globals [class length-of-day count-exposed count-symptomatic count-asymptomatic count-recovered count-quarantined] turtles-own [seated? row link-value infected-value exposed-timer days-since-exposure infected-timer covid-vaccine recovered cohort quarantined? covid-student-from-chair-infection-rate covid-chair-from-student-infection-rate covid-prolonged-contact-infection-rate covid-pass-by-infection-rate covid-outside-of-class-infection-rate exposed-to-susceptible-rate BuI-exposed-to-susceptible-rate double-BuI-exposed-to-susceptible-rate double-BuI virus-timer days-since-quarantined quarantine-timer covid-asymptomatic-rate] breed [students student ] breed [professors professor] breed [chairs chair] to setup clear-all reset-ticks ;; creation of pathway ask patches with [pycor - 1 mod 10 = -12 and pxcor > -18 and pxcor < 33] [set pcolor white] ask patches with [pycor - 1 mod 10 = -13 and pxcor > -18 and pxcor < 33] [set pcolor white] ask patches with [pxcor - 1 mod 10 = -18 and pycor <= 35 and pycor > -15 ] [set pcolor white] ask patches with [pxcor + 1 mod 10 = 33 and pycor <= 35 and pycor > -15] [set pcolor white] ask patches with [pycor mod 5 = 0 and pycor < 37 and pycor > -14 and pxcor > -18 and pxcor < 33 ] [ set pcolor white] ask patches with [pycor = -14 or pycor = -13 and pxcor > -18 and pxcor < 33] [ set pcolor white] ;; creation of chairs and podium ask patches with [pxcor mod 5 = 0 and pycor mod 5 = 1 and pycor > -12 and pycor < 37 and pxcor > -20 and pxcor < 31 ] [set pcolor orange] ask patch 0 -14 [ set pcolor yellow] ask patches with [pcolor = orange] [sprout-chairs 1] ask chairs [ hide-turtle] ;; Full Capacity sets all chairs open to student use "orange" if Classroom-Capacity = "Full Capacity" [ ask chairs [ set color orange ]] ;; Half Capacity sets every other chair open to student use, inactive chairs are set "grey" if Classroom-Capacity = "Half Capacity" [ ask chairs [ ifelse xcor mod 2 = ycor mod 2 [ set color grey ] [ set color orange ]]] ;; Cohort capacity follows the same activation as Half Capacity with every other chair set as active if Classroom-Capacity = "Cohort" [ ask chairs [ ifelse xcor mod 2 = ycor mod 2 [ set color grey ] [ set color orange ]]] ;; patches with inactive chairs turn grey ask patches with [pcolor = orange] [ if any? chairs with [color = grey] in-radius 0 [ set pcolor grey]] ;; creation of entrances/exits and area of hiding between classes ask patches with [pxcor - 0 mod 10 = -18 and pycor <= -15 and pycor >= -18 ] [set pcolor red] ask patches with [pxcor - 1 mod 10 = -18 and pycor <= -15 and pycor >= -18 ] [set pcolor red] ask patches with [pxcor - 2 mod 10 = -18 and pycor <= -15 and pycor >= -18 ] [set pcolor red] ask patches with [pxcor + 0 mod 10 = 33 and pycor <= -15 and pycor >= -18 ] [set pcolor red] ask patches with [pxcor + 1 mod 10 = 33 and pycor <= -15 and pycor >= -18 ] [set pcolor red] ask patches with [pxcor + 2 mod 10 = 33 and pycor <= -15 and pycor >= -18 ] [set pcolor red] ;; Cohort Capacity - create 100 students with initialized values (50 in Cohort A, 50 in Cohort B) ifelse Classroom-Capacity = "Cohort" [ create-students 100 [ ifelse random-float 1 < 0.5 ;; door through which the students are entering is randomized [setxy -17 -16] [setxy 32 -16] set shape "person" set color red set size 3 set days-since-exposure 0 ;; days-since-exposure is initialized set days-since-quarantined 0 set infected-value "susceptible" ;; infected-value set as susceptible before infection of any kind set link-value 0 ;; link-value 0 indicates a lack of links to an assigned chair set covid-vaccine false set recovered false set quarantined? false set double-BuI false set cohort "A"] ask n-of 50 students [ set cohort "B"]] [create-students count chairs with [color = orange] [ ifelse random-float 1 < 0.5 [setxy -17 -16] [setxy 32 -16] set shape "person" set color red set size 3 set days-since-exposure 0 set days-since-quarantined 0 set infected-value "susceptible" set link-value 0 set covid-vaccine false set recovered false set quarantined? false set double-BuI false ]] ask students ;; students hidden until time to enter the classroom [hide-turtle] ;; create professor create-professors 1 ;; professor is created - unable to become infected [ setxy -17 -16 set shape "person" set color red set size 3 hide-turtle ] ;; vaccinate based on "Percent-Vaccinated" slider ask n-of (Percent-Vaccinated / 100 * count chairs with [color = orange]) students [ set covid-vaccine true set shape "vacperson"] ;; mask if Mask-Mandate = "Everyone" [ ask students with [covid-vaccine = false] [ set shape "maskperson"] ask students with [covid-vaccine = true] [ set shape "maskvacperson"]] if Mask-Mandate = "Unvaccinated Only" [ ask students with [covid-vaccine = false] [ set shape "maskperson"]] ;; initialize base values set class 0 set length-of-day 500 set count-exposed 0 set count-symptomatic 0 set count-asymptomatic 0 set count-recovered 0 ;; initialize infection values ask students [ set covid-student-from-chair-infection-rate 0.000001 set covid-chair-from-student-infection-rate 0.000001 set covid-prolonged-contact-infection-rate 0.0026 set covid-pass-by-infection-rate 0.000001 set covid-outside-of-class-infection-rate 0.01 set covid-asymptomatic-rate 0.155 set exposed-to-susceptible-rate 0.885 set BuI-exposed-to-susceptible-rate 0.9517 ;;should we continue to include vaccine efficacy when we have numbers (we can include average numbers in addition to including the ability to adjust vaccine efficacy) set double-BuI-exposed-to-susceptible-rate 0.9747 ;; if we do include vaccine efficacy, how do we translate that into double BuI ] ;; adjust rates for students with masks ask students with [shape = "maskperson" or shape = "maskvacperson"] [ set covid-student-from-chair-infection-rate covid-student-from-chair-infection-rate * 0.5 set covid-chair-from-student-infection-rate covid-chair-from-student-infection-rate * 0.5 set covid-prolonged-contact-infection-rate covid-prolonged-contact-infection-rate * 0.5 set covid-pass-by-infection-rate covid-pass-by-infection-rate * 0.5 ] ;; adjust outside of classroom infected rate based on "Rate-of-Transmission" if Rate-of-Transmission = "High" [ ask students [ set covid-outside-of-class-infection-rate covid-outside-of-class-infection-rate * 12 ]] if Rate-of-Transmission = "Substantial" [ ask students [ set covid-outside-of-class-infection-rate covid-outside-of-class-infection-rate * 8 ]] if Rate-of-Transmission = "Moderate" [ ask students [ set covid-outside-of-class-infection-rate covid-outside-of-class-infection-rate * 5 ]] if Rate-of-Transmission = "Low" [ ask students [ set covid-outside-of-class-infection-rate covid-outside-of-class-infection-rate * 2 ]] ;; set links for each student to assigned seat ifelse Classroom-Capacity = "Cohort" [ ask students with [link-value = 0 and cohort = "A"] [ create-link-to one-of chairs with [link-value = 0 and color = orange] if any? link-neighbors [ set link-value 1 ask link-neighbors [ set link-value 1]]] ask students with [link-value = 0 and cohort = "B"] [create-link-to one-of chairs with [link-value = 1 and color = orange] if any? link-neighbors [ set link-value 1 ask link-neighbors [set link-value 2]]]] [ ask students with [link-value = 0] [create-link-to one-of chairs with [link-value = 0 and color = orange] if any? link-neighbors [ set link-value 1 ask link-neighbors [set link-value 1]]]] ;; set row to which each student will travel to during move-to-row subprocedure ask students with [link-value = 1] [ let seat one-of link-neighbors if [ycor] of seat = -9 [ set row 1 ] if [ycor] of seat = -4 [ set row 2 ] if [ycor] of seat = 1 [ set row 3] if [ycor] of seat = 6 [ set row 4 ] if [ycor] of seat = 11 [ set row 5] if [ycor] of seat = 16 [ set row 6] if [ycor] of seat = 21 [ set row 7] if [ycor] of seat = 26 [ set row 8] if [ycor] of seat = 31 [ set row 9] if [ycor] of seat = 36 [ set row 10]] ;; hide links created ask links [ hide-link] end to go ;; create class procedure, each class lasts 500 ticks with students beginning to enter the classroom at tick 0 and leaving beginning at tick 300 ifelse ticks mod length-of-day <= 300 [ unhide move-to-row move-to-chair] [leave test transition] infect sanitize ;;create class change and counter if ticks mod length-of-day = 0 [ set class class + 1] ;;hide links when created ask links [hide-link] ;; stops simulation after the 40th class (traditional college semester) if Classroom-Capacity = "Full Capacity" [if ticks > 19500 and count students with [ any? chairs in-radius 0 ] = 100 [ stop ]] if Classroom-Capacity = "Half Capacity" or Classroom-Capacity = "Cohort" [ if ticks > 19500 and count students with [ any? chairs in-radius 0 ] = 50 [ stop]] tick end to infect ;; set corresponding colors for each infected-value ask students with [color != red] [ if infected-value = "exposed" [ set color green ] if infected-value = "asymptomatic" [ set color pink ] if infected-value = "symptomatic" [ set color 14] if infected-value = "recovered" [ set color brown]] ask students with [infected-value = "susceptible" or infected-value = "recovered"] [if [pcolor] of patch-here = green [ if random-float 1 < covid-student-from-chair-infection-rate [set infected-value "exposed" set exposed-timer 4 set infected-timer random-gamma 3.5 1 set virus-timer exposed-timer + infected-timer set count-exposed count-exposed + 1]]] ask chairs with [color = orange] [ if any? students with [infected-value = "exposed"] in-radius 0 [ if random-float 1 < covid-chair-from-student-infection-rate [set color green ask patch-here [set pcolor green]]]] ask students with [infected-value = "susceptible" or infected-value = "recovered"] [ if color != red [ if any? students with [infected-value = "asymptomatic" or infected-value = "symptomatic"] in-radius 5 [ if random-float 1 < covid-prolonged-contact-infection-rate and any? chairs in-radius 0 [ set infected-value "exposed" set exposed-timer 4 set infected-timer random-gamma 3.5 1 set virus-timer exposed-timer + infected-timer set count-exposed count-exposed + 1]]] if any? students with [ infected-value = "asymptomatic" or infected-value = "symptomatic"] in-radius 5 [ if random-float 1 < covid-pass-by-infection-rate [ set infected-value "exposed" set exposed-timer 4 set infected-timer random-gamma 3.5 1 set virus-timer exposed-timer + infected-timer set count-exposed count-exposed + 1]]] if class >= 2 [ ask students with [infected-value = "susceptible" or infected-value = "recovered" and color = red and ticks mod length-of-day - 1 = 0] [ if random-float 1 < covid-outside-of-class-infection-rate [ set infected-value "exposed" set exposed-timer 4 set infected-timer random-gamma 3.5 1 set virus-timer exposed-timer + infected-timer set count-exposed count-exposed + 1]]] end to sanitize if Sanitation-Effectiveness = "High" [ if ticks mod length-of-day = 1 [ ask chairs with [color = orange] [ if random-float 1 < 0.01 [ set color green ask patch-here [ set pcolor green]]]]] if Sanitation-Effectiveness = "Low" [ if ticks mod length-of-day = 1 [ ask chairs with [color = orange] [ if random-float 1 < 0.05 [ set color green ask patch-here [ set pcolor green]]]]] if Sanitation-Effectiveness = "None" [ if ticks mod length-of-day = 1 [ ask chairs with [color = orange] [ if random-float 1 < 0.1 [ set color green ask patch-here [ set pcolor green]]]]] if ticks mod length-of-day = 0 ;;clears all seats at the end of each class (100% chance) [ ask chairs with [color = green] [set color orange ask patch-here [ set pcolor orange]]] end to unhide ask students with [infected-value = "susceptible" and color = red and random-float 1 < 0.3] ;;once students return from the door the next day, they must be set back to their appropriate "infected condition" [ show-turtle set color blue] ask students with [ infected-value = "exposed" and color = red and random-float 1 < 0.3] [ show-turtle set color green] ask students with [ infected-value = "asymptomatic" and color = red and random-float 1 < 0.3] [ show-turtle set color pink] ask students with [ infected-value = "recovered" and color = red and random-float 1 < 0.3] [ show-turtle set color brown] ask students with [infected-value = "symptomatic" and color = red and random-float 1 < 0.3 ] [ show-turtle set color 14] ask professors with [color = red and random-float 1 < 0.3] [ show-turtle set color yellow] end to move-to-row ;;vertical movement of students ask professors with [color != red and ycor != -14 and random-float 1 < 0.3] [ move-to min-one-of patches with [pcolor = white or pcolor = yellow or pcolor = red ] in-radius 1 [distance patch -17 -14] ] ;; professor moves to podium by moving to white patches closest to set podium ask students with [quarantined? = true] [ hide-turtle] ifelse Classroom-Capacity = "Cohort" [ ifelse class mod 2 = 1 [ ask students with [cohort = "B"] [ hide-turtle] ask students with [cohort = "A"] [ show-turtle ] ask students with [cohort = "A" and color != red and row = 1 and xcor < 0 and ycor != -10 and ycor != -9 and quarantined? = false and random-float 1 < 0.3] [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green] in-radius 1 [distance patch -17 -10]] ask students with [cohort = "A" and color != red and row = 1 and xcor > 0 and ycor != -10 and ycor != -9 and quarantined? = false and random-float 1 < 0.3] [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green ] in-radius 1 [distance patch 32 -10]] ask students with [cohort = "A" and color != red and row = 2 and xcor < 0 and ycor != -5 and ycor != -4 and quarantined? = false and random-float 1 < 0.3] [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green] in-radius 1 [distance patch -17 -5]] ask students with [cohort = "A" and color != red and row = 2 and xcor > 0 and ycor != -5 and ycor != -4 and quarantined? = false and random-float 1 < 0.3] [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green] in-radius 1 [distance patch 32 -5]] ask students with [cohort = "A" and color != red and row = 3 and xcor < 0 and ycor != 0 and ycor != 1 and quarantined? = false and random-float 1 < 0.3] [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green] in-radius 1 [distance patch -17 0]] ask students with [cohort = "A" and color != red and row = 3 and xcor > 0 and ycor != 0 and ycor != 1 and quarantined? = false and random-float 1 < 0.3] [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green] in-radius 1 [distance patch 32 0]] ask students with [cohort = "A" and color != red and row = 4 and xcor < 0 and ycor != 5 and ycor != 6 and quarantined? = false and random-float 1 < 0.3] [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green] in-radius 1 [distance patch -17 5]] ask students with [cohort = "A" and color != red and row = 4 and xcor > 0 and ycor != 5 and ycor != 6 and quarantined? = false and random-float 1 < 0.3] [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green] in-radius 1 [distance patch 32 5]] ask students with [cohort = "A" and color != red and row = 5 and xcor < 0 and ycor != 10 and ycor != 11 and quarantined? = false and random-float 1 < 0.3] [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green] in-radius 1 [distance patch -17 10]] ask students with [cohort = "A" and color != red and row = 5 and xcor > 0 and ycor != 10 and ycor != 11 and quarantined? = false and random-float 1 < 0.3] [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green] in-radius 1 [distance patch 32 10]] ask students with [cohort = "A" and color != red and row = 6 and xcor < 0 and ycor != 15 and ycor != 16 and quarantined? = false and random-float 1 < 0.3] [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green] in-radius 1 [distance patch -17 15]] ask students with [cohort = "A" and color != red and row = 6 and xcor > 0 and ycor != 15 and ycor != 16 and quarantined? = false and random-float 1 < 0.3] [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green] in-radius 1 [distance patch 32 15]] ask students with [cohort = "A" and color != red and row = 7 and xcor < 0 and ycor != 20 and ycor != 21 and quarantined? = false and random-float 1 < 0.3] [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green] in-radius 1 [distance patch -17 20]] ask students with [cohort = "A" and color != red and row = 7 and xcor > 0 and ycor != 20 and ycor != 21 and quarantined? = false and random-float 1 < 0.3] [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green] in-radius 1 [distance patch 32 20]] ask students with [cohort = "A" and color != red and row = 8 and xcor < 0 and ycor != 25 and ycor != 26 and quarantined? = false and random-float 1 < 0.3] [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green] in-radius 1 [distance patch -17 25]] ask students with [cohort = "A" and color != red and row = 8 and xcor > 0 and ycor != 25 and ycor != 26 and quarantined? = false and random-float 1 < 0.3] [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green] in-radius 1 [distance patch 32 25]] ask students with [cohort = "A" and color != red and row = 9 and xcor < 0 and ycor != 30 and ycor != 31 and quarantined? = false and random-float 1 < 0.3] [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green] in-radius 1 [distance patch -17 30]] ask students with [cohort = "A" and color != red and row = 9 and xcor > 0 and ycor != 30 and ycor != 31 and quarantined? = false and random-float 1 < 0.3] [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green] in-radius 1 [distance patch 32 30]] ask students with [cohort = "A" and color != red and row = 10 and xcor < 0 and ycor != 35 and ycor != 36 and quarantined? = false and random-float 1 < 0.3] [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green] in-radius 1 [distance patch -17 35]] ask students with [cohort = "A" and color != red and row = 10 and xcor > 0 and ycor != 35 and ycor != 36 and quarantined? = false and random-float 1 < 0.3] [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green] in-radius 1 [distance patch 32 35]]] [ ask students with [ cohort = "A"] [ hide-turtle] ask students with [cohort = "B"] [ show-turtle] ask students with [cohort = "B" and color != red and row = 1 and xcor < 0 and ycor != -10 and ycor != -9 and quarantined? = false and random-float 1 < 0.3] [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green] in-radius 1 [distance patch -17 -10]] ask students with [cohort = "B" and color != red and row = 1 and xcor > 0 and ycor != -10 and ycor != -9 and quarantined? = false and random-float 1 < 0.3] [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green ] in-radius 1 [distance patch 32 -10]] ask students with [cohort = "B" and color != red and row = 2 and xcor < 0 and ycor != -5 and ycor != -4 and quarantined? = false and random-float 1 < 0.3] [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green] in-radius 1 [distance patch -17 -5]] ask students with [cohort = "B" and color != red and row = 2 and xcor > 0 and ycor != -5 and ycor != -4 and quarantined? = false and random-float 1 < 0.3] [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green] in-radius 1 [distance patch 32 -5]] ask students with [cohort = "B" and color != red and row = 3 and xcor < 0 and ycor != 0 and ycor != 1 and quarantined? = false and random-float 1 < 0.3] [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green] in-radius 1 [distance patch -17 0]] ask students with [cohort = "B" and color != red and row = 3 and xcor > 0 and ycor != 0 and ycor != 1 and quarantined? = false and random-float 1 < 0.3] [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green] in-radius 1 [distance patch 32 0]] ask students with [cohort = "B" and color != red and row = 4 and xcor < 0 and ycor != 5 and ycor != 6 and quarantined? = false and random-float 1 < 0.3] [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green] in-radius 1 [distance patch -17 5]] ask students with [cohort = "B" and color != red and row = 4 and xcor > 0 and ycor != 5 and ycor != 6 and quarantined? = false and random-float 1 < 0.3] [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green] in-radius 1 [distance patch 32 5]] ask students with [cohort = "B" and color != red and row = 5 and xcor < 0 and ycor != 10 and ycor != 11 and quarantined? = false and random-float 1 < 0.3] [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green] in-radius 1 [distance patch -17 10]] ask students with [cohort = "B" and color != red and row = 5 and xcor > 0 and ycor != 10 and ycor != 11 and quarantined? = false and random-float 1 < 0.3] [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green] in-radius 1 [distance patch 32 10]] ask students with [cohort = "B" and color != red and row = 6 and xcor < 0 and ycor != 15 and ycor != 16 and quarantined? = false and random-float 1 < 0.3] [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green] in-radius 1 [distance patch -17 15]] ask students with [cohort = "B" and color != red and row = 6 and xcor > 0 and ycor != 15 and ycor != 16 and quarantined? = false and random-float 1 < 0.3] [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green] in-radius 1 [distance patch 32 15]] ask students with [cohort = "B" and color != red and row = 7 and xcor < 0 and ycor != 20 and ycor != 21 and quarantined? = false and random-float 1 < 0.3] [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green] in-radius 1 [distance patch -17 20]] ask students with [cohort = "B" and color != red and row = 7 and xcor > 0 and ycor != 20 and ycor != 21 and quarantined? = false and random-float 1 < 0.3] [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green] in-radius 1 [distance patch 32 20]] ask students with [cohort = "B" and color != red and row = 8 and xcor < 0 and ycor != 25 and ycor != 26 and quarantined? = false and random-float 1 < 0.3] [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green] in-radius 1 [distance patch -17 25]] ask students with [cohort = "B" and color != red and row = 8 and xcor > 0 and ycor != 25 and ycor != 26 and quarantined? = false and random-float 1 < 0.3] [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green] in-radius 1 [distance patch 32 25]] ask students with [cohort = "B" and color != red and row = 9 and xcor < 0 and ycor != 30 and ycor != 31 and quarantined? = false and random-float 1 < 0.3] [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green] in-radius 1 [distance patch -17 30]] ask students with [cohort = "B" and color != red and row = 9 and xcor > 0 and ycor != 30 and ycor != 31 and quarantined? = false and random-float 1 < 0.3] [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green] in-radius 1 [distance patch 32 30]] ask students with [cohort = "B" and color != red and row = 10 and xcor < 0 and ycor != 35 and ycor != 36 and quarantined? = false and random-float 1 < 0.3] [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green] in-radius 1 [distance patch -17 35]] ask students with [cohort = "B" and color != red and row = 10 and xcor > 0 and ycor != 35 and ycor != 36 and quarantined? = false and random-float 1 < 0.3] [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green] in-radius 1 [distance patch 32 35]] ]] [ ask students with [color != red and row = 1 and xcor < 0 and ycor != -10 and ycor != -9 and quarantined? = false and random-float 1 < 0.3] [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green] in-radius 1 [distance patch -17 -10]] ask students with [ color != red and row = 1 and xcor > 0 and ycor != -10 and ycor != -9 and quarantined? = false and random-float 1 < 0.3] [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green ] in-radius 1 [distance patch 32 -10]] ask students with [color != red and row = 2 and xcor < 0 and ycor != -5 and ycor != -4 and quarantined? = false and random-float 1 < 0.3] [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green] in-radius 1 [distance patch -17 -5]] ask students with [color != red and row = 2 and xcor > 0 and ycor != -5 and ycor != -4 and quarantined? = false and random-float 1 < 0.3] [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green] in-radius 1 [distance patch 32 -5]] ask students with [color != red and row = 3 and xcor < 0 and ycor != 0 and ycor != 1 and quarantined? = false and random-float 1 < 0.3] [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green] in-radius 1 [distance patch -17 0]] ask students with [color != red and row = 3 and xcor > 0 and ycor != 0 and ycor != 1 and quarantined? = false and random-float 1 < 0.3] [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green] in-radius 1 [distance patch 32 0]] ask students with [color != red and row = 4 and xcor < 0 and ycor != 5 and ycor != 6 and quarantined? = false and random-float 1 < 0.3] [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green] in-radius 1 [distance patch -17 5]] ask students with [color != red and row = 4 and xcor > 0 and ycor != 5 and ycor != 6 and quarantined? = false and random-float 1 < 0.3] [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green] in-radius 1 [distance patch 32 5]] ask students with [color != red and row = 5 and xcor < 0 and ycor != 10 and ycor != 11 and quarantined? = false and random-float 1 < 0.3] [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green] in-radius 1 [distance patch -17 10]] ask students with [color != red and row = 5 and xcor > 0 and ycor != 10 and ycor != 11 and quarantined? = false and random-float 1 < 0.3] [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green] in-radius 1 [distance patch 32 10]] ask students with [color != red and row = 6 and xcor < 0 and ycor != 15 and ycor != 16 and quarantined? = false and random-float 1 < 0.3] [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green] in-radius 1 [distance patch -17 15]] ask students with [color != red and row = 6 and xcor > 0 and ycor != 15 and ycor != 16 and quarantined? = false and random-float 1 < 0.3] [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green] in-radius 