SIR model

SIR model

Usage

ModelSIR(name, prevalence, transmission_rate, recovery_rate)

Arguments

name

String. Name of the virus

prevalence

Double. Initial proportion of individuals with the virus.

transmission_rate

Numeric scalar between 0 and 1. Virus's rate of infection.

recovery_rate

Numeric scalar between 0 and 1. Rate of recovery_rate from virus.

Value

• The ModelSIR function returns a model of class epiworld_model.

Details

The initial_states function allows the user to set the initial state of the model. In particular, the user can specify how many of the non-infected agents have been removed at the beginning of the simulation.

Model diagram

See also

epiworld-methods

Other Models: ModelDiffNet(), ModelMeaslesMixing(), ModelMeaslesMixingRiskQuarantine(), ModelMeaslesSchool(), ModelSEIR(), ModelSEIRCONN(), ModelSEIRD(), ModelSEIRDCONN(), ModelSEIRMixing(), ModelSEIRMixingQuarantine(), ModelSIRCONN(), ModelSIRD(), ModelSIRDCONN(), ModelSIRLogit(), ModelSIRMixing(), ModelSIS(), ModelSISD(), ModelSURV(), epiworld-data

Examples

model_sir <- ModelSIR(name = "COVID-19", prevalence = 0.01,
  transmission_rate = 0.9, recovery_rate = 0.1)

# Adding a small world population
agents_smallworld(
  model_sir,
  n = 1000,
  k = 5,
  d = FALSE,
  p = .01
)

# Running and printing
run(model_sir, ndays = 100, seed = 1912)
#> _________________________________________________________________________
#> Running the model...
#> ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| done.
model_sir
#> ________________________________________________________________________________
#> Susceptible-Infected-Recovered (SIR)
#> It features 1000 agents, 1 virus(es), and 0 tool(s).
#> The model has 3 states.
#> The final distribution is: 0 Susceptible, 0 Infected, and 1000 Recovered.

# Plotting
plot(model_sir)