Model of Vaccinations on Covid19

Model of Vaccinations on Covid19 preview image

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Default-person Alex Brown (Author)

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globals [
  max-infected
  daily-delta
  total-infected
  avg-daily-delta
  total-delta
  moving-average
  day-1
  day-2
  day-3
  day-4
  day-5
  transmissibility ; what is the chance the healthy person becomes sick if they contact a carrier
  ;interactions ; total dialy interactions
  pop-density
  Ro


]


turtles-own[
  infected?
  immune?
  recovered?
  mask?
  dead? ; add superspeader?
  infectionDay?
  recoveryDay?
  quarantineDay?
  death?
  mouthBreather? ;how likely is this person to ifect others if they come into contact
  secondaryinfections?
  superspreader?
  lambda?
  movement? ;how much a turtle moves - also should account for frequency
  distanced?
  quarantine-if-sick?
  quarantined?
  interactions?
  num-neighbors?
  vaccinated?

]

to setup
  clear-all
  set-patch-size (275 / max-pxcor) ; just resets display size
  reset-ticks
  setup-turtles
  setup-masked
  setup-mouthBreather
  setup-distancers
  setup-quarantine
  setup-infected
  set max-infected (count turtles with [infected?])
  set transmissibility (probability-of-transmission)
  set pop-density (num-people / (((max-pxcor * 2) + 1) * ((max-pycor * 2) + 1) ) )
  set Ro (0)
  update-plots
  startingconditions
end 

to setup-turtles
  create-turtles num-people [
    set color white
    set shape "person"
    set size 2
    set infected? false
    set immune? false
    set mask? false
    set dead? false
    set death? false
    set distanced? false
    set mouthBreather? 0
    set secondaryinfections? 0
    set superspreader? false
    set lambda? random-gamma shape-r ((sociability-prob) / (1 - sociability-prob) )
    set movement? 0
    set quarantine-if-sick? false
    set quarantineDay? 0
    set quarantined? false
    set recovered? false
    set interactions? 0
    set num-neighbors? 0
    set vaccinated? false
    setxy random-pxcor random-pycor
  ]
end 

to setup-masked
  ask n-of ((percent-masked / 100) * num-people) turtles [
    set color white
    set shape "person business"
    set mask? true
  ]
end 

to setup-distancers
  ask n-of((prop-distancers / 100) * num-people) turtles [
    set distanced? true
  ]
end 

; this function makes some turtles become infected

to setup-infected
  ask n-of init-infected turtles [    ;n-of takes two inputs, an integer , and a group (turtles),
    set infected? true
    set color 26
    if mask?
      [set shape "person business"]
    recover-time
  ]
end 

to-report random-beta [ #alpha #beta ]
  let XX random-gamma #alpha 1
  let YY random-gamma #beta 1
  report XX / (XX + YY)
end 

to-report random-nbinom [ #r #p ]
  let lambda random-gamma #r ((#p) / (1 - #p) )
  report random-poisson lambda
end 

to setup-mouthBreather
  let mu (Probability-of-Transmission)
  let v (Transmission-shape-parameter)
  let alpha (mu * v)
  let beta ((1 - mu) * v)

  ask turtles [
    set mouthBreather? ((random-beta alpha beta) * transmissibility-scalar)
  ]
end 

to setup-quarantine
  ask n-of ((percent-who-quarantine / 100) * num-people) turtles [
  set quarantine-if-sick? true
  ]
end 

to change-tendencies
  setup-masked
  setup-distancers
  setup-quarantine
end 

to go
    ask turtles [
    set interactions? (0)
  ]
  no-display
  move-normal1
  ;infect-susceptibles
  quarantine
  recover-infected-die
  recolor
  calculate-daily-intereactions
  if (ticks >= 1) [
      calculate-max-infected
      calculate-daily-delta
      calculate-avg-daily-delta
      calculate-total-infected
      calculate-moving-average
      ]
  if (count turtles with [dead? or recovered?] ) >= 1 [
      set Ro (mean [secondaryinfections?] of turtles with [dead? or recovered?])
  ]
  if vaccinations = true [
    if (ticks < 31) [
    vaccinate
    ]
  ]
 ; if ( ((ticks mod 1) > 0.85) and ((ticks mod 1) < 0.95) ) [
    update-plots ;]

  tick-advance 1
  display
end 

to vaccinate
  if (count turtles with [not vaccinated? and not dead? and not immune?] < (num-people * vaccinated-over-30-days) / 30)[
    ask turtles with [not vaccinated? and not dead? and not immune?] [
      set vaccinated? true
    ]
  ]
  if (count turtles with [not vaccinated? and not dead? and not immune?] >= (num-people * vaccinated-over-30-days) / 30)[
      ask n-of ((num-people * vaccinated-over-30-days) / 30) turtles with [not vaccinated? and not dead? and not immune?] [
        set vaccinated? true
      ]
]
end 