1 [distance patch 32 15]] ask students with [color != red and row = 7 and xcor < 0 and ycor != 20 and ycor != 21 and quarantined? = false and random-float 1 < 0.3] [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green] in-radius 1 [distance patch -17 20]] ask students with [color != red and row = 7 and xcor > 0 and ycor != 20 and ycor != 21 and quarantined? = false and random-float 1 < 0.3] [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green] in-radius 1 [distance patch 32 20]] ask students with [color != red and row = 8 and xcor < 0 and ycor != 25 and ycor != 26 and quarantined? = false and random-float 1 < 0.3] [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green] in-radius 1 [distance patch -17 25]] ask students with [color != red and row = 8 and xcor > 0 and ycor != 25 and ycor != 26 and quarantined? = false and random-float 1 < 0.3] [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green] in-radius 1 [distance patch 32 25]] ask students with [color != red and row = 9 and xcor < 0 and ycor != 30 and ycor != 31 and quarantined? = false and random-float 1 < 0.3] [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green] in-radius 1 [distance patch -17 30]] ask students with [color != red and row = 9 and xcor > 0 and ycor != 30 and ycor != 31 and quarantined? = false and random-float 1 < 0.3] [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green] in-radius 1 [distance patch 32 30]] ask students with [color != red and row = 10 and xcor < 0 and ycor != 35 and ycor != 36 and quarantined? = false and random-float 1 < 0.3] [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green] in-radius 1 [distance patch -17 35]] ask students with [color != red and row = 10 and xcor > 0 and ycor != 35 and ycor != 36 and quarantined? = false and random-float 1 < 0.3] [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green] in-radius 1 [distance patch 32 35]]] end to move-to-chair ;; horizontal movement of students on their path to their chairs ask students with [quarantined? = true] [ hide-turtle] ask students with [row = 1 and ycor = -10] [ let seat one-of link-neighbors move-to min-one-of patches with [pcolor = white or pcolor = orange or pcolor = green] in-radius 1 [distance seat]] ask students with [ row = 2 and ycor = -5] [ let seat one-of link-neighbors move-to min-one-of patches with [pcolor = white or pcolor = orange or pcolor = green] in-radius 1 [distance seat]] ask students with [row = 3 and ycor = 0] [ let seat one-of link-neighbors move-to min-one-of patches with [pcolor = white or pcolor = orange or pcolor = green] in-radius 1 [distance seat]] ask students with [row = 4 and ycor = 5] [ let seat one-of link-neighbors move-to min-one-of patches with [pcolor = white or pcolor = orange or pcolor = green] in-radius 1 [distance seat]] ask students with [row = 5 and ycor = 10] [ let seat one-of link-neighbors move-to min-one-of patches with [pcolor = white or pcolor = orange or pcolor = green] in-radius 1 [distance seat]] ask students with [row = 6 and ycor = 15] [ let seat one-of link-neighbors move-to min-one-of patches with [pcolor = white or pcolor = orange or pcolor = green] in-radius 1 [distance seat]] ask students with [row = 7 and ycor = 20] [ let seat one-of link-neighbors move-to min-one-of patches with [pcolor = white or pcolor = orange or pcolor = green] in-radius 1 [distance seat]] ask students with [row = 8 and ycor = 25] [ let seat one-of link-neighbors move-to min-one-of patches with [pcolor = white or pcolor = orange or pcolor = green] in-radius 1 [distance seat]] ask students with [row = 9 and ycor = 30] [ let seat one-of link-neighbors move-to min-one-of patches with [pcolor = white or pcolor = orange or pcolor = green] in-radius 1 [distance seat]] ask students with [row = 10 and ycor = 35] [ let seat one-of link-neighbors move-to min-one-of patches with [pcolor = white or pcolor = orange or pcolor = green] in-radius 1 [distance seat]] ask professors with [ycor = -14 ] [move-to min-one-of patches with [pcolor = white or pcolor = orange or pcolor = green or pcolor = yellow] in-radius 1 [distance patch 0 -14] if [pxcor = 0 and pycor = -14] of patch-here [ stop ]] ask students ;; students stop on their chair [ if any? chairs in-radius 0 [stop]] end to leave ;;students and professors leave chairs and return to the door ask professors [ if [pcolor] of patch-here = yellow ;; if turtles are on their "seat", seated variable is set to either true or false to vary departure time [ set seated? one-of [true false]]] ask professors [ ifelse seated? = false [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = yellow] in-radius 1 [distance patch -17 -16] ] [ stop ]] ask students [ if [pcolor] of patch-here = orange or [pcolor] of patch-here = green and random-float 1 < 0.25 [set seated? one-of [true false]]] ask students with [xcor > 0] ;; for students on the left side of the room, going to the left door [ ifelse seated? = false [ move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green] in-radius 1 [distance patch 32 -16] ] ;;when students are ready to leave, they follow the path to the nearest door [stop] ] ask students with [xcor < 0] ;;for students on the right side of the room, going to the right door [ ifelse seated? = false [move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green] in-radius 1 [distance patch -17 -16]] [stop] ] ask students with [xcor = 0] ;;for students in the center of the room (equidistant to both doors) [ ifelse seated? = false [ ifelse random-float 1 < 0.5 [move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green ] in-radius 1 [distance patch 32 -16]] ;;students in center of the room are randomly assigned to a door, simulates choice [move-to min-one-of patches with [pcolor = white or pcolor = red or pcolor = green] in-radius 1 [distance patch -17 -16]] ] [stop] ] ;through door and leaving the room ask students [ if [pcolor] of patch-here = red ;;changes color of students and professor to red in order to make them "disappear" in the door, correlates with the next day [ set color red hide-turtle ]] ask professors [ if [pcolor] of patch-here = red [ set color red hide-turtle]] end to test if Testing = "Random" [ if ticks mod length-of-day = length-of-day - 2 and random-float 1 < 0.5 [ ask n-of (count students / 3) students [ if infected-value = "asymptomatic" or infected-value = "symptomatic" and quarantined? = false [ set quarantined? true set count-quarantined