In many settings, individual-based (i.e. "line-list") data are often unavailable or unreliable. Here we provide a model framework that relies on aggregated death counts and seroprevalence data to infer the infection fatality ratio, or the probability of death givin infection. Models are fit within a Bayesian framework and account for onset-outcome delays and serologic test characteristics.