mem-package: Moving Epidemic Method R Package

Description Details Value Author(s) References Examples

Description

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

Details

Package: mem
Type: Package
Title: Moving Epidemics Method R Package.
Version: 1.4
Date: 2014-07-10
Author: Jose E. Lozano Alonso <[email protected]>
Maintainer: Jose E. Lozano Alonso <[email protected]>
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.

https://github.com/lozalojo/mem

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.

https://github.com/lozalojo/memapp

Value

NULL

Author(s)

Jose E. Lozano [email protected]

References

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

Examples

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# 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)

Example output

Call:
memtiming(i.data = flucyl[1])

Optimum:
[1] 10

Timing:
[1] 14 23
Call:
memtiming(i.data = flucyl[1])

Optimum:
[1]   10.00000   89.49422 2594.10739

Timing:
[1] 14 23

Pre-epidemic values:
[1] 39.24867 35.93245 29.49091 28.52660 14.44930

Post-epidemic values:
[1] 47.364011 20.966998 16.993075  5.369704  3.500053
Call:
memmodel(i.data = flucyl[1:7])

Epidemic threshold:
            Pre  Post
Threshold 50.41 62.59

Intensity thresholds:
                  Threshold
Medium (40%)         251.49
High (90%)           459.94
Very high (97.5%)    600.60
Call:
memmodel(i.data = flucyl[1:7])

Parameters:
	- General:
		+ Number of seasons restriction:  Restricted to 10 
		+ Number of seasons used:  7 
		+ Seasons used:  2001/2002,2002/2003,2003/2004,2004/2005,2005/2006,2006/2007,2007/2008 
		+ Number of weeks:  33 
	- Confidence intervals:
		+ Epidemic threshold:  Arithmetic mean and its one sided 95% CI using 2*SD 
		+ Intensity:  Geometric mean and its one sided 40,90,97.5% CI using (log) 2*SD 
		+ Curve:  Geometric mean and its two sided 95% CI using the (log) normal approximation 
		+ Others:  Median and its two sided 95% CI using the KC Method 
	- Epidemic timing calculation:
		+ Method:  2 
		+ Parameter:  2.8 
	- Epidemic threshold calculation:
		+ Pre-epidemic values:  Optimized: 4 
		+ Tails of CI:  1 
	- Intensity thresholds calculation:
		+ Number of values:  Optimized: 4 
		+ Tails of CI:  1 
		+ Levels of CI:  Medium: 40% High: 90% Very high: 97.5% 
	- Bootstrap (if used):
		+ Technique:  norm 
		+ Bootstrap samples:  10000 

Epidemic description:
	- Typical influenza season lasts  10  weeks.  95 %CI 	[ 8 , 16 ]
	- This optimal  10  weeks influenza season includes the 85.3 % of the total sum of rates


Epidemic threshold:
            Pre  Post
Threshold 50.41 62.59


Intensity thresholds:
                  Threshold
Medium (40%)         251.49
High (90%)           459.94
Very high (97.5%)    600.60
$intensity.thresholds
                     Epidemic Medium (40%) High (90%) Very high (97.5%)
Intensity Thresholds 50.41495     251.4922   459.9445          600.6025

$param.i.flu
Call:
memmodel(i.data = flucyl[1:7])

Epidemic threshold:
            Pre  Post
Threshold 50.41 62.59

Intensity thresholds:
                  Threshold
Medium (40%)         251.49
High (90%)           459.94
Very high (97.5%)    600.60

$call
memintensity(i.flu = epi)

$trend.thresholds
                 Ascending Threshold Descending Threshold
Trend Thresholds            43.38746            -40.52415

$param.flu
Call:
memmodel(i.data = flucyl[1:7])

Epidemic threshold:
            Pre  Post
Threshold 50.41 62.59

Intensity thresholds:
                  Threshold
Medium (40%)         251.49
High (90%)           459.94
Very high (97.5%)    600.60

$param.type
[1] 1

$param.level
[1] 0.95

$param.type.boot
[1] "norm"

$param.iter.boot
[1] 10000

$call
memtrend(i.flu = epi)

$graph.name
[1] "./mem surveillance graph.tiff"

$current.season
   nombre.semana numero.semana      rates season.scheme
1             40             1  39.996000             1
2             41             2  12.558607             1
3             42             3   3.807783             1
4             43             4  16.697975             1
5             44             5   4.043672             1
6             45             6  11.343442             1
7             46             7  31.229262             1
8             47             8  36.516341             1
9             48             9  17.719814             1
10            49            10  61.479347             2
11            50            11  77.432677             2
12            51            12 277.897561             2
13            52            13 250.777876             2
14             1            14 205.603902             2
15             2            15 284.820141             2
16             3            16 183.801005             2
17             4            17 109.911678             2
18             5            18  80.599944             2
19             6            19  57.228700             3
20             7            20  61.851919             3
21             8            21  38.898397             3
22             9            22  29.439906             3
23            10            23  19.074505             3
24            11            24  24.102193             3
25            12            25   3.673095             3
26            13            26   8.179959             3
27            14            27   8.694896             3
28            15            28   4.071164             3
29            16            29   0.000000             3
30            17            30   4.632847             3
31            18            31   0.000000             3
32            19            32   0.000000             3
33            20            33   0.000000             3

$real.start.week
[1] 10

$forced.start.week
[1] NA

$start.week
[1] 10

$end.week
[1] 19

$param.current
    2008/2009
40  39.996000
41  12.558607
42   3.807783
43  16.697975
44   4.043672
45  11.343442
46  31.229262
47  36.516341
48  17.719814
49  61.479347
50  77.432677
51 277.897561
52 250.777876
1  205.603902
2  284.820141
3  183.801005
4  109.911678
5   80.599944
6   57.228700
7   61.851919
8   38.898397
9   29.439906
10  19.074505
11  24.102193
12   3.673095
13   8.179959
14   8.694896
15   4.071164
16   0.000000
17   4.632847
18   0.000000
19   0.000000
20   0.000000

$param.epidemic.thresholds
[1] 50.41495 62.59166

$param.intensity.thresholds
[1] 251.4922 459.9445 600.6025

$param.mean.length
[1] 10

$param.force.length
[1] FALSE

$param.output
[1] "."

$param.graph.title
[1] ""

$param.graph.file
[1] FALSE

$param.graph.file.name
[1] ""

$param.week.report
[1] NA

$param.equal
[1] FALSE

$param.pos.epidemic
[1] FALSE

$param.no.epidemic
[1] FALSE

$param.no.intensity
[1] FALSE

$param.epidemic.start
[1] NA

$param.range.x
[1] 40 20

$param.range.x.53
[1] FALSE

$param.range.y
[1] NA

$param.no.labels
[1] FALSE

$param.start.end.marks
[1] TRUE

$call
memsurveillance(i.current = flucyl[8], i.epidemic.thresholds = e.thr, 
    i.intensity.thresholds = i.thr, i.graph.file = FALSE)

mem documentation built on Nov. 17, 2017, 5:26 a.m.