How can I measure the uncertainty interval between two groups of multigroup brms model

Here is my model

set.seed(1)

n <- 50       # number of reps/group
mu_n <- 0  # means for each group
mu_c1 <- 0.2
mu_c2 <- 0.3
mu_c3 <- 0.3
mu_c4 <- 0.4

d <- tibble(group = rep(c("normal", "covid1", "covid2", "covid3", "covid4"), each = n),
            cases = rep(c (1, 2, 3, 4, 5), each = n)) %>%
   #as.factor(cases) %>%
   mutate(y = c(rnorm(n , mean = mu_n, sd = 1),rnorm(n , mean = mu_c1, sd = 1),rnorm(n , mean = mu_c2, sd = 1),rnorm(n , mean = mu_c3, sd = 1),rnorm(n , mean = mu_c4, sd = 1)))

as.factor(d$cases)
fit <-
brm(data = d,
family = gaussian,
y ~0+ cases,
prior = c(prior(normal(0,2), class = b),
prior(student_t(3,1,1), class = sigma)),
seed =1)
sim_d_and_fit <-
function
(seed, n, probs=c(0.05, 0.95),) {
mu_n <- 0  # means for each group
mu_c1 <- 0.2
mu_c2 <- 0.3
mu_c3 <- 0.3
mu_c4 <- 0.4
set.seed(seed)
d <- tibble(group = rep(c("normal", "covid1", "covid2", "covid3", "covid4"), each = n),
            cases = rep(c (1, 2, 3, 4, 5), each = n)) %>%
   #as.factor(cases) %>%
   mutate(y = c(rnorm(n , mean = mu_n, sd = 1),rnorm(n , mean = mu_c1, sd = 1),rnorm(n , mean = mu_c2, sd = 1),rnorm(n , mean = mu_c3, sd = 1),rnorm(n , mean = mu_c4, sd = 1)))

as.factor(d$cases)

update(fit,
newdata = d,
seed = seed) %>%
fixef(probs = probs) %>%
data.frame() %>%
rownames_to_column("parameter") %>%
filter(parameter =="cases")
n_sim <-100
s1 <-
tibble(seed =1:n_sim) %>%
mutate(b1 = map(seed, sim_d_and_fit, n =60)) %>%
unnest(b1)

My question is how can I measure the UI of the difference between the normal group and covid4 group?? and what does b1 represent here?