
Immunity model for vaccination and infection events with biomarker-quantity-mediated protection
Source:R/immunity_models.R
immunity_model_vacc_ifxn_biomarker_prot.Rd
This immunity model should be used if exposures represent vaccination and infection events. The probability of a successful vaccination exposure event depends on the number of vaccines received prior to time t while the probability of successful infection is dependent on the biomarker quantity at the time of exposure and the total number of successful infections prior to that point.
Usage
immunity_model_vacc_ifxn_biomarker_prot(
i,
t,
x,
exposure_histories,
biomarker_states,
demography,
biomarker_map,
model_pars,
max_events,
vacc_exposures,
vacc_age = 1,
cross_reactivity_table = NULL,
...
)
Arguments
- i
integer for the individual ID
- t
integer for the time period
- x
integer for the exposure ID
- exposure_histories
a 3D array of exposure histories for all individuals, time steps and exposure IDs
- biomarker_states
an 3D array of biomarker states (biomarker quantities) for all individuals, time steps and biomarker IDs
- demography
a tibble of demographic information for each individual in the simulation
- biomarker_map
a table specifying the relationship between exposure IDs and biomarker IDs
- model_pars
a tibble of parameters needed for the immunity model
- max_events
a vector of the maximum number of successful exposure events possible for each exposure ID
- vacc_exposures
a vector of exposure IDs (x)
- vacc_age
a vector of the minimum age at which an individual is eligible for vaccination for each exposure event; If an exposure event is not a vaccination event then input
NA
- cross_reactivity_table
an optional table which indicates cross-reactivity between exposure and biomarker quantities. Here users can specify whether other biomarker quantities are also protective against successful exposure. Defaults to NULL.
- ...
Additional arguments
Examples
tmp_exposure_history <- array(0, dim=c(1, 10, 2))
## Toy example: individual has 3 prior exposures to exposure ID 1, and none to exposure ID 2
tmp_exposure_history[1,1:3,1] <- 1
## Set all biomarker states to 3 for sake of example
tmp_biomarker_states <- array(0, dim=c(1,10,1))
tmp_biomarker_states[1,,1] <- 3
tmp_demography <- dplyr::tibble(i=1, birth=1)
tmp_pars <- reformat_biomarker_map(example_model_pars_biphasic)
## Successful exposure probability for exposure ID 1 (representing vaccination)
## is 1 or 0 depending on exposure history
immunity_model_vacc_ifxn_biomarker_prot(1,8,1,exposure_histories=tmp_exposure_history,
biomarker_states=tmp_biomarker_states, demography=tmp_demography,
biomarker_map=example_biomarker_map_numeric, model_pars=tmp_pars,
max_events=c(3),vacc_exposures=c(1),vacc_age=c(1))
#> [1] 0
## Successful exposure probability for exposure ID 2 (representing infection)
## is conditional on titer
immunity_model_vacc_ifxn_biomarker_prot(1,8,2,exposure_histories=tmp_exposure_history,
biomarker_states=tmp_biomarker_states, demography=tmp_demography,
biomarker_map=example_biomarker_map_numeric, model_pars=tmp_pars,max_events=c(3,10),
vacc_exposures=c(1),vacc_age=c(1))
#> [1] 0.9999039