A simulation about mask wearing in public schools in the Greater Houston Area

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With increasing incidences of COVID-19 infections in the Greater Houston Area and students returning to school, informed decisions must be made regarding teacher and student continuous masking recommendations. As of today (August 21), 1. An executive order is in place which prohibits Texas government funded entities from mandating mask wearing on premises . 2. Houston ISD is one of a handful of schools in the Houston area which does have a mask wearing mandate in place. 3. Colleges in the our area have established differing policies. Notably, Rice University has COVID policies that mandate mask wearing indoors . To provide quantitative insight into these mixed stances, we ran a targeted simulation using our COVID-19 Outbreak Simulator to compare the number of COVID-cases that develop in schools that mandate or do not mandate mask wearing.

Code Snippets

%cd #home/BoPeng/back-to-school/
res = read.csv('results.csv', check.names=FALSE)

SoS Notebook From line 88 of main/back_to_school.ipynb

library(ggplot2)
library(gridExtra)

SoS Notebook ggplot2 gridExtra From line 93 of main/back_to_school.ipynb

res_1600 = res[res$`Community Infection Rate (per million)` == 1600, ]
res_1600_cur_vac = rbind(
    res_1600[res_1600$`Vaccination Coverage (teachers / students)` == '60% / 30%',],
    res_1600[res_1600$`Vaccination Coverage (teachers / students)` == '60% / 0%',])

#geom_text(aes(hjust=0, vjust=0,
#                label=round(`Percentage of students infected at school`, 2)),
#                size=5) +
#geom_errorbar(aes(ymin=`Percentage of students infected at school, 2.5% Quantile`,
#                    ymax=`Percentage of students infected at school, 97.5% Quantile`),
#                colour="black", width=.1) +    

p1 <- ggplot(res_1600_cur_vac,  aes(x=`Mask wearing %`, y=`Percentage of students infected at school`) ) + 
  geom_point(aes(col=School), size=2) +  theme(aspect.ratio = 1) +   
  coord_cartesian(xlim=c(0, 100), ylim=c(0, 25)) + 
  labs(title="Percentage of students infected at school")
p2 <- ggplot(res_1600_cur_vac, aes(x=`Mask wearing %`, y=`Percentage of students isolated due to COVID symptoms`) ) + 
  geom_point(aes(col=School), size=2) + theme(aspect.ratio = 1) +   
  coord_cartesian(xlim=c(0, 100), ylim=c(0, 25)) + 
  labs(title="Percentage of students isolated due to COVID symptoms"
       )

ggsave('school.png', plot=grid.arrange(p1, p2, nrow = 1), width=3000, height=1400, units='px', dpi=240)

SoS Notebook ggplot2 From line 98 of main/back_to_school.ipynb

%preview school.png

SoS Notebook From line 124 of main/back_to_school.ipynb

input: for_each=high_contexts

scratch_dir = '#ictr-scratch/bpeng/back_to_school/'

SoS Notebook From line 269 of main/back_to_school.ipynb

task: queue='hpc', cores=4, walltime='1h', mem='4G', tags=name, workdir=scratch_dir, trunk_size=1

SoS Notebook From line 274 of main/back_to_school.ipynb

sh: expand=True
  #rm -f {name}.log.lock
  outbreak_simulator --popsize {pop_size} \
      --stop-if 't>{duration}' -j 4 \
      --track-events INFECTION WARNING PLUGIN SHOW_SYMPTOM QUARANTINE \
      --repeat {num_replicates} --resume \
      --symptomatic-r0 {sym_r0} T={distancing_multiplier} S={distancing_multiplier} \
      --asymptomatic-r0 {asym_r0} T={distancing_multiplier} S={distancing_multiplier} \
      --immunity-of-recovered {immunity_of_recovered} \
      --infectivity-of-recovered {infectivity_of_recovered} \
      --incubation-period {incu_period} \
      --prop-asym-carriers {prop_asym_carriers} \
      --handle-symptomatic 'quarantine?duration=10&infected=true' \
      --logfile {name}.log \
      --plugin init \
          --incidence-rate {community_infection_rate} \
          --seroprevalence {prop_recovered} --as-proportion \
      --plugin vaccinate \
          --start 0 --proportion {vac_proportion} --immunity {vac_immunity} \
          --infectivity {vac_infectivity} \
      --plugin community_infection --start 0 --interval 1 --probability {community_infection_rate}

SoS Notebook COVID19 Outbreak Simulator From line 276 of main/back_to_school.ipynb

input: for_each=elementary_contexts

scratch_dir = '#ictr-scratch/bpeng/back_to_school/'

SoS Notebook From line 300 of main/back_to_school.ipynb

task: queue='hpc', cores=4, walltime='1h', mem='4G', tags=name, workdir=scratch_dir, trunk_size=1

SoS Notebook From line 305 of main/back_to_school.ipynb

sh: expand=True
  rm -f {name}.log.lock
  outbreak_simulator --popsize {pop_size} \
      --stop-if 't>{duration}' -j 4 \
      --track-events INFECTION WARNING PLUGIN SHOW_SYMPTOM QUARANTINE \
      --vicinity 'T-S*=0.5' 'T-T=10' 'S*-!&=0.25' 'S*-&=19' 'S*-T=3' \
      --repeat {num_replicates} \
      --symptomatic-r0 {sym_r0} T={distancing_multiplier} 'S*={distancing_multiplier}' \
      --asymptomatic-r0 {asym_r0} T={distancing_multiplier} 'S*={distancing_multiplier}' \
      --immunity-of-recovered {immunity_of_recovered} \
      --infectivity-of-recovered {infectivity_of_recovered} \
      --incubation-period {incu_period} \
      --prop-asym-carriers {prop_asym_carriers} \
      --handle-symptomatic 'quarantine?duration=10&infected=true' \
      --logfile {name}.log \
      --plugin init \
          --incidence-rate {community_infection_rate} \
          --seroprevalence {prop_recovered} --as-proportion \
      --plugin vaccinate \
          --start 0 --proportion {vac_proportion} --immunity {vac_immunity} \
          --infectivity {vac_infectivity} \
      --plugin community_infection --start 0 --interval 1 --probability {community_infection_rate}