mem (version 2.5)

memmodel: Methods for influenza modelization

Description

Function memmodel is used to calculate the threshold for influenza epidemic using historical records (surveillance rates). The method to calculate the threshold is described in the Moving Epidemics Method (MEM) used to monitor influenza activity in a weekly surveillance system.

Usage

memmodel(i.data, i.seasons = 10, i.type.threshold = 5,
  i.level.threshold = 0.95, i.tails.threshold = 1, i.type.intensity = 6,
  i.level.intensity = c(0.4, 0.9, 0.975), i.tails.intensity = 1,
  i.type.curve = 2, i.level.curve = 0.95, i.type.other = 3,
  i.level.other = 0.95, i.method = 2, i.param = 2.8, i.n.max = -1,
  i.type.boot = "norm", i.iter.boot = 10000)

Arguments

i.data

Data frame of input data.

i.seasons

Maximum number of seasons to use.

i.type.threshold

Type of confidence interval to calculate the threshold.

i.level.threshold

Level of confidence interval to calculate the threshold.

i.tails.threshold

Tails for the confidence interval to calculate the threshold.

i.type.intensity

Type of confidence interval to calculate the intensity thresholds.

i.level.intensity

Levels of confidence interval to calculate the intensity thresholds.

i.tails.intensity

Tails for the confidence interval to calculate the threshold.

i.type.curve

Type of confidence interval to calculate the modelled curve.

i.level.curve

Level of confidence interval to calculate the modelled curve.

i.type.other

Type of confidence interval to calculate length, start and percentages.

i.level.other

Level of confidence interval to calculate length, start and percentages.

i.method

Method to calculate the optimal timing of the epidemic.

i.param

Parameter to calculate the optimal timing of the epidemic.

i.n.max

Number of pre-epidemic values used to calculate the threshold.

i.type.boot

Type of bootstrap technique.

i.iter.boot

Number of bootstrap iterations.

Value

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

i.data

input data

pre.post.intervals

Pre/post confidence intervals (Threhold is the upper limit of the confidence interval).

ci.length

Mean epidemic length confidence interval.

ci.percent

Mean covered percentage confidence interval.

mean.length

Mean length.

moving.epidemics

Moving epidemic rates.

mean.start

Mean epidemic start.

epi.intervals

Epidemic levels of intensity.

typ.curve

Typical epidemic curve.

n.max

Effective number of pre epidemic values.

Details

Input data is a data frame containing rates that represent historical influenza surveillance data. It can start and end at any given 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. Parameters i.type, i.type.threshold and i.type.curve defines how to calculate confidence intervals along the process. i.type.curve is used for calculating the typical influenza curve, i.type.threshold is used to calculate the pre and post epidemic threshold and i.type is used for any other confidende interval used in the method. All three parameters must be a number between 1 and 6:

	1	Arithmetic mean and mean confidence interval.
2	Geometric mean and mean confidence interval.		3
Median and the KC Method to calculate its confidence interval.		4	Median and bootstrap confidence interval.
	5	Arithmetic mean and point confidence interval (standard deviations).
6	Geometric mean and point confidence interval (standard deviations).		1

Option 4 uses two more parameters: i.type.boot indicates which bootstrap method to use. The values are the same of those of the boot.ci function. Parameter i.iter.boot indicates the number of bootstrap samples to use. See boot for more information about this topic. Parameters i.level, i.level.threshold and i.level.curve indicates, respectively, the level of the confidence intervals described above. The i.n.max parameter indicates how many pre epidemic values to use to calculate the threshold. A value of -1 indicates the program to use an appropiate number of points depending on the number of seasons provided as input. i.tails tells the program to use 1 or 2 tailed confidence intervals when calculating the threshold (1 is recommended). Parameters i.method and i.param indicates how to find the optimal timing of the epidemics. See memtiming for details on the values this parameters can have.

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)
# Finds the timing of the first season: 2001/2002
epi<-memmodel(flucyl)
print(epi)
summary(epi)
plot(epi)