; see social distancing model for implementing social distancing w/r turtles

to move-normal1

  ask turtles with [not dead? and not distanced? and not quarantined?] [
    set movement? (random-poisson lambda?)
    let y (0)
    let x (0)
    let z (0)
    while [y < ((movement?))][
      right random 360 ;;get a new random heading
      forward movement?

      set interactions? (interactions? + (count (turtles-on neighbors)))
      ;set neighbor-interactions? (count turtles-on neighbors + neighbor-interactions?)
       ;default socialbility is 3 integer becasue finite number of patches maybe change to long tail distribution,
      if (infected? and not immune?)
      [

      infect-susceptibles

      ]
      let foo (1)
      if vaccinated? [
        set foo (1 - vaccine-efficacy)
      ]


      if (not infected? and not immune? and z = 0 )
      [
        ask turtles-on neighbors [
          set interactions? (interactions? + 1)
         ; set neighbor-interactions? (neighbor-interactions? + 1)
          if (infected? and not quarantined?)[
            let infectivity ifelse-value (mask? = true)
            [(mouthBreather? * masked-transmissibility)]
            [mouthBreather?]


            if (random-float 1 < (infectivity * foo))[
              set secondaryinfections? (secondaryinfections? + 1)
              set z 1
            ]
          ]
          if z = 1 [stop]
        ]

        if (z = 1)[
          set infected? true
          set secondaryinfections? 0
          recover-time
        ]
      ]

     set y (y + 1)
     if (z = 1)[
          stop
      ]


    ; add quarantine metric, see social distance model - sociability distanced: how much do people who are social distances move- typically set at zero
  ]
  ]
end 

to recover-time

  set infectionDay? (floor ticks) ; maybe remove this floor? but also dont if add incubation period
  let rv (7)
  set recoveryDay? (infectionDay? + rv)
   ifelse random-float 1 < 0.01
    [set death? true]
    [set death? false]
  set quarantineDay? (infectionDay? + 5) ; this should eventually be changed
end 

to infect-susceptibles ;; S -> I version of Susceptible Infected Recover model
  ;ask turtles with [infected? and not dead? and not quarantined?][
    let x 0
    let infectivity ifelse-value (mask? = true)
    [(mouthBreather? * masked-transmissibility)]
    [mouthBreather?]
    ask turtles-on neighbors  [
    set interactions? (interactions? + 1)
    let foo (1)
    if vaccinated? [
        set foo (1 - vaccine-efficacy)
      ]
      if (not infected? and not immune?)[
        if (random-float 1 < (infectivity * foo))[
          set secondaryinfections? 0
          set infected? true
          recover-time
          set x (x + 1)
        ]
      ]
    ]
    set secondaryinfections? (secondaryinfections? + x)
    if (secondaryinfections? >= Super-Spreader-Threshold)[
      set superspreader? true
    ]
end 

to quarantine ;
  ask turtles with [infected? and not dead? and quarantine-if-sick?]
  [
    if (ticks >= quarantineDay?)
    [set quarantined? true
    ]
  ]
end 

to recover-infected-die ;;I -> R

  ;;avg case length is 2 weeks.
  ;;should have 50% chance of becoming immune at 2 weeks
  ;;if we are saying each tick equals 1 day,
  ;;daily odds of recovering should be (1-x)^14=.5, x= 0.0483 ;eventually change this to a Normal? distribution look into this


  ask turtles with [infected? and not dead?]
  [
    if (ticks >= recoveryDay?)
    [
      ifelse (death? = false)
      [
        set infected? false
        set recovered? true ;added
        ifelse (are-survivors-immune? = true)
        [
          set immune? true
          ;set color gray
        ]
        [
          ;set color white
        ]
      ]
      [ set dead? true
        ;set color green
        set infected? false

