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SURV model

Source: R/ModelSURV.R
ModelSURV.Rd

SURV model

Usage

ModelSURV(
  name,
  prevalence,
  efficacy_vax,
  latent_period,
  infect_period,
  prob_symptoms,
  prop_vaccinated,
  prop_vax_redux_transm,
  prop_vax_redux_infect,
  surveillance_prob,
  transmission_rate,
  prob_death,
  prob_noreinfect
)

# S3 method for class 'epiworld_surv'
plot(x, main = get_name(x), ...)

Arguments

name

String. Name of the virus.

prevalence

Initial number of individuals with the virus.

efficacy_vax

Double. Efficacy of the vaccine. (1 - P(acquire the disease)).

latent_period

Double. Shape parameter of a 'Gamma(latent_period, 1)' distribution. This coincides with the expected number of latent days.

infect_period

Double. Shape parameter of a 'Gamma(infected_period, 1)' distribution. This coincides with the expected number of infectious days.

prob_symptoms

Double. Probability of generating symptoms.

prop_vaccinated

Double. Probability of vaccination. Coincides with the initial prevalence of vaccinated individuals.

prop_vax_redux_transm

Double. Factor by which the vaccine reduces transmissibility.

prop_vax_redux_infect

Double. Factor by which the vaccine reduces the chances of becoming infected.

surveillance_prob

Double. Probability of testing an agent.

transmission_rate

Double. Raw transmission probability.

prob_death

Double. Raw probability of death for symptomatic individuals.

prob_noreinfect

Double. Probability of no re-infection.

x

Object of class SURV.

main

Title of the plot.

...

Currently ignore.

Value

The plot function returns a plot of the SURV model of class epiworld_model.

See also

epiworld-methods

Other Models: ModelDiffNet(), ModelSEIR(), ModelSEIRCONN(), ModelSEIRD(), ModelSEIRDCONN(), ModelSEIRMixing(), ModelSIR(), ModelSIRCONN(), ModelSIRD(), ModelSIRDCONN(), ModelSIRLogit(), ModelSIRMixing(), ModelSIS(), ModelSISD(), epiworld-data

Examples

model_surv <- ModelSURV(
  name                  = "COVID-19",
  prevalence            = 20,
  efficacy_vax          = 0.6,
  latent_period         = 4,
  infect_period         = 5,
  prob_symptoms         = 0.5,
  prop_vaccinated       = 0.7,
  prop_vax_redux_transm = 0.8,
  prop_vax_redux_infect = 0.95,
  surveillance_prob     = 0.1,
  transmission_rate     = 0.2,
  prob_death            = 0.001,
  prob_noreinfect       = 0.5
)

# Adding a small world population
agents_smallworld(
  model_surv,
  n = 10000,
  k = 5,
  d = FALSE,
  p = .01
)

# Running and printing
run(model_surv, ndays = 100, seed = 1912)
#> _________________________________________________________________________
#> Running the model...
#> ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| done.
#>  done.
model_surv
#> ________________________________________________________________________________
#> Surveillance
#> It features 10000 agents, 1 virus(es), and 1 tool(s).
#> The model has 8 states.
#> The final distribution is: 9974 Susceptible, 0 Latent, 0 Symptomatic, 0 Symptomatic isolated, 0 Asymptomatic, 0 Asymptomatic isolated, 26 Recovered, and 0 Removed.

# Plotting
plot(model_surv, main = "SURV Model")