ModelSEIRDCONN {epiworldR} | R Documentation |
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
)
## S3 method for class 'epiworld_seirdconn'
plot(x, main = get_name(x), ...)
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. |
x |
Object of class SEIRCONN. |
main |
Title of the plot. |
... |
Currently ignore. |
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.
Value
The
ModelSEIRDCONN
function returns a model of class epiworld_model.
The plot
function returns a plot of the SEIRDCONN model of class
epiworld_model.
See Also
epiworld-methods
Other Models:
ModelDiffNet()
,
ModelSEIR()
,
ModelSEIRCONN()
,
ModelSEIRD()
,
ModelSIR()
,
ModelSIRCONN()
,
ModelSIRD()
,
ModelSIRDCONN()
,
ModelSIRLogit()
,
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)
add_virus(model = model_seirdconn, virus = flu, proportion = .001)
#' # Running and printing
run(model_seirdconn, ndays = 100, seed = 1912)
model_seirdconn
plot(model_seirdconn)