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10/6/2019 Disease Dynamics (ABM) | Insight Maker

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This tutorial describes how to construct a model of an infectious disease usingThis tutorial describes how to construct a model of an infectious disease using

Insight Maker. Agent-Based Modeling techniques are used in the constructionInsight Maker. Agent-Based Modeling techniques are used in the construction

of this model. Before starting the tutorial, make sure you have familiarizedof this model. Before starting the tutorial, make sure you have familiarized

yourself with how to yourself with how to create primitivescreate primitives and and run modelsrun models..

Table of Contents Insight Maker Guide

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Types of Modeling

System Dynamics

Agent Based Modeling

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Disease Dynamics (SD)

Disease Dynamics (ABM)

Predator-Prey Interactions (SD)

Manual

Model Diagramming

Ghosting

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Equations and Formulae

Built-In Functions

Advanced Equations

Types of Primitives

Stocks

Flows

Variables

Links

Converters

States

Transitions

Actions

Agent Populations

Running a Model

Model Verification

Constraints

Units

Storytelling

Agent Geography

Spatial Geography

Network Geography

Sensitivity Testing

Optimization

Vectors

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Model Scripting

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Disease Dynamics (ABM)

Create a new Create a new StateState named named HealthyHealthy ..11

Create a new Create a new StateState named named InfectedInfected ..22

Create a new Create a new StateState named named RecoveredRecovered ..33

The model diagram should now look something like this:The model diagram should now look something like this:

States represent the condition someone is in. So in ourStates represent the condition someone is in. So in our

model a person can either be healthy, infected, ormodel a person can either be healthy, infected, or

recovered from the infection. Now, lets add transitionsrecovered from the infection. Now, lets add transitions

that move a person from state to state.that move a person from state to state.

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Create a new Create a new TransitionTransition going from the primitive going from the primitive HealthyHealthy to the primitive to the primitive InfectedInfected ..

Name that transition Name that transition InfectionInfection ..

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Create a new Create a new TransitionTransition going from the primitive going from the primitive InfectedInfected to the primitive to the primitive

RecoveredRecovered . Name that transition . Name that transition RecoveryRecovery ..

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The model diagram should now look something like this:The model diagram should now look something like this:77

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Please note that in this model someone who is recoveredPlease note that in this model someone who is recovered

cannot become sick again. They have gained immunity tocannot become sick again. They have gained immunity to

the disease. the disease.

Now that the model structure has been designed, let's addNow that the model structure has been designed, let's add

equations and configure the primitives.equations and configure the primitives.

Change the Change the Initial ActiveInitial Active property of the primitive property of the primitive HealthyHealthy to to truetrue ..

When a state is active, it means a person is in that state.When a state is active, it means a person is in that state.

By setting By setting HealthyHealthy to start to start truetrue, we have the person, we have the person start in the healthy state.start in the healthy state.

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Change the Change the Trigger TypeTrigger Type property of the primitive property of the primitive InfectionInfection to to ProbabilityProbability ..99

Change the Change the Value/EquationValue/Equation property of the primitive property of the primitive InfectionInfection to to 0.30.3 ..1010

Change the Change the Trigger TypeTrigger Type property of the primitive property of the primitive RecoveryRecovery to to ProbabilityProbability ..1111

Change the Change the Value/EquationValue/Equation property of the primitive property of the primitive RecoveryRecovery to to 0.20.2 ..

Using the Using the ProbabilityProbability type for the transition trigger means type for the transition trigger means that the person has a fixed probability of transitioningthat the person has a fixed probability of transitioning

from one state to the next each year.from one state to the next each year.

1212

Run the model. Here are sample results:Run the model. Here are sample results:1313

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Don't worry if your results do not look exactly like this.Don't worry if your results do not look exactly like this.

This is a stochastic model so each time we run it, we willThis is a stochastic model so each time we run it, we will

get different results. Let's give it a try.get different results. Let's give it a try.

Run the model. Here are sample results:Run the model. Here are sample results:1414

Run the model. Here are sample results:Run the model. Here are sample results:1515

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When a state takes on the value When a state takes on the value 11 it means the person is it means the person is in the state. We can see how the person moves fromin the state. We can see how the person moves from

healthy, to infected, to recovered in these three differenthealthy, to infected, to recovered in these three different

simulation runs. Right now we are only simulating a singlesimulation runs. Right now we are only simulating a single

person. Let's extend our model to simulate a population ofperson. Let's extend our model to simulate a population of

people.people.

Create a new Create a new FolderFolder named named PersonPerson . The folder should surround the primitives. The folder should surround the primitives

HealthyHealthy , , InfectedInfected , , RecoveredRecovered , , InfectionInfection and and RecoveryRecovery ..

