epiR (version 0.9-82)

epi.edr: Estimated dissemination ratio

Description

Computes estimated dissemination ratio on the basis of a vector of numbers (usually counts of incident cases identified on each day of an epidemic).

Usage

epi.edr(dat, n = 4, conf.level = 0.95, nsim = 99, na.zero = TRUE)

Arguments

dat

a numeric vector listing the number of incident cases for each day of an epidemic.

n

scalar, defining the number of days to be used when computing the estimated dissemination ratio.

conf.level

magnitude of the returned confidence interval. Must be a single number between 0 and 1.

nsim

scalar, defining the number of simulations to be used for the confidence interval calculations.

na.zero

logical, replace NaN or Inf values with zeros?

Value

Returns the point estimate of the EDR and the lower and upper bounds of the confidence interval of the EDR.

Details

In infectious disease epidemics the n-day estimated dissemination ratio (EDR) at day i equals the total number of incident cases between day i and day [i - (n - 1)] (inclusive) divided by the total number of incident cases between day (i - n) and day (i - 2n) + 1 (inclusive). EDR values are often calculated for each day of an epidemic and presented as a time series analysis. If the EDR is consistently less than unity, the epidemic is said to be `under control'.

A simulation approach is used to calculate confidence intervals around each daily EDR estimate. The numerator and denominator of the EDR estimate for each day is taken in turn and a random number drawn from a Poisson distribution, using the calculated numerator and denominator value as the mean. EDR is then calculated for these simulated values and the process repeated nsim times. Confidence intervals are then derived from the vector of simulated values for each day.

References

Miller W (1976). A state-transition model of epidemic foot-and-mouth disease. In: Proceedings of an International Symposium: New Techniques in Veterinary Epidemiology and Economics, University of Reading, Reading, pp. 56 - 72.

Morris R, Sanson R, Stern M, Stevenson M, Wilesmith J (2002). Decision-support tools for foot-and-mouth disease control. Revue Scientifique et Technique de l'Office International des Epizooties 21, 557 - 567.

Examples

set.seed(123)
dat <- rpois(n = 50, lambda = 2) 
edr.04 <- epi.edr(dat, n = 4, conf.level = 0.95, nsim = 99, na.zero = TRUE)
sdate <- as.Date(x = "31/12/2015", format = "%d/%m/%Y")

dat.04 <- data.frame(idate = sdate + 1:50, est = edr.04$est, 
   low = edr.04$lower, upp = edr.04$upper)

## Line plot of EDR (and its 95% confidence interval) as a function of 
## calendar time:


library(ggplot2)

ggplot(dat.04, aes(x = as.integer(idate), y = est)) + 
   geom_line() +
   geom_line(dat = dat.04, aes(x = as.integer(idate), y = upp), 
      lty = 3, size = 0.5) +
   geom_line(dat = dat.04, aes(x = as.integer(idate), y = low), 
      lty = 3, size = 0.5) +
   scale_x_continuous(name = "Date", 
      breaks = seq(from = min(as.integer(dat.04$idate)), 
         to = max(as.integer(dat.04$idate)), by = 7), 
      labels = seq(from = min(dat.04$idate), 
         to = max(dat.04$idate), by = 7), 
      limits = c(min(as.integer(dat.04$idate)), 
         max(as.integer(dat.04$idate)))) +
   scale_y_continuous(name = "Estimated disemination ratio (EDR)", 
   limits = c(0,10)) +
   theme(axis.text.x = element_text(angle = 90, vjust = 0.5, size = 10)) +
   geom_hline(yintercept = 1, lty = 2)