We present a forecasting model aim to predict hospital occupancy in metropolitan areas during the current COVID-19 pandemic. Our SEIRD type 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 breakpoints to model lockdown interventions and the increase in effective population size due to lockdown relaxation. The latter features let us model lockdown-induced 2nd waves. Our Bayesian approach allows us to produce timely probabilistic forecasts of hospital demand. We have applied the model to analyze more than 70 metropolitan areas and 32 states in Mexico.

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branching probabilities forecasting model infections hospital admittance confirmed dynamical system considering lockdowninduced 2nd probabilistic forecasts of hospital demand effective population size predict hospital occupancy contact rate bayesian approach lockdown interventions initial conditions nonexponential residence times

Christen, J. Andres,Capistran, Marcos A, Capella, Antonio,.Forecasting hospital demand in metropolitan areas during the current COVID-19 pandemic and estimates of lockdown-induced 2nd waves. (),.

Christen, J. Andres,Capistran, Marcos A, Capella, Antonio,"Forecasting hospital demand in metropolitan areas during the current COVID-19 pandemic and estimates of lockdown-induced 2nd waves"

Christen, J. Andres,Capistran, Marcos A, Capella, Antonio,"Forecasting hospital demand in metropolitan areas during the current COVID-19 pandemic and estimates of lockdown-induced 2nd waves"