Epidimiology module SEIR model why the observation follow distribution binomial_dist(S2E, rho), what is the meaning of rho

1 
# Condition on observations.
        t_is_observed = isinstance(t, slice) or t < self.duration
        pyro.sample(
            "obs_{}".format(t),
            binomial_dist(S2E, rho),
            obs=self.data[t] if t_is_observed else None,
        )

what is the meaning of rho here? We’re observing new infections; logically, shouldn’t these new infections align with the E2I transition?

Thanks

2

Hi @superhaoshuai,

I believe rho is probability of observing an infected individuals. I believe S2E measures newly infected (i.e. “exposed”) individuals, whereas E2S measures newly contagious (i.e. “infectious”) individuals.