ABM Pandemic

No preview image

1 collaborator

Tags

(This model has yet to be categorized with any tags)
Visible to everyone | Changeable by the author
Model was written in NetLogo 6.4.0 • Viewed 220 times • Downloaded 2 times • Run 0 times
Download the 'ABM Pandemic' modelDownload this modelEmbed this model

Do you have questions or comments about this model? Ask them here! (You'll first need to log in.)


Comments and Questions

Please start the discussion about this model! (You'll first need to log in.)

Click to Run Model

turtles-own [
  state  ;; State of each turtle (S for , I for infectious, R for recovered)
]

globals [
  delay
  infection_rate
  K
  observer
  plot-data
  max-ticks
]

to setup
  clear-all
  reset-ticks
  set max-ticks 80
  set infection_rate initial_infection_rate

  create-turtles population-size [
    set shape "person"
    setxy random-xcor random-ycor
    set state "S"
    set color green
  ]

  ask n-of initial_infections turtles [
    set state "I"
    set color red
  ]

  ask n-of initial_recoveries turtles [
    set state "R"
   set color blue
  ]

  set K population-size / 4 ;; Carrying capacity in population (logistic growth)
end 

to run-simulation
  let tick-count 0

  while [tick-count < max-ticks] [
    ask turtles [
      runge-kutta-step count turtles with [state = "S"] count turtles with [state = "I"] count turtles with [state = "R"]
    ]
    ask turtles [
      move
      if state = "I" [
        ask other turtles in-radius 1 [
          if state = "S" and random-float 1 < infection_rate [
            set state "I"
            set color red
          ]
        ]
        ifelse random-float 1 < recovery_rate [
          set state "R"
          set color blue
        ] [
          ;; Infected turtles that do not recover
        ]
      ]
    ]
    set delay (10 / tick_speed) * 0.1

    wait delay
    tick
    set tick-count tick-count + 1

    ;; Record counts and update plot
    let current-s count turtles with [state = "S"]
    let current-i count turtles with [state = "I"]
    let current-r count turtles with [state = "R"]
    set plot-data (list current-s current-i current-r)

    ;; Check if there are still infectious turtles
    if not any? turtles with [state = "I"] [
      stop
    ]
  ]
end 

to move
  rt random 360
  fd 1
  display
end 

to runge-kutta-step [S I R]
  let dt 0.1  ;; Time step size
  let k1 derivative S I R
  let k2 derivative (S + 0.5 * dt * item 0 k1) (I + 0.5 * dt * item 1 k1) (R + 0.5 * dt * item 2 k1)
  let k3 derivative (S + 0.5 * dt * item 0 k2) (I + 0.5 * dt * item 1 k2) (R + 0.5 * dt * item 2 k2)

  ;;print (list "Before update - S: " S " I: " I " R: " R)

  let k4 derivative (S + dt * item 0 k3) (I + dt * item 1 k3) (R + dt * item 2 k3)

  set S S + (dt / 6) * (item 0 k1 + 2 * item 0 k2 + 2 * item 0 k3 + item 0 k4)
  set I I + (dt / 6) * (item 1 k1 + 2 * item 1 k2 + 2 * item 1 k3 + item 1 k4)
  set R R + (dt / 6) * (item 2 k1 + 2 * item 2 k2 + 2 * item 2 k3 + item 2 k4)

  ;; Calculate logistic growth
  let t ticks
  let logistic-growth-value logistic-growth t

  ;; Update infection rate based on logistic growth
  set infection_rate infection_rate + logistic-growth-value

  ;;print (list "After update - S: " S " I: " I " R: " R)
end 

to-report derivative [S I R]
  let N count turtles
  let dS_dt ((-1) * (infection_rate / N) * I * S)
  let dI_dt ((infection_rate / N) * I * S) - (recovery_rate * I)
  let dR_dt (recovery_rate * I)

  report (list dS_dt dI_dt dR_dt)
end 

to-report logistic-growth [t]
  report K / (1 + initial_infection_rate * exp(- recovery_rate * t))
end 

There is only one version of this model, created 7 months ago by Zeus Morley Pineda.

Attached files

No files

This model does not have any ancestors.

This model does not have any descendants.