      ]
      set quarantined? false
    ]
  ]
end 

to recolor

  ask turtles with [infected? and not dead? and (secondaryinfections? < Super-Spreader-Threshold)]
  [ set color 26]
  ask turtles with [infected? and not dead? and (secondaryinfections? >= Super-Spreader-Threshold)]
  [ set color red
  set superspreader? true]
  ask turtles with [not infected?]
  [set color white]
  ask turtles with [immune?]
  [set color gray]
  ask turtles with [dead?]
  [ set color green]
  ask turtles with [quarantined?]
  [ set color pink]
end 

to calculate-max-infected
  let x (count turtles with [infected? and not dead?])
  if x > max-infected
  [set max-infected x]
end 

to calculate-total-infected
  set total-infected ((count turtles with [infected? and not dead?])) ;double parenthesis not necessary
end 

to calculate-daily-delta
  set daily-delta (count turtles with [infected? and not dead?] - total-infected)
end 

to calculate-avg-daily-delta
  let y (daily-delta)
  set total-delta (total-delta + y)
  set avg-daily-delta (total-delta / ticks)
end 

to calculate-moving-average

  set day-1 (day-2)
  set day-2 (day-3)
  set day-3 (day-4)
  set day-4 (day-5)
  set day-5 (daily-delta)
 if (ticks >= 5) [
  set moving-average ((day-1 + day-2 + day-3 + day-4 + day-5) / 5)
  ]
end 

to calculate-daily-intereactions
  ask turtles [
    ;set interactions? (interactions? + (count turtles-on neighbors) )
    set num-neighbors? interactions? ; this is pretty dumb and can be cleaned up - but it works rn
  ]
end 

to topdecilex
if ticks > 30 [

print("total infected") print(count(turtles with [infected? or recovered? or dead?]))

let mid (median[secondaryinfections?] of turtles with [infected? or recovered? or dead?])
print("median") print(mid)
print("count above median") print(count(turtles with [(infected? or recovered? or dead?) and secondaryinfections? > mid]))


set mid (median[secondaryinfections?] of turtles with [secondaryinfections? > mid])
print("second median")print(mid)
print("mean")print(mean[secondaryinfections?] of turtles with [secondaryinfections? > mid])
print("count above median")print(count(turtles with [(infected? or recovered? or dead?) and secondaryinfections? > mid]))


set mid (median[secondaryinfections?] of turtles with [secondaryinfections? > mid])
print("third median")print(mid)
print("mean")print(mean[secondaryinfections?] of turtles with [secondaryinfections? > mid])
print("count above median")print(count(turtles with [(infected? or recovered? or dead?) and secondaryinfections? > mid]))


set mid (median[secondaryinfections?] of turtles with [secondaryinfections? > mid])
print("fourth median")print(mid)
print("mean")print(mean[secondaryinfections?] of turtles with [secondaryinfections? > mid])
print("count above median")print(count(turtles with [(infected? or recovered? or dead?) and secondaryinfections? > mid]))


set mid (median[secondaryinfections?] of turtles with [secondaryinfections? > mid])
print("fifth median")print(mid)
print("mean")print(mean[secondaryinfections?] of turtles with [secondaryinfections? > mid])
print("count above median")print(count(turtles with [(infected? or recovered? or dead?) and secondaryinfections? > mid]))


  ]
end 

to-report topdecile
if count(turtles with [infected? or recovered? or dead?]) > 10 [

;print("bloop")
let infectious (count(turtles with [infected? or recovered? or dead?]) / 10)
let avg ( mean ([secondaryinfections?] of max-n-of infectious turtles [secondaryinfections?]))
;print("average")print(avg)

;print("how many are in top 10%")print(count( max-n-of infectious turtles [secondaryinfections?]))
;print("that times average")print(avg * infectious)
;print("total infected recovered or dead")print(count(turtles with [infected? or recovered? or dead?]) - init-infected)
;print("total secondary infections")print(sum ([secondaryinfections?] of turtles))
;print((avg * infectious) / ((count(turtles with [infected? or recovered? or dead?]) - init-infected)))

let proportion ((avg * infectious) / (sum ([secondaryinfections?] of turtles)))

report proportion
  ]


;print (sum [secondaryinfections?] of turtles)
end 

to startingconditions
  print("START")
  print ("num-people") print(num-people)
  print ("sociability-prob") print(sociability-prob)
  print ("shape-r") print(shape-r)
  print ("probability-of-transmission") print(Probability-of-Transmission)
  print ("Transmission-shape-parameter") print(Transmission-shape-parameter)
  print ("density") print(pop-density)
end 

to-report max-infected-prop
  report max-infected / num-people
end 

to-report prop-dead
  let y (count turtles with [dead?])
  report y / num-people
end 

to-report prop-uninfected

  report (count turtles with [not infected? and not immune?]) / num-people
end 

There is only one version of this model, created 3 months ago by Alex Brown.

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