count-quarantined + 1]]] if Testing = "Weekly-Only-Unvaccinated" [ if ticks mod length-of-day = length-of-day * 3 - 1 [ ask students with [infected-value = "asymptomatic" or infected-value = "symptomatic" and covid-vaccine = false and quarantined? = false] [ set quarantined? true set count-quarantined count-quarantined + 1]]]] end to transition if ticks mod length-of-day = length-of-day - 1 [ ask students with [quarantined? = true] [ set days-since-quarantined days-since-quarantined + 2] ask students with [infected-value = "exposed" or infected-value = "symptomatic" or infected-value = "asymptomatic"] [ set days-since-exposure days-since-exposure + 2] ask students [ if covid-vaccine = true and recovered = true [ set double-BuI true]] ask students with [infected-value = "exposed" and color = red] [ if covid-vaccine = true and recovered = false [ if random-float 1 < BuI-exposed-to-susceptible-rate [ set infected-value "susceptible"]]] ask students with [infected-value = "exposed" and color = red] [ if covid-vaccine = false and recovered = true [ if random-float 1 < BuI-exposed-to-susceptible-rate [ set infected-value "recovered"]]] ask students with [infected-value = "exposed" and color = red] [ if double-BuI = true [ if random-float 1 < double-BuI-exposed-to-susceptible-rate [ set infected-value "recovered"]]] ask students with [infected-value = "exposed" and color = red] [ if covid-vaccine = false and recovered = false [ if random-float 1 < exposed-to-susceptible-rate [ set infected-value "susceptible"]]] ask students with [infected-value = "susceptible" and color = red ] [ if random-float 1 < covid-outside-of-class-infection-rate [ set infected-value "exposed" set count-exposed count-exposed + 1 set exposed-timer 4 set infected-timer random-gamma 5 1 set virus-timer exposed-timer + infected-timer set days-since-exposure random-float 2]] ask students with [infected-value = "recovered" and color = red ] [ if random-float 1 < covid-outside-of-class-infection-rate [ set infected-value "exposed" set count-exposed count-exposed + 1 set exposed-timer 4 set infected-timer random-gamma 5 1 set virus-timer exposed-timer + infected-timer set days-since-exposure random-float 2]] ask students with [infected-value = "exposed" and color = red ] [ if days-since-exposure >= exposed-timer [ set infected-value "asymptomatic" set count-asymptomatic count-asymptomatic + 1 ]] ask students with [infected-value = "asymptomatic" and color = red ] [ if days-since-exposure >= exposed-timer + 4.5 and random-float 1 > covid-asymptomatic-rate [ set infected-value "symptomatic" set count-symptomatic count-symptomatic + 1 set quarantined? true set count-quarantined count-quarantined + 1 if Contact-Tracing = "On" [ ask students with [infected-value = "asymptomatic"] [ set quarantined? true set count-quarantined count-quarantined + 1]]]] ask students with [infected-value = "asymptomatic" and color = red] [ if days-since-exposure >= virus-timer [ set infected-value "recovered" set recovered true print days-since-exposure set days-since-exposure 0 set count-recovered count-recovered + 1]] ask students with [infected-value = "symptomatic" and color = red] [ if days-since-exposure >= virus-timer [ set infected-value "recovered" set recovered true print days-since-exposure set days-since-exposure 0 set count-recovered count-recovered + 1]] ask students with [quarantined? = true] [ if days-since-quarantined >= Quarantine-Period [ set quarantined? false ]] ] end @#$#@#$#@ GRAPHICS-WINDOW 11 12 629 631 -1 -1 10.0 1 10 1 1 1 0 0 0 1 -20 40 -20 40 0 0 1 ticks 30.0 BUTTON 666 22 732 55 NIL setup NIL 1 T OBSERVER NIL NIL NIL NIL 1 MONITOR 717 70 774 115 Class class 17 1 11 CHOOSER 1237 483 1388 528 Classroom-Capacity Classroom-Capacity "Full Capacity" "Half Capacity" "Cohort" 0 CHOOSER 1222 553 1395 598 Sanitation-Effectiveness Sanitation-Effectiveness "High" "Low" "None" 2 BUTTON 764 22 827 55 NIL go T 1 T OBSERVER NIL NIL NIL NIL 1 PLOT 864 20 1413 233 Current Infection Numbers NIL NIL 0.0 10.0 0.0 100.0 true true "" "" PENS "Susceptible" 1.0 0 -13345367 true "" "plot count students with [infected-value = \"susceptible\"] + count students with [infected-value = \"recovered\"]" "Exposed" 1.0 0 -10899396 true "" "plot count students with [infected-value = \"exposed\"]" "Asymptomatic" 1.0 0 -2064490 true "" "plot count students with [infected-value = \"asymptomatic\"] + count students with [infected-value = \"quarantined\"] " "Recovered" 1.0 0 -6459832 true "" "plot count students with [infected-value = \"recovered\"]" "Symptomatic" 1.0 0 -5298144 true "" "plot count students with [infected-value = \"symptomatic\"]" "Never Infected" 1.0 0 -817084 true "" "plot count students with [infected-value = \"susceptible\" and recovered = false] " PLOT 865 256 1416 461 Total Infection Numbers NIL NIL 0.0 20000.0 0.0 100.0 false true "" "" PENS "Exposed" 1.0 0 -10899396 true "" "plot count-exposed" "Asymptomatic" 1.0 0 -2064490 true "" "plot count-asymptomatic" "Recovered" 1.0 0 -6459832 true "" "plot count-recovered" "Symptomatic" 1.0 0 -16777216 true "" "plot count-symptomatic" SLIDER 961 484 1134 517 Percent-Vaccinated Percent-Vaccinated 0 100 95.0 1 1 NIL HORIZONTAL CHOOSER 966 625 1130 670 Mask-Mandate Mask-Mandate "Everyone" "Unvaccinated Only" "None" 2 CHOOSER 1234 625 1396 670 Rate-of-Transmission Rate-of-Transmission "High" "Substantial" "Moderate" "Low" 3 CHOOSER 658 480 874 525 Testing Testing "Random" "Weekly-Only-Unvaccinated" 0 SWITCH 964 553 1123 586 Contact-Tracing Contact-Tracing 1 1 -1000 SLIDER 680 550 852 583 Vaccine-Efficacy Vaccine-Efficacy 1 100 1.0 1 1 NIL HORIZONTAL SLIDER 668 632 840 665 Quarantine-Period Quarantine-Period 0 14 0.0 1 1 NIL HORIZONTAL @#$#@#$#@ ## WHAT IS IT? The model allows for an evaluation of different preventative measures implemented by the University of Pittsburgh, including the cohort classroom attendance model, mask and vaccine mandates, contact tracing, and classroom sanitation. Through the use of the model's interactive interface, the impact of adjusting specific measures by the institution could be visualized, providing a valuable tool for combating diseases that spread through droplet transmission. ## HOW IT WORKS The model assumes 40 classes in a semester, each spaced 48 hours apart. Each class lasted one hour, represented in the model by a time period of 500 ticks. To retain simplicity, we excluded weekends and holidays. The classroom was modeled after