epiworldR (version 0.12.0.0)

ModelSEIRMixingQuarantine: Susceptible Exposed Infected Removed model (SEIR) with mixing and quarantine

Description

ModelSEIRMixingQuarantine creates a model of the SEIR type with mixing and a quarantine mechanism. Agents who are infected can be quarantined or isolated. Isolation happens after the agent has been detected as infected, and agents who have been in contact with the detected person will me moved to quarantined status.

Usage

ModelSEIRMixingQuarantine(
  name,
  n,
  prevalence,
  contact_rate,
  transmission_rate,
  incubation_days,
  recovery_rate,
  contact_matrix,
  hospitalization_rate,
  hospitalization_period,
  days_undetected,
  quarantine_period,
  quarantine_willingness,
  isolation_willingness,
  isolation_period,
  contact_tracing_success_rate,
  contact_tracing_days_prior
)

Value

• The ModelSEIRMixingQuarantine function returns a model of class epiworld_model.

Arguments

name

String. Name of the virus

n

Number of individuals in the population.

prevalence

Double. 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. Average number of days in the incubation period.

recovery_rate

Numeric scalar between 0 and 1. Probability of recovery.

contact_matrix

Matrix of contact rates between individuals.

hospitalization_rate

Double. Rate of hospitalization.

hospitalization_period

Double. Period of hospitalization.

days_undetected

Double. Number of days an infection goes undetected.

quarantine_period

Integer. Number of days for quarantine.

quarantine_willingness

Double. Proportion of agents willing to quarantine.

isolation_willingness

Double. Proportion of agents willing to isolate.

isolation_period

Integer. Number of days for isolation.

contact_tracing_success_rate

Double. Probability of successful contact tracing.

contact_tracing_days_prior

Integer. Number of days prior to the onset of the infection for which contact tracing is effective.

Model diagram

Details

The contact_matrix is a matrix of contact rates between entities. The matrix should be of size n x n, where n is the number of entities. This is a row-stochastic matrix, i.e., the sum of each row should be 1.

The quarantine and isolation processes can be turned off by specifying quarantine_period < 0 and isolation_period < 0 respectively. In other words, any negative value for these parameters will suppress the corresponding process.

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.

See Also

epiworld-methods

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

Examples

# Start off creating three entities.
# Individuals will be distributed randomly between the three.
e1 <- entity("Population 1", 3e3, as_proportion = FALSE)
e2 <- entity("Population 2", 3e3, as_proportion = FALSE)
e3 <- entity("Population 3", 3e3, as_proportion = FALSE)

# Row-stochastic matrix (rowsums 1)
cmatrix <- c(
  c(0.9, 0.05, 0.05),
  c(0.1, 0.8, 0.1),
  c(0.1, 0.2, 0.7)
) |> matrix(byrow = TRUE, nrow = 3)

N <- 9e3

flu_model <- ModelSEIRMixingQuarantine(
  name                  = "Flu",
  n                     = N,
  prevalence            = 1 / N,
  contact_rate          = 20,
  transmission_rate     = 0.1,
  recovery_rate         = 1 / 7,
  incubation_days       = 7,
  contact_matrix        = cmatrix,
  hospitalization_rate  = 0.05,
  hospitalization_period = 7,
  days_undetected       = 3,
  quarantine_period     = 14,
  quarantine_willingness = 0.8,
  isolation_period      = 7,
  isolation_willingness = 0.5,
  contact_tracing_success_rate = 0.7,
  contact_tracing_days_prior = 3
)

# Adding the entities to the model
flu_model |>
  add_entity(e1) |>
  add_entity(e2) |>
  add_entity(e3)

set.seed(331)
run(flu_model, ndays = 100)
summary(flu_model)