# Real-time predictions of the final size and the turning point at the end of the epidemic, the incidence and the cumulative number of cases in future observations.

### Description

This function provides real-time predictions of the final size and the turning point at the end of the epidemic for each built-in model and model averaged, as well as the incidence and the cumulative number of cases in future observations. Also this function, when all the built-in models are used, gives the AIC of each model, the model averaged weights and predicted incidence and cumulative cases.

### Usage

1 2 | ```
## Object of the S3 class dengue
allmodelpredict(inc,time,pred,start=NULL,model)
``` |

### Arguments

`inc,time` |
Vector of equal length specifying incidence (number of reported cases per time unit) and time interval (from the start of outbreak). |

`pred` |
Number of observation in which the incidence and the cumulative number of cases will be predicted. |

`start` |
A list with the starting values of the model to be used for fitting the data. If model="all" the imput must be a list of a list with the starting values of Richards, 3P logistic, Sigmoid Emax, Gompertz, Weibull and 5P logistic model parameters. By default, the initial values are provided by self-starting functions (see argument start in |

`model` |
The nonlinear model to be used for fitting the data. Built-in models are "Richards"", "Logistic3P", "SigmEmax", "Gompertz", "Weibull" and "Logistic5P" (see argument model in |

### Value

An object with the parameter estimate for each built-in model and model averaged estimate for final size and turning point of outbreak. It is a list:

`Incidence ` |
All the available incidences |

`Time` |
All the available time points |

`PredTime` |
Period of time for which the prediction is required |

`AIC` |
The AIC for each built-in model and model averaged |

`tTable` |
A table with parameter estimates and t test. It is not available when all the model are used. |

`Weights` |
Model averaged weights. It is not availabe when is used only one built-in model |

`FinalSize` |
95% confidence interval and point estimate of the final size of outbreak for each built-in model and model averaged estimate |

`TurningPoint` |
95% confidence interval and point estimate of the turning point of outbreak for each built-in model and model averaged estimate |

`Predict` |
Predicted cumulative cases for each built-in model |

`PredictMA` |
Predicted cumulative cases for model averaged |

`PredInc` |
Predicted incidence for each built-in model |

`PredMAInc` |
Predicted incidence for model averaged |

`function.type` |
Name of the function |

`model.type` |
models used to estimate |

Generic functions such as plot and summary have methods to show the results of the fit

### Author(s)

Carlos Sebrango, Lizet Sanchez, Ziv Shkedy

### References

K. Burnham, D. R. Anderson, Model Selection and Multimodel Inference: A Practical Information - theoretic Approach,
2nd Edition, Springer-Verlag, New York, 2002.

G. Claeskens, N. L. Hjort, Model selection and model averaging, Cambridge University Press, 2008.

### Examples

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 | ```
## Not run:
## (data example 1)
data("dengueoutbreak1")
## Using only the information until time point 20
## Prediction of the final size and turning point at the end of epidemic,
## the incidence and the cumulative number of cases in the observation
## number 36 using only the Richards model
allmodelpredict(dengueoutbreak1$Incidence[1:20],dengueoutbreak1$Time[1:20],36,
model = "Richards")
## Using only the information until time point 22
## Now using all built-in model, Prediction of the final size and turning point
## at the end of epidemic, the incidence and the cumulative number of cases
## in the observation number 30
allmodelpredict(dengueoutbreak1$Incidence[1:22],dengueoutbreak1$Time[1:22],30,
model = "all")
## End(Not run)
## (data example 2)
data("dengueoutbreak2")
## Using only the information until time point 18, Prediction of the final size and
## turning point at the end of epidemic, the incidence and the cumulative number
## of cases in the observation number 31 using only the 3P logistic model
allmodelpredict(dengueoutbreak2$Incidence[1:18],dengueoutbreak2$Time[1:18],31,
model = "logistic3P")
## Not run:
## Using only the information until time point 20
## Now using all built-in model, Prediction of the final size and turning point
## at the end of epidemic, the incidence and the cumulative number of cases
## in the observation number 30
allmodelpredict(dengueoutbreak2$Incidence[1:20],dengueoutbreak2$Time[1:20],34,
model = "all")
## End(Not run)
``` |