a standard college lecture hall, with 100 desks arranged into 10 even rows. Each desk was assumed to be one meter apart from others in the same row and one meter apart from others in the same column. In compliance with fire safety regulations, the classroom had two accessible entrances/exits on both sides of the front of the classroom [1]. A lectern for the professor was located in between the entrances on the ground floor of the lecture hall, 2 meters from the first row of student desks. Each student was randomly assigned a specific seat in the setup procedure. Students remained assigned to their specific desk throughout the semester. Students entered randomly from either of the two entrances at the start of class and moved to their specific seat, first moving to the correct intersection between column and row and then moving down the row to their assigned seat. Movement speed and entrance time were randomized for each student every class in order to mimic the varied behaviors of students and better represent the phenomenon. We assumed that no students arrived late or left early from class to retain simplicity. Each student would sit in their seat upon arriving and stay until the end of class (at 350 ticks). At this point, students would be dismissed and would start moving towards the exit closest to them. Again, movement speed and time of departure were varied to alter infection rates. Upon reaching the exit, students would leave the classroom until the start of the next class. The professor would follow this same movement path but would enter randomly from the entrance and move towards the lectern, staying there until the end of class. Students could become exposed through three interactions, characterized as student-to-student, environmental, and outside of the classroom. When within two meters of a contagious individual, students were given a rate of exposure of R(S) [5]. Additionally, desks were given a specific probability of retaining viral load from possibly infectious students who had sat in the desks prior to this class period. Students had a rate of exposure from environmental interactions R(E). Initially, as the perceived threat of environmental exposure was seen as a major factor in the spread of COVID-19, this rate of exposure was relatively high and played a large factor in infection probability. However, with more information about viral transmission dynamics over time, the rate was made substantially lower in order to reflect the minimal impact of environmental exposure highlighted in CDC guidelines [6]. Lastly, based on Allegheny County's rate of transmission and CDC classifications regarding the spread, students were given a specific rate of exposure outside of the classroom R(O) [5]. These rates were based on overall rates and classifications outlined by the CDC. Classifications included high, substantial, moderate, and low [7]. This rate can be altered by the user on the model interface using the Rate-of-Transmission slider. To retain simplicity, the professor was excluded from exposure and progression of infection as it was assumed that, if infected, a substitute lecturer would take over teaching the class. Each student is assigned a vaccination status prior to entering the classroom. The probability of vaccination can be altered by the user using the Percent-Vaccinated slider. Students can become exposed through the three interactions listed above: student-to-student, environmental, and outside of the classroom. Upon exposure, students will either return to susceptibility E(H), as their viral load never crosses the threshold necessary to test positive for the virus, or progress to the asymptomatic classification E(A) due to the sufficient build-up of viral load [5]. From there, these infected individuals will either proceed to the symptomatic classification or remain asymptomatic based on a asymptomatic rate A(S). It is assumed that, throughout the model, symptomatic students do not engage in classroom activities once they develop symptoms and stay in their respective residences until their quarantine period had subsided. 100 percent compliance with recommended guidelines and quarantine timetables is also assumed in order to maintain simplicity. After the infection has subsided, the students return to susceptibility from either their asymptomatic classification A(H) or their symptomatic classification S(H). These flow rates are determined by the random number of days that the individual remains infectious. Students can then become re-exposed, at a lower rate equal to that of a vaccinated individual, due to the same interactions and will either progress through the entire viral cycle or return to susceptibility if viral load never exceeds the infected threshold [15]. With more information becoming available throughout the process of creating the model and the changing landscape of our understanding of transmission dynamics, a variety of preventative measures were implemented in order to vary rates of exposure/viral progression and mirror the methodologies utilized by educational institutions globally. During the early stages of the model, there were radical difference between university policies of dealing with the pandemic, with some institutions choosing to operate in a relatively normal fashion while others choosing to shut down all together. As we wanted to explore the dangers of in-person learning at our home institution, we decided to focus on evaluating the University of Pittsburgh's preventative measures including the implementation of the cohort model, mask mandates, regular sanitation, random testing, contacting tracing, and eventually a vaccination mandate. To reflect social distancing, three different classroom capacities were introduced: full capacity, half capacity, and cohort. Full capacity allows for all 100 students to participate in a traditional, in-person class for all 40 classes in the semester [4]. All seats in the classroom are filled when this capacity is utilized. Half capacity allows for only 50 students to be enrolled in the class and enter the classroom to simulate for social distancing of two meters between individuals. The cohort model, on the other hand, allows all 100 students to be enrolled in the class. However, only 50 of these students, a cohort, will enter and participate in the classroom for each class day. Cohort A attends odd numbered classes while Cohort B attends even numbered classes. Each