modelling-outbreak-risk

risk-model.R

library(tidyverse)
n.sim <- 1000
lambda <- 8/45 # 8 nursing homes in 45 days


set.seed(336)
n.sim <- 1000
mu <- 20  # 20 hospitalizations mean
sigma <- 5  ## =/- 10 patients
sigma.sq <- sigma^2
beta <- mu/sigma.sq
alpha <- beta * mu
severity <- rgamma(n.sim, alpha, beta)

severity <- rmutil::rpareto(n = n.sim,m = mu, s = 100000)

severity[severity>100] <- 100

summary(severity)

(VaR.sev <- quantile(severity, alpha_tol))

frequency <- rpois(n.sim, lambda)

summary(frequency)
poisson.freq <- data.frame(Frequency = frequency,
                           Distribution = rep("Poisson", each = n.sim))
ggplot(poisson.freq, aes(x = frequency,
                         fill = Distribution)) + geom_density(alpha = 0.3)

loss <- rpois(n.sim, severity * lambda)

summary(loss)

hist(loss, breaks = 30, col = "lightgray")
loss
loss.rf <- data.frame(Loss = loss, Distribution = rep("Potential Loss",
                                                      each = n.sim))
alpha_tol <- 0.05

(VaR.loss.rf <- quantile(loss.rf$Loss,
                         1 - alpha_tol))
(ES.loss.rf <- mean(loss.rf$Loss[loss.rf$Loss >
                                   VaR.loss.rf]))