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The model diagram should now look something like this:The model diagram should now look something like this:1717

Change the Change the BehaviorBehavior property of the primitive property of the primitive PersonPerson to to AgentAgent ..1818

Create a new Create a new Agent PopulationAgent Population named named PopulationPopulation ..1919

Change the Change the Agent BaseAgent Base property of the primitive property of the primitive PopulationPopulation to to PersonPerson ..2020

The model diagram should now look something like this:The model diagram should now look something like this:2121

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Run the model. Here are sample results:Run the model. Here are sample results:

That looks pretty good! We can see the disease graduallyThat looks pretty good! We can see the disease gradually

spread through our population of 100 people.spread through our population of 100 people.

However, right now the probability of a person gettingHowever, right now the probability of a person getting

sick is independent of the other people in the model. Let'ssick is independent of the other people in the model. Let's

change the model so the more sick people there are, thechange the model so the more sick people there are, the

higher the likelihood that a healthy person will becomehigher the likelihood that a healthy person will become

sick.sick.

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Create a new Create a new VariableVariable named named Percent InfectedPercent Infected ..2323

Create a new Create a new LinkLink going from the primitive going from the primitive PopulationPopulation to the primitive to the primitive

Percent InfectedPercent Infected ..

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Create a new Create a new LinkLink going from the primitive going from the primitive Percent InfectedPercent Infected to the primitive to the primitive

InfectionInfection ..

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The model diagram should now look something like this:The model diagram should now look something like this:2626

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Now let's configure the value of Now let's configure the value of Percent InfectedPercent Infected and and change the change the InfectionInfection transition to use it. transition to use it.

Change the Change the EquationEquation property of the primitive property of the primitive Percent InfectedPercent Infected to to

Count(FindState([Population], [Infected]))/PopulationSize([Population])Count(FindState([Population], [Infected]))/PopulationSize([Population]) ..

This equation uses the This equation uses the FindStateFindState function to select all the function to select all the people in the people in the PopulationPopulation primitive who are in the primitive who are in the InfectedInfected state. It then divides the count of those people state. It then divides the count of those people

by the total size of the population.by the total size of the population.

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Change the Change the Value/EquationValue/Equation property of the primitive property of the primitive InfectionInfection to to

[Percent Infected][Percent Infected] , and the , and the RecalculateRecalculate property of the primitive property of the primitive InfectionInfection to to

YesYes ..

The The RecalculateRecalculate property causes the infection rate to be property causes the infection rate to be recalculated and updated at every time step of therecalculated and updated at every time step of the

simulation.simulation.

2828

Run the model. Here are sample results:Run the model. Here are sample results:

Nothing happened because we don't have anyone to startNothing happened because we don't have anyone to start

the infection. Let's seed the model with a single initialthe infection. Let's seed the model with a single initial

infected person.infected person.

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Change the Change the Initial ActiveInitial Active property of the primitive property of the primitive InfectedInfected to to Index(Self) == 1Index(Self) == 1 ..3030

Change the Change the Initial ActiveInitial Active property of the primitive property of the primitive HealthyHealthy to to Index(Self) <> 1Index(Self) <> 1 ..

Each of the people in the population is given an uniqueEach of the people in the population is given an unique

IndexIndex. The first agent will have an index of 1, the second. The first agent will have an index of 1, the second

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an index of 2, and so on. an index of 2, and so on. SelfSelf always refers to the always refers to the current agent. These equations will set the first agentcurrent agent. These equations will set the first agent

created to start in the created to start in the InfectedInfected state while all the other state while all the other agents will start in the agents will start in the HealthyHealthy state. state.

Run the model. Here are sample results:Run the model. Here are sample results:

Since this is a stochastic model, each time you run it, youSince this is a stochastic model, each time you run it, you

will get slightly different results. Let's give it a try.will get slightly different results. Let's give it a try.

3232

Run the model. Here are sample results:Run the model. Here are sample results:3333

Run the model. Here are sample results:Run the model. Here are sample results:3434

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That works great! We now have a full Agent-Based ModelThat works great! We now have a full Agent-Based Model

of disease.of disease.

We can also configure the display type to be a map of theWe can also configure the display type to be a map of the

people in the model in a two-dimensional geography.people in the model in a two-dimensional geography.

Right now, people are placed randomly, but we couldRight now, people are placed randomly, but we could

configure their locations or make them move around inconfigure their locations or make them move around in

response to different factors. We could also change theresponse to different factors. We could also change the

infection rate so the infection spread geographically.infection rate so the infection spread geographically.

Run the model. Here are sample results:Run the model. Here are sample results:3535

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