student was spaced two meters apart in this form of classroom capacity. To further alter exposure rates, three different face mask mandates were implemented into the model: all individuals, unvaccinated individuals only, and none. Upon entering the classroom, face mask use reduces student-to-student exposure rates R(S) by a factor of 0.5 [5]. Furthermore, sanitation effectiveness was another measure introduced to vary the environmental exposure rate. Sanitation effectiveness is applied to the condition of the classroom before the simulated class entered, as we assumed that this classroom was used for prior classes before the simulated class began. Users are able to change the sanitation effectiveness using the Sanitation-Effectiveness chooser on the interface and could alter the probability of viral load remaining on the desk from the previous class. High sanitation effectiveness reduces R(E) by a factor of 0.1 while low effectiveness has a lesser effect, reducing by a factor of 0.5 [12]. Testing was another measure encoded into the model. The model allows for two types of testing: random and weekly testing for only unvaccinated individuals, both policies that were implemented by the University of Pittsburgh during different stages of the pandemic. Random testing randomly selects 1/3 of the class to be tested after every three classes. Weekly testing for the unvaccinated occurs after every three classes as well. If students test positive through the testing procedure, they are placed in a mandatory quarantine, with its length based on the Quarantine-Period slider in the interface, regardless of their symptoms. Additionally, the model allows for an option to introduce contact tracing. If selected, all students in the class will be subject to testing after a single positive test from one of their classmates and all positive individuals will be placed in quarantine [4]. To retain simplicity, it was assumed that testing was 100 percent accurate in determining infection status of students. Finally, vaccination was another preventative measure that was implemented. Vaccination decreases rates of progression for exposed individuals and increases the likelihood of developing minor symptoms that could be classified as asymptomatic [10]. Both vaccination and prior COVID-19 infection reduced rates of individuals progressing from the exposed classification to the asymptomatic classification E(A). In return, vaccination and prior immunity from previous infection increased the rate of exposed individuals returning to the susceptible or recovered classification E(H) [16]. Vaccination efficacy could be altered by the user on the interface in order to incorporate different estimates of efficacy based on the type of vaccine (Moderna, Pfizer, etc.) against a specific variant of COVID-19 (Delta, Omicron, etc.). ## HOW TO USE IT Use the choosers and sliders on the interface to adjust the preventative measures being used to combat the spread of the virus. Use the Classroom-Capacity chooser to select how many students attend class. The Testing chooser allows for the user to choose random testing or weekly testing for unvaccinated students. The Contact-Tracing switch also allows for the user to choose whether they would like the institution to be contact tracing and testing the entire class after one individual has tested positive. The Percent-Vaccinated can be altered to choose the proportion of students who are vaccinated before the start of the semester and the Vaccine-Efficacy slider can be used to alter the efficacy of this vaccination in combating infection. Use the Mask-Mandate, Sanitation-Effectiveness, and Rate-of-Transmission choosers to alter the preventative measures being used the university. The Quarantine-Period slider can be used to alter the amount of time positive students spend away from the classroom before returning. ## THINGS TO NOTICE The graphs on the interface give insight into the current and total infection models. Notice the differences between simulations run with and without preventative measures. Also, be sure to note the color of the students as they attend class. This will provide information about their current infection status. ## THINGS TO TRY Incorporate your own paramter values and change the variables in the model using the sliders in order to model your own approach/preventative measures. ## EXTENDING THE MODEL This model can be translated into any size classroom or work environment as viral dynamics should be relatively similar. We would love to see an expansion of the work into different settings including airplanes, offices, etc. Additionally, the model could be altered slightly to reflect the transmission of other droplet-based diseases (Influenza, Common Cold, etc.) ## CREDITS AND REFERENCES [1] 34 Pa. Code § 54.21. Minimum exits. [2] NetLogo 5.1.0 User Manual. [3] (2020). COVID EXPOSURE & SYMPTOM PRO- TOCOL. [4] (2021). Pitt COVID-19 Dashboard. [5] Bahl, R., Eikmeier, N., Fraser, A., Junge, M., Keesing, F., Nakahata, K., and Reeves, L. (2021). Mod- eling COVID-19 spread in small colleges. PLOS ONE, 16(8):e0255654. Publisher: Public Library of Science. [6] CDC (2020a). Cases, Data, and Surveillance. [7] CDC (2020b). COVID Data Tracker. [8] CDC (2020c). Healthcare Workers. [9] Chiacchio, F., Pennisi, M., Russo, G., Motta, S., and Pappalardo, F. (2014). Agent-Based Modeling of the Immune System: NetLogo, a Promising Framework. BioMed Research International, 2014:907171. [10] Chirico, F., da Silva, J. A. T., Tsigaris, P., and Sharun, K. (2022). Safety & effectiveness of COVID- 19 vaccines: A narrative review. The Indian Journal of Medical Research, 155(1):91–104. [11] Christie, A. (2021). Guidance for Implementing COVID-19 Prevention Strategies in the Context of Varying Community Transmission Levels and Vacci- nation Coverage. MMWR. Morbidity and Mortality Weekly Report, 70. [12] Desye, B. (2021). COVID-19 Pandemic and Wa- ter, Sanitation, and Hygiene: Impacts, Challenges, and Mitigation Strategies. Environmental Health Insights, 15:11786302211029447. [13] Hammond, R. A. (2015). Considerations and Best Practices in Agent-Based Modeling to Inform Policy. In Assessing the Use of Agent-Based Models for Tobacco Regulation. National Academies Press (US). [14] Hatfield, K. M., Baggs, J., Wolford, H., Fang, M., Sattar, A. A., Montgomery, K. S., Jin, S., Jernigan, J., and Pilishvili, T. (2022). 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