About: Using posterior predictive distributions to analyse epidemic models: COVID-19 in Mexico City

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Using posterior predictive distributions to analyse epidemic models: COVID-19 in Mexico City
Creator	Velasco-Hernandez, Jorge Castillo, Isaac Benet, Luis Boyer, Denis Carranco-Sapiéns, Gabriel Mantilla-Beniers, Natalia Mena, Ramsés
source	ArXiv
abstract	Epidemiological models contain a set of parameters that must be adjusted based on available observations. Once a model has been calibrated, it can be used as a forecasting tool to make predictions and to evaluate contingency plans. It is customary to employ only point estimators for such predictions. However, some models may fit the same data reasonably well for a broad range of parameter values, and this flexibility means that predictions stemming from such models will vary widely, depending on the particular parameter values employed within the range that give a good fit. When data are poor or incomplete, model uncertainty widens further. A way to circumvent this problem is to use Bayesian statistics to incorporate observations and use the full range of parameter estimates contained in the posterior distribution to adjust for uncertainties in model predictions. Specifically, given the epidemiological model and a probability distribution for observations, we use the posterior distribution of model parameters to generate all possible epidemiological curves via the posterior predictive distribution. From the envelope of all curves one can extract the worst-case scenario and study the impact of implementing contingency plans according to this assessment. We apply this approach to the potential evolution of COVID-19 in Mexico City and assess whether contingency plans are being successful and whether the epidemiological curve has flattened.
has issue date	2020-05-05(xsd:dateTime)
has license	arxiv
sha1sum (hex)	c0652bad850400bce6b777fbf2eac89c068fe304
resource representing a document's title	Using posterior predictive distributions to analyse epidemic models: COVID-19 in Mexico City
resource representing a document's body	covid:c0652bad850400bce6b777fbf2eac89c068fe304#body_text
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