memintensity: Thresholds for influenza intensity

Description Usage Arguments Details Value Author(s) References Examples

View source: R/memintensity.R


Function memintensity is used to calculate the thresholds for influenza activity using historical records (surveillance rates).





An object of class mem.


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.

MEM is used to locate the epidemic for each season. Then confidence intervals are calculated at different levels.

The parameter i.levels, define the three levels of the confidence intervals used to calculate thresholds.

The i.n.max parameter indicates how many epidemic values to use to calculate the thresholds. A value of -1 indicates the program to use an appropiate number of points depending on the number of seasons provided as input.

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.

Intensity thresholds and Epidemic threshold defines 5 levels of intensity:

  1. Baseline level - Below epidemic threshold.

  2. Low level - Above epidemic threshold and below low intensity threshold.

  3. Medium level - Above low intensity threshold and below medium intensity threshold.

  4. High level - Above medium intensity threshold and below high intensity threshold.

  5. Very high level - Above high intensity threshold.


memintensity returns a list with three objects, two of them are the parameters used (param.levels and param.seasons) and the third one (intensity.thresholds) is a matrix 1x4 with the epidemic and intensity thresholds.

  1. Epidemic threshold.

  2. Low intensity threshold.

  3. Medium intensity threshold.

  4. High intensity threshold.


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: DOI:10.5281/zenodo.165983


# Castilla y Leon Influenza Rates data
# mem model
# Calculates intensity thresholds

mem documentation built on June 2, 2018, 1:04 a.m.