mem (version 2.5)

mem-package: Moving Epidemic Method R Package

Description

This package creates the model described in the Moving Epidemics Method (MEM), used to monitor influenza activity during the seasonal surveillance.

Value

NULL

Details

Package:	mem
Type:	Package
Title:	Moving Epidemics Method R Package.
Version:	1.4
Date:	2014-07-10
Author:	Jose E. Lozano Alonso <[e-mail]>
Maintainer:	Jose E. Lozano Alonso <[e-mail]>
Depends:	R (>= 3.2.0)
Description:	Modelization of influenza epidemics in order to monitor future activity.
License:	GPL (>= 2)

Functions to calculate the optimal timing of the epidemic and a threshold to give an early alert of the upcoming epidemic.

References

Vega Alonso, Tomas, Jose E Lozano Alonso, Raul Ortiz de Lejarazu, and Marisol Gutierrez Perez. 2004. Modelling Influenza Epidemic: Can We Detect the Beginning and Predict the Intensity and Duration? International Congress Series, Options for the Control of Influenza V. Proceedings of the International Conference on Options for the Control of Influenza V, 1263 (June): 281-83. doi:10.1016/j.ics.2004.02.121. Vega, Tomas, Jose Eugenio Lozano, Tamara Meerhoff, Rene Snacken, Joshua Mott, Raul Ortiz de Lejarazu, and Baltazar Nunes. 2013. Influenza Surveillance in Europe: Establishing Epidemic Thresholds by the Moving Epidemic Method. Influenza and Other Respiratory Viruses 7 (4): 546-58. doi:10.1111/j.1750-2659.2012.00422.x. Vega, Tomas, Jose E. Lozano, Tamara Meerhoff, Rene Snacken, Julien Beaute, Pernille Jorgensen, Raul Ortiz de Lejarazu, et al. 2015. Influenza Surveillance in Europe: Comparing Intensity Levels Calculated Using the Moving Epidemic Method. Influenza and Other Respiratory Viruses 9 (5): 234-46. doi:10.1111/irv.12330.

Examples

# Castilla y Leon Influenza Rates data
data(flucyl)
# Optimal timing of an epidemic
tim<-memtiming(flucyl[1])
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[8],e.thr,i.thr,i.graph.file=FALSE)