epiworldR (version 0.8.2.0)

ModelSEIRDCONN: Susceptible Exposed Infected Removed Deceased model (SEIRD connected)

Description

The SEIRD connected model implements a model where all agents are connected. This is equivalent to a compartmental model (wiki).

Usage

ModelSEIRDCONN(
  name,
  n,
  prevalence,
  contact_rate,
  transmission_rate,
  incubation_days,
  recovery_rate,
  death_rate
)

Value

• The ModelSEIRDCONNfunction returns a model of class epiworld_model.

Arguments

name

String. Name of the virus.

n

Number of individuals in the population.

prevalence

Initial proportion of individuals with the virus.

contact_rate

Numeric scalar. Average number of contacts per step.

transmission_rate

Numeric scalar between 0 and 1. Probability of transmission.

incubation_days

Numeric scalar greater than 0. Average number of incubation days.

recovery_rate

Numeric scalar between 0 and 1. Probability of recovery_rate.

death_rate

Numeric scalar between 0 and 1. Probability of death.

Details

The initial_states function allows the user to set the initial state of the model. The user must provide a vector of proportions indicating the following values: (1) Proportion of exposed agents who are infected, (2) proportion of non-infected agents already removed, and (3) proportion of non-ifected agents already deceased.

See Also

epiworld-methods

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

Examples

# An example with COVID-19
model_seirdconn <- ModelSEIRDCONN(
  name                = "COVID-19",
  prevalence          = 0.01,
  n                   = 10000,
  contact_rate        = 2,
  incubation_days     = 7,
  transmission_rate   = 0.5,
  recovery_rate       = 0.3,
  death_rate          = 0.01
)

# Running and printing
run(model_seirdconn, ndays = 100, seed = 1912)
model_seirdconn

plot(model_seirdconn)

# Adding the flu
flu <- virus(
  "Flu", prob_infecting = .3, recovery_rate = 1 / 7,
  prob_death = 0.001,
  prevalence = 0.001, as_proportion = TRUE
)
add_virus(model = model_seirdconn, virus = flu)

#' # Running and printing
run(model_seirdconn, ndays = 100, seed = 1912)
model_seirdconn

plot(model_seirdconn)