Decide on how to account for high risk population vacc

[shauntruelove]

Options:

1. Explicitly include low/high risk groups by age group in full structure (i.e., in SEIR. Do not need to do low/high for 65+ group)

   • computationally more intensive?
   • requires adjusting low risk group outcome parameters
   • requires more parameters?

2. Adjust vaccination rates to account for proportion low/high in each age group

   • requires vaccination rate adjustment (and a catch to make sure not vaccinating too many)
   • also requires adjustment of outcomes in low risk (unvaccinated) group

[saraloo]

Leaning towards doing (2) here - seems more straightforward.

Also noticed this in a vaccination script, though it isn't used...

state_pop_agestrat_50 <- get_state_pop_agestrat(
    round_num = round_num,
    age_groups = c("0_17", "18_49","50_64", "65_100"),
    state_pop_file = "data/geodata_2019.csv",
    state_pop_1yr_file = "data/state_age_pops.csv") %>% suppressMessages() %>%
    group_by(USPS) %>%
    mutate(popadj = pop[age_group=="50_64"] / (pop[age_group=="18_49"] + pop[age_group=="50_64"])) %>%
    ungroup() %>% 
    select(USPS, subpop, popadj) %>% distinct() %>% 
    mutate(age_group = "18_64") %>%
    mutate(popadj = popadj + (1-popadj)*0.20) # 50+ and 20% chronic conditions for 18-49
state_pop_agestrat_50 <- state_pop_agestrat_50 %>%
    bind_rows(state_pop_agestrat_50 %>% mutate(age_group="65_100", popadj=1)) %>%
    bind_rows(state_pop_agestrat_50 %>% mutate(age_group="0_17", popadj=0))

Has this been attempted before?

[saraloo]

Decided to model high risk in 18-64 age group as separate 'age' compartment. Will need to have adjustments to outcome probabilities.