Influenza Epidemic Timing

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Description

Function epitiming is used to find the optimal timing of an influenza epidemic in a set of weekly influenza surveillance rates. It provides the start and the end of the epidemic, also it returns a list of pre-epidemic and post-epidemic rates that can be used to calculate influenza baselines and thresholds.
The method to calculate the optimal timing of an epidemic is described as part of the Moving Epidemics Method (MEM), used to monitor influenza activity in a weekly surveillance system.

Usage

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epitiming(i.data, i.n.values = 5, i.method = 2, i.param = 2.8)
## S3 method for class 'epidemic'
print(x, ...)
## S3 method for class 'epidemic'
summary(object, ...)
## S3 method for class 'epidemic'
plot(x, ...)

Arguments

i.data

a numeric object (or one that can be coerced to that class).

i.n.values

a number, which indicates how many pre-epidemic values are taken from the pre-epidemic period.

i.method

a number from 1 to 4, to select which optimization method to use.

i.param

an optional parameter used by the method.

x

An epidemic class item.

object

An epidemic class item.

...

Not used.

Details

Input data is a vector of rates that represent a full influenza surveillance season. It can start and end at any 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.
The i.n.values parameter is used to get information from the pre-epidemic and post-epidemic period. The function will extract the highest pre/post values in order to use it later to calculate other influenza indicators, such as baseline activity or threshold for influenza epidemic. Depending of the value i.method, the function will use a different method to calculate the optimum epidemic timing.

[1] original method
[2] fixed criterium method
[3] slope method
[4] second derivative method

All methods are based upon the MAP curve, as described in the MEM Method.
The original method uses the process shown in the original paper, which describes the MEM as it was created. The fixed criterium method is an update of the MEM that uses the slope of the MAP curve fo find the optimum, which is the point where the slope is lower than a predefined value. The slope method also calculates the slope of the MAP curve, but the optimum is the one that matches the global/mean slope. The second derivative method calculates the second derivative and equals to zero to search an inflexion point in the original curve.
Two of the four methods require an additional parameter i.param: for the fixed criterium method is the predefined value to find the optimum, which typically is 2.5-3.0%, and for the original method it is needed the window parameter to smooth the map curve. A value of -1 indicates it should use h.select to select the window parameter. See sm for more information about this topic.

Value

epitiming returns an object of class epidemic. An object of class epidemic is a list containing at least the following components:

i.data

input data

map.curve

MAP curve

optimum.map

optimum

pre.epi

pre-epidemic highest rates

post.epi

post-epidemic highest rates

Author(s)

Jose E. Lozano Alonso <lozalojo@jcyl.es>.

References

Vega T., Lozano J.E. (2004) Modelling influenza epidemic - can we detect the beginning and predict the intensity and duration? International Congress Series 1263 (2004) 281-283.
Vega T., Lozano J.E. (2012) Influenza surveillance in Europe: establishing epidemic thresholds by the Moving Epidemic Method. Influenza and Other Respiratory Viruses, DOI:10.1111/j.1750-2659.2012.00422.x.

Examples

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## Castilla y Leon Influenza Rates data 
data(flucyl)
## Finds the timing of the first season: 2001/2002
tim<-epitiming(flucyl[1])
print(tim)
summary(tim)
plot(tim)