Learn R Programming

SimInf (version 6.2.0)

SEIR: Create an SEIR model

Description

Create an SEIR model to be used by the simulation framework.

Usage

SEIR(u0, tspan, events = NULL, beta = NULL, epsilon = NULL,
  gamma = NULL)

Arguments

u0

A data.frame with the initial state in each node (see ‘Details’).

tspan

A vector (length >= 2) of increasing time points where the state of each node is to be returned. Can be either an integer or a Date vector. A Date vector is coerced to a numeric vector as days, where tspan[1] becomes the day of the year of the first year of tspan. The dates are added as names to the numeric vector.

events

a data.frame with the scheduled events, see SimInf_model.

beta

The transmission rate from susceptible to exposed.

epsilon

The incubation rate from exposed to infected.

gamma

The recovery rate from infected to recovered.

Value

SEIR

Details

The SEIR model contains four compartments; number of susceptible (S), number of exposed (E) (those who have been infected but are not yet infectious), number of infectious (I), and number of recovered (R). Moreover, it has three state transitions,

$$S \stackrel{\beta S I / N}{\longrightarrow} E$$ $$E \stackrel{\epsilon E}{\longrightarrow} I$$ $$I \stackrel{\gamma I}{\longrightarrow} R$$

where \(\beta\) is the transmission rate, \(\epsilon\) is the incubation rate, \(\gamma\) is the recovery rate, and \(N=S+E+I+R\).

The argument u0 must be a data.frame with one row for each node with the following columns:

S

The number of sucsceptible in each node

E

The number of exposed in each node

I

The number of infected in each node

R

The number of recovered in each node

Examples

Run this code
# NOT RUN {
## Create a SEIR model object.
model <- SEIR(u0 = data.frame(S = 99, E = 0, I = 1, R = 0),
              tspan = 1:100,
              beta = 0.16,
              epsilon = 0.25,
              gamma = 0.077)

## Run the SEIR model and plot the result.
set.seed(3)
result <- run(model)
plot(result)
# }

Run the code above in your browser using DataLab