memevolution analyzes the evolution of mem estimators
memevolution( i.data, i.evolution.seasons = 10, i.evolution.method = "sequential", ... )
Data frame of input data.
Maximum number of seasons to use.
method to calculate evolution.
other parameters passed to memmodel.
Shows the evolution of some indicators across time. The indicators are: duration of the epidemic, start of the epidemic, epidemic percentage, pre-epidemic, post-epidemic and intensity thresholds.
Values per season are the estimates of the indicator for this specific season calculated according the memmodel options: cross or sequential (validation option), maximum number of seasons to use in calculations. In memmodel is it also possible to select the method used to calculate indicators and the level of confidence of the confidence intervals.
To clarify how it is calculated, the
evolution.seasons showss for each x-axis point the seasons
selected to calculate thresholds. Each row is a different season and in each column it is shown if that
specific season has been used to calculate the indicators. If
TRUE, it has been used, if
NO, it hasn't.
In the last row, called Next, the indicators are calculated for the upcoming next season.
Note that if you select sequential validation, first points in the graphs calculate thresholds using less data (there are less seasons before the current data than the max. seasons value), and this will affect the range of confidence intervals.
There are four indicators:
Duration: Average duration of the epidemic and its confidence interval.
Start: Average start of the epidemic and its confidence interval.
Epidemic percentage: The sum of the values (cases/rates) in the epidemic period divided by the total sum of values of the whole surveillance period. It's a coverage percentage of the epidemic period. And its confidence interval.
Thresholds: Pre-epidemic, post-epidemic and intensity thresholds.
The confidence intervals for these indicators are calculated using the other CI and other CI. level parameters of memmodel.
memevolution returns a list.
A list containing at least the following components:
evolution.data data for each value analysed.
Jose E. Lozano email@example.com
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
# Castilla y Leon Influenza Rates data data(flucyl) # evolution of estimators evolution <- memevolution(flucyl) evolution$evolution.data
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