SIR_ABM.nlogo

globals[
susceptibles_  ;the number of susceptible people
infecteds_ ;the number of infected people
recovereds_  ;the number of recovered people
width  ;;the width of the world
]

turtles-own[
status ;1 for susceptible, 2 for infected, 3 for recovered
]

to setup
ca
reset-ticks
;;set the world size equal to the number of agents
set susceptibles_ read-from-string susceptibles
set width (sqrt susceptibles_) - 1
resize-world 0 width 0 width

;;create agents
crt susceptibles_ [set status 1 move-to one-of patches ]
ask turtles [rt random 360 fd 1]

;;one agent gets infected
ask n-of 1 turtles [set status 2]

change-color

end


to go
  ;;stop if no agent is infected
  if count turtles with [status = 2] = 0 [stop]

  ;;agents randomly move around
  ask turtles [rt random 360 fd 1]

  ;;infected agents may infect agents in the same cell by a
  ;;probability = infection rate / the probability to be in the same cell * change in time
  ask turtles with [status = 2][
    ask turtles-here [
      if random-float 1 < (InfectionRate * ( width * width )* dt)[
      if status = 1 [set status 2]]
    ]]

  ;;infected agents may recover by a probability = recovery rate * change in time
  ask turtles with [status = 2][
    if random-float 1 < RecoveryRate * dt [
      set status 3
    ]]

;;update the number of each type of agents
  set susceptibles_ count turtles with [status = 1]
  set infecteds_ count turtles with [status = 2]
  set recovereds_ count turtles with [status = 3]

  change-color

  abm-do-plot

  tick
end


to change-color
;;susceptibles are green, infecteds are red, recovereds are blue
ask turtles[ if status = 1[set color green]
             if status = 2[set color red]
             if status = 3[set color blue]]


end

to abm-do-plot
;;plot the numbers of susceptibles, infecteds, recovereds over time
  if plot-pen-exists? "susceptibles" [
    set-current-plot-pen "susceptibles"
    plotxy ticks susceptibles_
  ]
  if plot-pen-exists? "infecteds" [
    set-current-plot-pen "infecteds"
    plotxy ticks infecteds_
  ]
  if plot-pen-exists? "recovereds" [
    set-current-plot-pen "recovereds"
    plotxy ticks recovereds_
  ]
end
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@#$#@#$#@
## WHAT IS IT?

This is an agent-based model (ABM) version of the SIR model.

## HOW IT WORKS

At the beginning, one agent will get infected. During each iteration (dt), an infected agent may infect agents on the same cell. Here, I would like to map the probability of getting infected to the one in the SD model. Therefore, as in the SD model, the infection rate is the infection rate on the entire population. In this ABM, the probability to get infected is equal to the infection rate divided by the probability to be in the same cell, times the change in time. Each infected agent has a probability to recover in each time period, which equals to the recovery rate times the change in time. The equations can be expressed as:

probability that an agent will get infected = infection rate / probability to be in the same cell * change in time

probability that an agent will recovered = recover rate * change in time


## HOW TO USE IT

1. Use the sliders to set the parameters.

2. Press setup to create the agents, randomly distribute them on the landscape, and randomly select the first infected cell.

3. Press go to run the simulation.

The display in the middle shows the agents, green = susceptibles, red = infected, blue = recovered.


## RELATED MODELS

I have built SIR model using three different approaches, including Agent-Based Modeling, Cellular Automata, and Discrete Event Simulation. Also, there is System Dynamics model of SIR available from Shiflet et al. (2014).

The models are posted on my blog: http://geospatialcss.blogspot.com

## CREDITS AND REFERENCES

Shiflet, A. B., & Shiflet, G. W. (2014). Introduction to computational science: modeling and simulation for the sciences. Princeton University Press.
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NetLogo 6.1.0
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<experiments>
  <experiment name="experiment" repetitions="10" runMetricsEveryStep="true">
    <setup>setup</setup>
    <go>go</go>
    <exitCondition>count turtles with [status = 2] = 0</exitCondition>
    <metric>susceptibles_</metric>
    <metric>infecteds_</metric>
    <metric>recovereds_</metric>
    <enumeratedValueSet variable="RecoveryRate">
      <value value="0.5"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="InfectionRate">
      <value value="0.002"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="susceptibles">
      <value value="&quot;2500&quot;"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="dt">
      <value value="0.001"/>
    </enumeratedValueSet>
  </experiment>
</experiments>
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