R/process_flu_city_log.R

In lwillem/IBMcourseTutorials: IBM Course Tutorials

############################################################################
#
# This file is part of the course material for "Individual-based Modelling
# in Epidemiology" by Wim Delva and Lander Willem.
#
############################################################################

process_flu_city_log <- function(sim_data_log)
{

  # set plot panels
  par(mfrow=c(2,3))

  ## INFECTIONS OVER TIME (days)
  plot(table(sim_data_log$day),type='b',xlab='days',ylab='new infections',main='incidence (per day)')

  ## INFECTIONS OVER TIME (hour of a day)
  plot(table(sim_data_log$hour),type='b',xlab='time of day (hour)',ylab='new infections',main='incidence (time of day)')
  abline(v=7.5,lty=2)
  abline(v=16.5,lty=2)
  abline(v=20.5,lty=2)

  ## GET AGE CLASSES
  range(sim_data_log$age)
  age_class_threshold <- c(0,18,65,90)
  sim_data_log$age_class <- cut(sim_data_log$age,age_class_threshold,right = FALSE)
  sim_data_log$age_infector_class <- cut(sim_data_log$age_infector,age_class_threshold,right = FALSE)

  ## CONTEXT
  barplot(table(sim_data_log$context),main='context',xlab='frequency',las=2,horiz = T)

  ## INFECTIONS: TOTAL
  print(table(sim_data_log$age_infector_class,sim_data_log$age_class,dnn=c('age infecter','age infected \t\t -- all infections --')))

  ## INFECTIONS: HOUSEHOLD
  sel <- sim_data_log$context == 'household'
  print(table(sim_data_log$age_infector_class[sel],sim_data_log$age_class[sel],dnn=c('age infector','age infected \t\t -- infections at home --')))

  ## INFECTIONS: SCHOOL
  sel <- sim_data_log$context == 'school'
  print(table(sim_data_log$age_infector_class[sel],sim_data_log$age_class[sel],dnn=c('age infector','age infected \t\t -- infections at school--')))

  ## INFECTIONS: WORKPLACE
  sel <- sim_data_log$context == 'workplace'
  print(table(sim_data_log$age_infector_class[sel],sim_data_log$age_class[sel],dnn=c('age infector','age infected \t\t -- infections at work --')))

  ## SECUNDARY CASES
  barplot(table(table(sim_data_log$id_infector)),main='secundary cases',xlab='secundary cases',ylab='count')

  ## SECUNDARY CASES OVER TIME
  sim_data_log$sec_cases <- NA
  for(i in 1:dim(sim_data_log)[1])
  {
    sim_data_log$sec_cases[i] <- sum(sim_data_log$id_infector == sim_data_log$id[i])
  }
  boxplot(sim_data_log$sec_cases ~ sim_data_log$day, outline=F,xlab='days',ylab='secundary cases',main='secundary cases')

  mean_cases <- aggregate(sec_cases ~ day, data=sim_data_log ,mean)
  lines(mean_cases$day+1,mean_cases$sec_cases,type='l',lwd=2,col=2)
  legend('topright','mean',lwd=2,col=2)

  # set plot panels back to default
  par(mfrow=c(1,1))

}