# time_bins.sir

##### SIR model on graphs

Run simulations for an SIR (susceptible-infected-recovered) model, on a graph

- Keywords
- graphs

##### Usage

```
# S3 method for sir
time_bins(x, middle = TRUE)
```# S3 method for sir
median(x, na.rm = FALSE, ...)

# S3 method for sir
quantile(x, comp = c("NI", "NS", "NR"), prob, ...)

sir(graph, beta, gamma, no.sim = 100)

##### Arguments

- x
A

`sir`

object, returned by the`sir`

function.- middle
Logical scalar, whether to return the middle of the time bins, or the boundaries.

- na.rm
Logical scalar, whether to ignore

`NA`

values.`sir`

objects do not contain any`NA`

values currently, so this argument is effectively ignored.- …
Additional arguments, ignored currently.

- comp
Character scalar. The component to calculate the quantile of.

`NI`

is infected agents,`NS`

is susceptibles,`NR`

stands for recovered.- prob
Numeric vector of probabilities, in [0,1], they specify the quantiles to calculate.

- graph
The graph to run the model on. If directed, then edge directions are ignored and a warning is given.

- beta
Non-negative scalar. The rate of infection of an individual that is susceptible and has a single infected neighbor. The infection rate of a susceptible individual with n infected neighbors is n times beta. Formally this is the rate parameter of an exponential distribution.

- gamma
Positive scalar. The rate of recovery of an infected individual. Formally, this is the rate parameter of an exponential distribution.

- no.sim
Integer scalar, the number simulation runs to perform.

##### Details

The SIR model is a simple model from epidemiology. The individuals of the population might be in three states: susceptible, infected and recovered. Recovered people are assumed to be immune to the disease. Susceptibles become infected with a rate that depends on their number of infected neigbors. Infected people become recovered with a constant rate.

The function `sir`

simulates the model.

Function `time_bins`

bins the simulation steps, using the
Freedman-Diaconis heuristics to determine the bin width.

Function `median`

and `quantile`

calculate the median and
quantiles of the results, respectively, in bins calculated with
`time_bins`

.

##### Value

For `sir`

the results are returned in an object of class
‘`sir`

’, which is a list, with one element for each simulation.
Each simulation is itself a list with the following elements. They are all
numeric vectors, with equal length:

- times
The times of the events.

- NS
The number of susceptibles in the population, over time.

- NI
The number of infected individuals in the population, over time.

- NR
The number of recovered individuals in the population, over time.

Function `time_bins`

returns a numeric vector, the middle or the
boundaries of the time bins, depending on the `middle`

argument.

`median`

returns a list of three named numeric vectors, `NS`

,
`NI`

and `NR`

. The names within the vectors are created from the
time bins.

`quantile`

returns the same vector as `median`

(but only one, the
one requested) if only one quantile is requested. If multiple quantiles are
requested, then a list of these vectors is returned, one for each quantile.

##### References

Bailey, Norman T. J. (1975). The mathematical theory of infectious diseases and its applications (2nd ed.). London: Griffin.

##### See Also

`plot.sir`

to conveniently plot the results

##### Examples

```
# NOT RUN {
g <- sample_gnm(100, 100)
sm <- sir(g, beta=5, gamma=1)
plot(sm)
# }
```

*Documentation reproduced from package igraph, version 1.2.2, License: GPL (>= 2)*