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http://conacyt.repositorioinstitucional.mx/jspui/handle/1000/6928| Forecasting hospital demand during COVID-19pandemic outbreaks | |
| Marcos Aurelio Capistrán Ocampo ANTONIO CAPELLA KORT José Andrés Christen Gracia | |
| Acceso Abierto | |
| Atribución-SinDerivadas | |
| https://arxiv.org/abs/2006.01873 | |
| 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. | |
| arXiv | |
| 02-06-2020 | |
| Preimpreso | |
| https://arxiv.org | |
| Inglés | |
| Epidemia COVID-19 | |
| Investigadores Público en general | |
| VIRUS RESPIRATORIOS | |
| Versión publicada | |
| publishedVersion - Versión publicada | |
| Aparece en las colecciones: | Artículos científicos |
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| Fichero | Tamaño | Formato | |
|---|---|---|---|
| Forecasting hospital demand during COVID.pdf | 1.48 MB | Adobe PDF | Visualizar/Abrir |