calc_doses: Calculate vaccine doses received with routine vaccination

In gilesjohnr/propvacc: Functions to model and simulate vaccine dose coverage

Description

This function calculates the proportion immune using conditional probabilities. The method assumes that vaccination events are dependent, where individuals that have recieved the first dose are the most likely to recieve the second dose and those that have received both the first and second doses are the most likely to receive the third.

Usage

calc_doses(v1, v2, v3 = NULL)

Arguments

v1	a scalar giving the proportion vaccinated with first routine immunization
v2	a scalar giving the proportion vaccinated with second routine immunization
v3	a scalar giving the proportion vaccinated with third routine immunization (default = NULL)

Details

When v3 = NULL, the function uses the simpler two dose method.

Value

A dataframe containing the relative proportions of the population that have recieved 0, 1, 2, or 3 doses

Author(s)

John Giles

See Also

Other prop_vacc: calc_doses_SIA(), calc_prop_vacc_SIA(), calc_prop_vacc()

Examples

# Two dose vaccine
calc_doses(0.9, 0.9)
calc_doses(1, 0.5)
calc_doses(0.5, 0.5)

# Three dose vaccine
calc_doses(0.9, 0.9, 0.8)
calc_doses(1, 0.5, 0)
calc_doses(1, 0, 0.5)

# Should be equivalent
all.equal(calc_doses(0.999, 0.5)[,2],
          calc_doses(0.999, 0.5, 0)[2:4,2],
          tolerance=0.02)

all.equal(calc_doses(0.9, 0.8)[,2],
          calc_doses(0.9, 0.8, 0)[2:4,2],
          tolerance=0.02)

# Boundary conditions
calc_doses(0, 0)
calc_doses(1, 0)
calc_doses(1, 0, 0)
calc_doses(1, 1)
calc_doses(1, 1, 0)
calc_doses(1, 1, 1)
calc_doses(1, 0, 1)
calc_doses(0, 1, 1)

# The assumptions may not hold well when v1 << v2 << v3.
# In this case its better to assume independence?
calc_doses(0.1, 0.9)
calc_doses(0.1, 0.5, 0.9)

# Calculate total proportion vaccinated with MCV1 and MCV2 with efficacy
p <- calc_doses(0.9, 0.8)
sum(0.93*p[2,2], 0.97*p[3,2])

# Estimate posterior distribution of proportion vaccinated given uncertainty around MCV1 and MCV2
n <- 1000
sims <- rep(NA, n)
for (i in 1:n) {

     p <- calc_doses(rbeta(1,40,1), rbeta(1,4,2))
     sims[i] <- 0.93*p[2,2] + 0.97*p[3,2]
}

q <- quantile(sims, c(0.025, 0.5, 0.975))

par(mfrow=c(1,1))
hist(sims, breaks=100, col='cyan', xlab='Proportion vaccinated')
abline(v=q[2], lwd=3)
abline(v=q[c(1,3)], lty=2, lwd=2)

gilesjohnr/propvacc documentation built on Aug. 24, 2020, 3:20 a.m.