  We present a compartmental SEIRD model aimed at forecasting hospital
occupancy in metropolitan areas during the current COVID-19 outbreak. The model
features asymptomatic and symptomatic infections with detailed hospital
dynamics. We model explicitly branching probabilities and non exponential
residence times in each latent and infected compartments. Using both hospital
admittance confirmed cases and deaths we infer the contact rate and the initial
conditions of the dynamical system, considering break points to model lockdown
interventions. Our Bayesian approach allows us to produce timely probabilistic
forecasts of hospital demand. The model has been used by the federal government
of Mexico to assist public policy, and has been applied for the analysis of
more than 70 metropolitan areas and the 32 states in the country.
