SIR Logistic model

SIR Logistic model

Usage

ModelSIRLogit(
  vname,
  data,
  coefs_infect,
  coefs_recover,
  coef_infect_cols,
  coef_recover_cols,
  prob_infection,
  recovery_rate,
  prevalence
)

Arguments

vname

Name of the virus.

data

A numeric matrix with n rows.

coefs_infect

Numeric vector. Coefficients associated to infect.

coefs_recover

Numeric vector. Coefficients associated to recover.

coef_infect_cols

Integer vector. Columns in the coeficient.

coef_recover_cols

Integer vector. Columns in the coeficient.

prob_infection

Numeric scalar. Baseline probability of infection.

recovery_rate

Numeric scalar. Baseline probability of recovery.

prevalence

Numeric scalar. Prevalence (initial state) in proportion.

Value

• The ModelSIRLogit function returns a model of class epiworld_model.

See also

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

Examples

set.seed(2223)
n <- 100000

# Creating the data to use for the "ModelSIRLogit" function. It contains
# information on the sex of each agent and will be used to determine
# differences in disease progression between males and females. Note that
# the number of rows in these data are identical to n (100000).
X <- cbind(
  Intercept = 1,
  Female    = sample.int(2, n, replace = TRUE) - 1
)

# Declare coefficients for each sex regarding transmission_rate and recovery.
coef_infect  <- c(.1, -2, 2)
coef_recover <- rnorm(2)

# Feed all above information into the "ModelSIRLogit" function.
model_logit <- ModelSIRLogit(
  "covid2",
  data = X,
  coefs_infect      = coef_infect,
  coefs_recover     = coef_recover,
  coef_infect_cols  = 1L:ncol(X),
  coef_recover_cols = 1L:ncol(X),
  prob_infection = .8,
  recovery_rate = .3,
  prevalence = .01
)

agents_smallworld(model_logit, n, 8, FALSE, .01)

run(model_logit, 50)
#> _________________________________________________________________________
#> |Running the model...
#> |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| done.
#> | done.

plot(model_logit)


# Females are supposed to be more likely to become infected.
rn <- get_reproductive_number(model_logit)

# Probability of infection for males and females.
(table(
  X[, "Female"],
  (1:n %in% rn$source)
) |> prop.table())[, 2]
#>       0       1 
#> 0.13466 0.14878 

# Looking into the individual agents.
get_agents(model_logit)
#> Agents from the model "Susceptible-Infected-Removed (SIR) (logit)":
#> Agent: 0, state: Susceptible (0), Has virus: no, NTools: 0i NNeigh: 8
#> Agent: 1, state: Susceptible (0), Has virus: no, NTools: 0i NNeigh: 8
#> Agent: 2, state: Susceptible (0), Has virus: no, NTools: 0i NNeigh: 8
#> Agent: 3, state: Susceptible (0), Has virus: no, NTools: 0i NNeigh: 8
#> Agent: 4, state: Susceptible (0), Has virus: no, NTools: 0i NNeigh: 8
#> Agent: 5, state: Susceptible (0), Has virus: no, NTools: 0i NNeigh: 8
#> Agent: 6, state: Susceptible (0), Has virus: no, NTools: 0i NNeigh: 8
#> Agent: 7, state: Susceptible (0), Has virus: no, NTools: 0i NNeigh: 8
#> Agent: 8, state: Susceptible (0), Has virus: no, NTools: 0i NNeigh: 8
#> Agent: 9, state: Susceptible (0), Has virus: no, NTools: 0i NNeigh: 8
#> ... 99990 more agents ...