data can be imported as graphs from the STEM framework by pulling the graphs from the STEM resources section on the right into the graphs section on the left under Project Explorer. Here we use country-specific data for Germany.

*Figure 6: Graph Import *

# Create the geography model

The bottom model in the model hierarchy is the geographic model.

This needs to be filled with the ‘area’ graph, the ‘nodes’ graph, and the ‘Common Border relationship within country DEU’ graph.

This is done by opening the ‘geo.model’ in the Designer view (simply by double-clicking it in the Project Explorer),

and dragging the graphs into the geo.model. Saving your geo.model at this stage is probably a good idea, and can be done by hitting <ctrl S> in the Designer view of the model.

*Figure 7: Equipping Models *

# Create the population model

The population model contains the geography model plus the population graph. It can be created by opening the pop.model in the Designer Perspective, and dragging the geo.model from the project view into the pop.model in the Designer. Add the population graph to this, and the basic population model is ready. It is possible to create more elaborate population models (e.g., multiple populations etc.), but no further specifications are not present, an automatically generated population model will be used; this is basically just a wrapper around the population data contained in the imported graph.

*Figure 8: Population Model *

# Create the disease model

To compose the disease model, we finally also need a disease. The disease construction is accessible via the “New -> disease” command. For the sake of creating an example disease, take note that the transmission rate is set to a value larger than zero, and also note that the name of this disease will be required to initialize the infector later on in the scenario construction. The newly defined disease appears in the ‘Decorators’ section of the Project Explorer, and this is also where the infector will appear when it is created. For this demonstration, we choose a DeterministicSEIRDiseaseModelImpl. with a Transmission Rate of 1.0, an Infectious Recovery rate of 0.5, Immunity Loss Rate of 0.3, and an Incubation Rate of 0.3

*Figure 9: Disease Creation *

After creating a disease, you will be able to inspect the properties of the disease in the ‘Properties’ tab of the Resources section of the Designer View. Make sure that all parameters have been entered correctly, e.g., that you have a Transmission Rate larger than Zero. You can change the values of properties in this tab by simply editing them, and saving them by hitting <ctrl-S> in the Designer View again.

To create the disease model, proceed just as before by pulling the pop.model and the disease from the Project Explorer onto the empty dis.model in the Designer View, and save the disease model with <ctrl-S> to update the project.

# Create an Infector

The command “New -> infector” allows you to create an infector. An essential information for the infector is the disease to be linked to this infector, thus ‘Disease Name’ must correctly spell out the name of the disease defined earlier. Furthermore, the geographical location must be specified (under the ‘Select Location’ button), in this case Hamburg as one of the 41 nodes in the geographic model for Germany.

*Figure 12: ISO Key selection for Infector *

# Create a Sequencer

Any scenario will also need a sequencer to be runnable. Under “New->Sequential Sequencer” you can create a sequencer, that specifies the temporal increments upon which the evolution of your scenario will be based. For the sake of brevity, the screenshot is omitted here.

# Equip the Scenario

With a sequencer, an infector, and a nested model hierarchy at our disposal, we can now proceed to equip the scenario. This is done again by simply dragging the components from the Project Explorer onto the scenario heading in the Designer View. The asterisk preceding the scenario name in the tab header of the Designer View indicates unsaved work. Saving the scenario will update the scenario hierarchy in the Project Explorer.

*Figure 13: Scenario Completion *

# Run the Scenario

Right-clicking on the scenario heading and selecting the green triangle icon for “run”, you can now run the scenario and observe the progression of the disease given the selected parameters.