calc_doses_SIA: Calculate vaccine doses received with routine vaccination and...
In gilesjohnr/propvacc: Functions to model and simulate vaccine dose coverage

Description Usage Arguments Details Value Author(s) See Also Examples

View source: R/funcs.R

This function calculates the proportion immune by calculating the conditional probability of routine vaccination with two- or three-dose routine coverage and one SIA campaign. 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. Receipt of a dose through the SIA campaign is dependent upon vaccination with at least one dose prior to the SIA campaign.

1	calc_doses_SIA(v1, v2, v3 = NULL, S)

`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)
`S`	a scalar giving the proportion vaccinated with supplemental campaign

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

A dataframe containing the relative proportions of the population that have recieved 0, 1, 2, 3, or 4 doses

John Giles

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

calc_doses_SIA(v1=0.9, v2=0.8, S=0.97) # High coverage two-dose routine and campaign

calc_doses_SIA(v1=0.9, v2=0.85, v3=0.8, S=0.95) # High coverage two-dose routine and campaign

calc_doses_SIA(v1=0.5, v2=0.5, v3=0.5, S=0.95) # Low routine coverage and high campaign

# Boundary conditions
calc_doses_SIA(v1=1, v2=1, v3=1, S=1)
calc_doses_SIA(v1=1, v2=0, v3=0, S=0)

# Complex models reduce to simpler models
calc_doses_SIA(v1=0.9, v2=0.8, v3=0.7, S=0)
calc_doses(v1=0.9, v2=0.8, v3=0.7)

calc_doses_SIA(v1=0.9, v2=0, v3=0, S=0)
calc_doses(v1=0.9, v2=0, v3=0)
calc_doses(v1=0.9, v2=0)