View source: R/generation.time.R

generation.time | R Documentation |

Create an object of class `GT`

representing a discretized Generation Time
distribution, that can be subsequently passed to estimation routines.

```
generation.time(
type = c("empirical", "gamma", "weibull", "lognormal"),
val = NULL,
truncate = NULL,
step = 1,
first.half = TRUE,
p0 = TRUE
)
```

`type` |
Type of distribution (can be any of |

`val` |
Vector of values used for the empirical distribution, or |

`truncate` |
Maximum extent of the GT distribution. |

`step` |
Time step used in discretization. |

`first.half` |
Boolean. When set to |

`p0` |
Boolen. When set to |

How the GT is discretized may have some impact on the shape of the distribution. For example, the distribution may be discretized in intervals of 1 time step starting at time 0, i.e. [0,1), [1,2), and so on. Or it may be discretized as [0,0.5), [0.5, 1.5), ... (the default).

If the GT is discretized from a given continuous distribution, the expected duration of the Generation Time will be less than the nominal, it will be in better agreement using the second discretization (default behavior).

If p0 is set to `TRUE`

(default), the generation time distribution is set to 0 for
day 0, meaning that the infectees generated by an infected individual will not
become incident on the same day.

If no truncation is provided, the distribution will be truncated at 99.99% probability.

A list with components:

`GT` |
The probabilities for each time unit, starting at time 0. |

`time` |
The time at which probabilities are calculated. |

`mean` |
The mean of the discretized GT. |

`sd` |
The standard deviation of the discretized GT. |

Pierre-Yves Boelle, Thomas Obadia

```
#Loading package
library(R0)
# GT for children at house(from Cauchemez PNAS 2011)
GT.chld.hsld1 <- generation.time("empirical", c(0,0.25,0.2,0.15,0.1,0.09,0.05,0.01))
plot(GT.chld.hsld1, col="green")
GT.chld.hsld1
# Discretized Generation Time distribution
# mean: 2.729412 , sd: 1.611636
# [1] 0.00000000 0.29411765 0.23529412 0.17647059 0.11764706 0.10588235 0.05882353
# [8] 0.01176471
GT.chld.hsld2 <- generation.time("gamma", c(2.45, 1.38))
GT.chld.hsld2
# Discretized Generation Time distribution
# mean: 2.504038 , sd: 1.372760
# [1] 0.0000000000 0.2553188589 0.3247178420 0.2199060781 0.1144367560
# [6] 0.0515687896 0.0212246257 0.0082077973 0.0030329325 0.0010825594
#[11] 0.0003760069 0.0001277537
# GT for school & community
GTs1 <- generation.time("empirical", c(0,0.95,0.05))
plot(GTs1, col='blue')
plot(GT.chld.hsld1, ylim=c(0,0.5), col="red")
par(new=TRUE)
plot(GT.chld.hsld2, xlim=c(0,7), ylim=c(0,0.5), col="black")
```

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