# Methods for influenza trend calculation

### Description

Function `memtrend`

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

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

### Usage

1 | ```
memtrend(i.data, i.seasons = 10)
``` |

### Arguments

`i.data` |
Data frame of input data. |

`i.seasons` |
Maximum number of seasons to use. |

### Details

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. | |

### Value

`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).

`1` | Delta - Ascending parameter. | |

`2` | Eta - Descending parameter. | |

### Author(s)

Jose E. Lozano Alonso <lozalojo@jcyl.es>.

### References

Vega T., Lozano J.E. (2004) Modelling influenza epidemic - can we detect the beginning
and predict the intensity and duration? International Congress Series 1263 (2004)
281-283.

Vega T., Lozano J.E. (2012) Influenza surveillance in Europe: establishing epidemic
thresholds by the Moving Epidemic Method. Influenza and Other Respiratory Viruses,
DOI:10.1111/j.1750-2659.2012.00422.x.

### Examples

1 2 3 4 5 |