Description Usage Arguments Details Value Author(s) References See Also Examples

This function takes the current state of an epidemic, described by the values of SIR, and evolves the epidemic by one time step, stochastically, according to the parameterization provided

1 |

`SIR` |
a |

`last` |
a (dummy) argument used to pass additional information
necessary for the sampling the dynamics of the epidemic;
the return-value of a user-defined |

`true` |
a |

This function is intended to be passed as an argument to the
`manage`

function, to describe the default evolution
of an epidemic under the SIR model. Other, user-defined, functions
undergoing different disease dynamics should follow the protocol (i.e.,
inputs and outputs) prototyped by this function. Similarly, this
function may be used as input to `MCmanage`

which
depends on the `manage`

function.

The epidemic described by the default parameterization
(given by `true`

)
is an approximation of an influenza epidemic in a British
boarding school described by Murray (see references below).

For more details on the parameterization and simulation of the
SIR model, etc., see `vignette("amei")`

`epistep`

returns a `list`

containing the
scalar integer components listed below indicating the number of
individuals which are

`rem ` |
newly removed |

`rec ` |
newly recovered |

`infect ` |
newly infected |

`dead ` |
newly dead |

Daniel Merl <danmerl@gmail.com>

Leah R. Johnson <lrjohnson@uchicago.edu>

Robert B. Gramacy <rbgramacy@chicagobooth.edu>

and Mark S. Mangel <msmangl@ams.ucsc.edu>

D. Merl, L.R. Johnson, R.B. Gramacy, and M.S. Mangel (2010).
“amei: An **R** Package for the Adaptive Management of
Epidemiological Interventions”. *Journal of Statistical Software*
**36**(6), 1-32. http://www.jstatsoft.org/v36/i06/

D. Merl, L.R. Johnson, R.B. Gramacy, M.S. Mangel (2009). “A
Statistical Framework for the Adaptive Management of Epidemiological
Interventions”. *PLoS ONE*, **4**(6), e5807.
http://www.plosone.org/article/info:doi/10.1371/journal.pone.0005807

Murray, J. D. (2002) *Mathematical Biology I: An Introduction*.
Springer Verlag

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ```
## parameters to epistep (similar default except mu != 0)
true <- list(b = 0.00218, k = 0.1, nu = 0.4, mu = 0.1)
SIR <- list(S=700, I=200, R=100)
## examine the distribution of the outputs of epistep
T <- 1000
na <- rep(NA, T)
out <- data.frame(rem=na, rec=na, infect=na, dead=na)
for(t in 1:T) {
out[t,] <- epistep(SIR=SIR, true=true)
}
## make histograms of the output
par(mfrow=c(2,2))
hist(out$rem)
hist(out$rec)
hist(out$infect)
hist(out$dead)
``` |

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