rsu.pfree.rs {epiR} | R Documentation |

Calculates the posterior probability (confidence) of disease freedom (negative predictive value) for one or more population sensitivity (se.p) estimates, over one or more time periods.

rsu.pfree.rs(se.p, p.intro = 0, prior = 0.5, by.time = TRUE)

`se.p` |
scalar, vector or matrix representing the population sensitivity estimates. |

`p.intro` |
scalar, vector or matrix representing the probability of disease introduction per time period. If |

`prior` |
scalar or vector of the same length as the number of rows of |

`by.time` |
logical, representing the type of analysis. See details, below. |

The `by.time`

argument is used for two specific circumstances.

Use `by.time = TRUE`

if the `se.p`

estimates are a vector of values for consecutive time periods. Use `by.time = FALSE`

if the `se.p`

estimates are a vector of multiple values (iterations) for a single time period.

Use `by.times = TRUE`

if `se.p`

is a symmetrical matrix and `p.intro`

is a vector of values representing the probability of disease introduction over consecutive time periods. Use `by.time = FALSE`

if `se.p`

is a symmetrical matrix (with columns for time periods and rows representing estimates of `se.p`

within each time period) and `p.intro`

is a vector of values corresponding to multiple values for a single time period that are the same across all periods.

A list comprised of six elements:

`PFree` |
The posterior probability of disease freedom. |

`SeP` |
The population sensitivity. |

`PIntro` |
The probability of disease introduction (as entered by the user). |

`Discounted prior` |
The discounted prior confidence of disease freedom. |

`Equilibrium PFree` |
The equilibrium probability of disease freedom. |

`Equilibrium prior` |
The equilibrium discounted prior probability of disease freedom. |

Martin P, Cameron A, Greiner M (2007). Demonstrating freedom from disease using multiple complex data sources 1: A new methodology based on scenario trees. Preventive Veterinary Medicine 79: 71 - 97.

Martin P, Cameron A, Barfod K, Sergeant E, Greiner M (2007). Demonstrating freedom from disease using multiple complex data sources 2: Case study - classical swine fever in Denmark. Preventive Veterinary Medicine 79: 98 - 115.

