This package creates the model described in the Moving Epidemics Method (MEM), used to monitor influenza activity during the seasonal surveillance.
|Title:||Moving Epidemics Method R Package.|
|Author:||Jose E. Lozano Alonso <email@example.com>|
|Maintainer:||Jose E. Lozano Alonso <firstname.lastname@example.org>|
|Depends:||R (>= 3.2.0)|
|Description:||Modelization of influenza epidemics in order to monitor future activity.|
|License:||GPL (>= 2)|
The Moving Epidemics Method (MEM) is a tool developed in the Health Sentinel Network of Castilla y Leon (Spain) to help in the routine influenza surveillance in health systems. It gives a better understanding of the annual influenza epidemics and allows the weekly assessment of the epidemic status and intensity.
Although in its conception it was originally created to be used with influenza data and health sentinel networks, MEM has been tested with different respiratory infectious diseases and surveillance systems so nowadays it could be used with any parameter which present a seasonal accumulation of cases that can be considered an epidemic. MEM development started in 2001 and the first record appeared in 2003 in the Options for the Control of Influenza V. It was presented to the baselines working group of the European Influenza Surveillance Scheme (EISS) in the 12th EISS Annual Meeting (Malaga, Spain, 2007), with whom started a collaboration that continued when, in 2008, was established the European Influenza Surveillance Network.
In 2009 MEM is referenced in an official European document: the Who European guidance for influenza surveillance in humans. A year later MEM was implemented in The European Surveillance System (TESSy), of the European Centre for Disease Prevention and Control (ECDC), and in 2012, after a year piloting, in the EuroFlu regional influenza surveillance platform, of the World Health Organization Regional Office for Europe (WHO-E).
As a result of the collaboration with ECDC and WHO-E, two papers have been published, one related to the establishment of epidemic thresholds and other to the comparison of intensity levels in Europe.
In 2014 a tool was created to help users around the world to apply mem on their data. It was released in July 2014 as a package for R, a free software environment for statistical computing and graphics. This is the first version of the library (also referred to as the stable version).
The second version of the mem R library was released in 2015 and included a lot of new features and graphics. It was published as an open source project at GitHub, a web-based Git or version control repository and Internet hosting service. It is available directly from github and it is also known as the development version since it is constantly being updated.
This version was incorporated to the official R repositories, The Comprehensive R Archive Network (CRAN), in June 2017. In 2017 a web application was created to serve as a graphical user interface for the R mem library using Shiny, a web application framework for R. This application is based on the development version of the mem R library. It is hosted at GitHub and also at CRAN.
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
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# Castilla y Leon Influenza Rates data data(flucyl) # Optimal timing of an epidemic tim<-memtiming(flucyl) print(tim) summary(tim) plot(tim) # Threshold calculation epi<-memmodel(flucyl[1:7]) print(epi) summary(epi) plot(epi) # Intensity thresholds intensity<-memintensity(epi) intensity # Trend parameters trend<-memtrend(epi) trend # Epidemic thresholds e.thr<-epi$epidemic.thresholds # Intensity threhsolds i.thr<-epi$intensity.thresholds # Surveillance memsurveillance(flucyl,e.thr,i.thr,i.graph.file=FALSE)
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