calc.timing.magnitude: Calculate peak timing of introduction and proportion infected

In gilesjohnr/hmob: Tools for analyzing human mobility data

Description

This function takes the output from a TSIR simulation and calculates the peak of the waiting time distribution and the mean proportion infected in each district.

Usage

calc.timing.magnitude(sim, N, pathogen, intro)

Arguments

sim	simulation object containing simulations with both basic gravity and gravity with duration
N	a named vector of population size of each district
pathogen	name of pathogen
intro	type of introduction

Value

a longform dataframe to use for plotting

Author(s)

John Giles

See Also

Other simulation: calc.hpd(), calc.prop.inf(), calc.prop.remain(), calc.wait.time(), decay.func(), get.age.beta(), get.beta.params(), sim.TSIR.full(), sim.TSIR(), sim.combine.dual(), sim.combine(), sim.gravity.duration(), sim.gravity(), sim.lambda(), sim.pi(), sim.rho(), sim.tau()

Examples

sims <- c(
     "./output/TSIR_sim_influenza_high.Rdata",
     "./output/TSIR_sim_influenza_low.Rdata",
     "./output/TSIR_sim_measles_high.Rdata",
     "./output/TSIR_sim_measles_low.Rdata",
     "./output/TSIR_sim_malaria_high.Rdata",
     "./output/TSIR_sim_malaria_low.Rdata",
     "./output/TSIR_sim_ebola_high.Rdata",
     "./output/TSIR_sim_ebola_low.Rdata",
     "./output/TSIR_sim_pertussis_high.Rdata",
     "./output/TSIR_sim_pertussis_low.Rdata",
     "./output/TSIR_sim_sars_high.Rdata",
     "./output/TSIR_sim_sars_lowRdata"
)

# Calculate peak waiting time and proportion infected for each scenario and district and combine
cl <- parallel::makeCluster(4)
doParallel::registerDoParallel(cl)
parallel::clusterExport(cl, ls(environment()), envir=environment())

sims <- foreach (i=1:length(sims), .combine='rbind', .packages=c('stringr', 'hmob')) %dopar% {
     
     sim <- load.obj(sims[i])
     sim.names <- str_split(str_sub(sims[i], end=-7), "_", simplify=TRUE)
     calc.timing.magnitude(sim=sim, N=N.pop, pathogen=sim.names[1,3], intro=sim.names[1,4])
}

# Add waiting time in days
sims$days <- NA
sims$days[sims$pathogen == 'influenza'] <- sims$wait.time[sims$pathogen == 'influenza']*3
sims$days[sims$pathogen == 'measles'] <- sims$wait.time[sims$pathogen == 'measles']*14
sims$days[sims$pathogen == 'malaria'] <- sims$wait.time[sims$pathogen == 'malaria']*60
sims$days[sims$pathogen == 'ebola'] <- sims$wait.time[sims$pathogen == 'ebola']*16.6
sims$days[sims$pathogen == 'pertussis'] <- sims$wait.time[sims$pathogen == 'pertussis']*25
sims$days[sims$pathogen == 'sars'] <- sims$wait.time[sims$pathogen == 'sars']*8

gilesjohnr/hmob documentation built on Aug. 8, 2020, 1:26 a.m.