SIS: Create an SIS model

In SimInf: A Framework for Data-Driven Stochastic Disease Spread Simulations

Create an SIS model

Description

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

Usage

SIS(u0, tspan, events = NULL, beta = NULL, gamma = NULL)

Arguments

u0	A data.frame with the initial state in each node (see ‘Details’).
tspan	A vector (length >= 1) 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	A numeric vector with the transmission rate from susceptible to infected where each node can have a different beta value. The vector must have length 1 or nrow(u0). If the vector has length 1, but the model contains more nodes, the beta value is repeated in all nodes.
gamma	A numeric vector with the recovery rate from infected to recovered where each node can have a different gamma value. The vector must have length 1 or nrow(u0). If the vector has length 1, but the model contains more nodes, the beta value is repeated in all nodes.

Details

The SIS model contains two compartments; number of susceptible (S), and number of infectious (I). Moreover, it has two state transitions,

S -- beta S I / N --> I

I -- gamma I --> S

where β is the transmission rate, γ is the recovery rate, and N=S+I.

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

I

The number of infected in each node

Value

A SimInf_model of class SIS

Examples

## Create an SIS model object.
model <- SIS(u0 = data.frame(S = 99, I = 1),
             tspan = 1:100,
             beta = 0.16,
             gamma = 0.077)

## Run the SIS model and plot the result.
set.seed(22)
result <- run(model)
plot(result)

SimInf documentation built on Jan. 23, 2023, 5:43 p.m.