## EXAMPLE 1: ## You have estimated herd-sensitivity for 20 herds for a disease of concern, ## all returned negative results. What is the confidence of disease freedom ## for these herds, assuming that based on other data, 20% of herds in the ## region are estimated to be disease positive? ## Generate 20 herd sensitivity estimates, using random values between 70% ## and 95%: herd.sens <- runif(n = 20, min = 0.70, max = 0.95) ## The background herd prevalence is 0.20, so the prior confidence of freedom ## is 1 - 0.2 = 0.8. For this example we assume the prior is applicable at ## the time of sampling so p.intro = 0 (the default) and we are carrying out ## an analysis using multiple estimates of population sensitivities for a ## single time period so we set by.time = FALSE. rval.df <- rsu.pfree.rs(se.p = herd.sens, p.intro = 0, prior = 0.80, by.time = FALSE) rval.df <- data.frame(SeP = rval.df$SeP, PFree = rval.df$PFree) range(rval.df$SeP) ## The herd-level probability of disease freedom ranges from about 0.93 to ## 0.99 depending on individual herd level sensitivity values. ## EXAMPLE 2: ## You have analysed 12 months of surveillance data for disease X, to provide ## 12 monthly estimates of population sensitivity. In addition, based on ## previous data, the monthly probability of the introduction of disease is ## estimated to be in the range of 0.005 (0.5%) to 0.02 (2%). The prior ## confidence of disease freedom is assumed to be 0.5 (i.e. uninformed). ## What is your level of confidence of disease freedom at the end of the 12 ## month surveillance period? ## Generate 12, monthly estimates of se.p and p.intro: pop.sens <- runif(n = 12, min = 0.40, max = 0.70) pintro <- runif(n = 12, min = 0.005, max = 0.020) ## For this example we're analysing a single population over multiple time ## periods, so we set by.time = TRUE: rval.df <- rsu.pfree.rs(se.p = pop.sens, p.intro = pintro, prior = 0.50, by.time = TRUE) rval.df <- data.frame(mnum = 1:12, mchar = seq(as.Date("2020/1/1"), by = "month", length.out = 12), SeP = t(rval.df$SeP), PFree = t(rval.df$PFree)) ## Plot the probability of disease freedom as a function of time: plot(x = rval.df$mnum, y = rval.df$PFree, xlim = c(1,12), ylim = c(0,1), xlab = "Month", ylab = "Probability of disease freedom", pch = 16, type = "b", xaxt = "n") axis(side = 1, at = rval.df$mnum, labels = format(rval.df$mchar, format = "%b")) abline(h = 0.95, lty = 2) ## Not run: library(ggplot2); library(scales) ggplot(data = rval.df, aes(x = mchar, y =PFree)) + geom_line(col = "black") + scale_x_date(breaks = date_breaks("1 month"), labels = date_format("%b"), name = "Month") + scale_y_continuous(limits = c(0,1), name = "Probability of disease freedom") + geom_hline(yintercept = 0.95, linetype = "dashed") + theme_bw() ## End(Not run) ## The estimated probability of disease freedom (Pfree) increases over time ## from about 0.70 (or less) to >0.99, depending on the actual se.p values ## generated by simulation. ## EXAMPLE 3: ## Extending the above example, instead of a simple deterministic estimate, ## you decide to use simulation to account for uncertainty in the monthly ## se.p and p.intro estimates. ## For simplicity, we generate 1200 random estimates of se.p and coerce them ## into a matrix with 12 columns and 100 rows: pop.sens <- matrix(runif(n = 1200, min = 0.40, max = 0.70), nrow = 100) ## For p.intro we generate a vector of 100 random values, which will then be ## used across all time periods: pintro <- runif(n = 100, min = 0.005, max = 0.020) ## For this example, because se.p is a matrix and p.intro is a vector matching ## one of the dimensions of se.p, by.time is ignored: rval.df <- rsu.pfree.rs(se.p = pop.sens, p.intro = pintro, prior = 0.5, by.time = TRUE) ## Calculate 95% confidence intervals for the probability of disease freedom: rval.df <- apply(rval.df$PFree, FUN = quantile, MARGIN = 2, probs = c(0.025,0.5,0.975)) rval.df <- data.frame(mnum = 1:12, mchar = seq(as.Date("2020/1/1"), by = "month", length.out = 12), t(rval.df)) ## Plot the probability of disease freedom as a function of time. Dashed lines ## show the lower and upper bound of the confidence interval around the ## probability of disease freedom estimates: plot(x = rval.df$mnum, y = rval.df$X50., xlim = c(1,12), ylim = c(0,1), xlab = "Month", ylab = "Probability of disease freedom", type = "l", lwd = 2, xaxt = "n") axis(side = 1, at = rval.df$mnum, labels = format(rval.df$mchar, format = "%b")) lines(x = rval.df$mnum, y = rval.df$X2.5., type = "l", lty = 2) lines(x = rval.df$mnum, y = rval.df$X97.5., type = "l", lty = 2) ## Not run: library(ggplot2); library(scales) ggplot(data = rval.df, aes(x = mchar, y = X50.)) + geom_line(col = "black") + geom_ribbon(aes(ymin = X2.5., ymax = X97.5.), alpha = 0.25) + scale_x_date(breaks = date_breaks("1 month"), labels = date_format("%b"), name = "Month") + scale_y_continuous(limits = c(0,1), name = "Probability of disease freedom") + theme_bw() ## End(Not run) ## The median probability of disease freedom increases over time from about ## 0.7 (or less) to >0.99, depending on the actual se.p values generated by ## simulation.

[Package *epiR* version 2.0.31 Index]