6 citations · 7 across the 4 of their papers we have counts for
4 papers
Bayesian sequential data assimilation for COVID-19 forecasting
Maria L. Daza-Torres, Marcos A. Capistrán, Antonio Capella +1
We introduce a Bayesian sequential data assimilation method for COVID-19 forecasting. It is assumed that suitable transmission, epidemic and observation models are available and pr…
Filtering and improved Uncertainty Quantification in the dynamic estimation of effective reproduction numbers
Marcos A. Capistrán, Antonio Capella, J. Andrés Christen
The effective reproduction number measures an infectious disease's transmissibility as the number of secondary infections in one reproduction time in a population having both…
Uncertainty quantification for fault slip inversion
J. Cricelio Montesinos-López, Antonio Capella, J. Andrés Christen +1
We propose an efficient Bayesian approach to infer a fault displacement from geodetic data in a slow slip event. Our physical model of the slip process reduces to a multiple linear…
Forecasting hospital demand during COVID-19 pandemic outbreaks
Marcos A. Capistran, Antonio Capella, J. Andres Christen
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 sympt…