epi.edr {epiR} R Documentation

## Estimated dissemination ratio

### Description

Computes estimated dissemination ratios on the basis of a vector of count data (usually 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?

### 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.

### Value

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

### 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.

Perez-Reche FJ, Taylor N, McGuigan C, Conaglen P, Forbes K, Strachan N, Honhold N (2021) Estimated Dissemination Ratio — A practical alternative to the reproduction number for infectious diseases. Frontiers in Public Health 9. DOI: 10.3389/fpubh.2021.675065.

### Examples

```## EXAMPLE 1:
set.seed(1234)
dat <- rpois(n = 50, lambda = 2)
dat.edr01 <- 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") + 1:50

dat.df01 <- data.frame(sdate = sdate, est = dat.edr01\$est,
low = dat.edr01\$lower, upp = dat.edr01\$upper)

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

## Not run:
library(ggplot2); library(scales)

ggplot() +
geom_line(data = dat.df01, aes(x = sdate, y = est)) +
geom_line(dat = dat.df01, aes(x = sdate, y = upp), lty = 3, size = 0.5) +
geom_line(dat = dat.df01, aes(x = sdate, y = low), lty = 3, size = 0.5) +
scale_x_date(breaks = date_breaks("1 week"),
labels = date_format("%d %b"), name = "Date") +
scale_y_continuous(trans = "log2", breaks = c(0.25,0.5,1,2,4,8,16),
limits = c(0.25,16),name = "Estimated disemination ratio (EDR)") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, size = 10)) +
geom_hline(yintercept = 1, lty = 2)

## End(Not run)
```

[Package epiR version 2.0.38 Index]