Description Usage Arguments Details Value Author(s) References Examples

Function `memtrend`

is used to calculate the two parameters for defining the
current influenza trend.

1 2 | ```
memtrend(i.flu, i.type = 1, i.level = 0.95, i.type.boot = "norm",
i.iter.boot = 10000)
``` |

`i.flu` |
An object of class |

`i.type` |
Type of confidence interval to calculate the trend thresholds. |

`i.level` |
Level of confidence interval to calculate the trend thresholds. |

`i.type.boot` |
Type of bootstrap technique. |

`i.iter.boot` |
Number of bootstrap iterations. |

This method is based on the Moving Epidemics Method (MEM) used to monitor influenza activity in a weekly surveillance system.

Input data is a data frame containing rates that represent historical influenza surveillance data. It can start and end at any given week (tipically at week 40th), and rates can be expressed as per 100,000 inhabitants (or per consultations, if population is not available) or any other scale.

The `i.seasons`

parameter indicates how many seasons are used for calculating
thresholds. A value of -1 indicates the program to use as many as possible. If there
are less than this parameter, the program used all seasons avalaible.

There are three different states for trend, to determine the state, the current rate and the difference of the current and last weekly rate are needed:

2 Ascending - When the weekly rate is above the epidemic threshold and the difference of the current and last weekly rate is higher than Delta OR this is the first time the rate is above the epidemic threshold.

3 Descending - When the weekly rate is above the epidemic threshold and the difference of the current and last weekly rate is lower than Eta OR this is the first time the rate is below the epidemic threshold after having been above it.

1 Stable - Otherwise.

`memtrend`

returns a list with two objects, the first one is the parameter used in
the calculations (`param.seasons`

) and the second one (`trend.thresholds`

) is
a matrix 1x2 with the Ascending (Delta) and Descending parameters (Eta).

Delta - Ascending parameter.

Eta - Descending parameter.

Jose E. Lozano [email protected]

Vega T, Lozano JE, Ortiz de Lejarazu R, Gutierrez Perez M. Modelling influenza epidemic - can we detect the beginning and predict the intensity and duration? Int Congr Ser. 2004 Jun;1263:281-3.

Vega T, Lozano JE, Meerhoff T, Snacken R, Mott J, Ortiz de Lejarazu R, et al. Influenza surveillance in Europe: establishing epidemic thresholds by the moving epidemic method. Influenza Other Respir Viruses. 2013 Jul;7(4):546-58. DOI:10.1111/j.1750-2659.2012.00422.x.

Vega T, Lozano JE, Meerhoff T, Snacken R, Beaute J, Jorgensen P, et al. Influenza surveillance in Europe: comparing intensity levels calculated using the moving epidemic method. Influenza Other Respir Viruses. 2015 Sep;9(5):234-46. DOI:10.1111/irv.12330.

Lozano JE. lozalojo/mem: Second release of the MEM R library. Zenodo [Internet]. [cited 2017 Feb 1]; Available from: https://zenodo.org/record/165983. DOI:10.5281/zenodo.165983

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