Inference in epidemiological agent-based models using ensemble-based data assimilation.

Autor: Tadeo Javier Cocucci, Manuel Pulido, Juan Pablo Aparicio, Juan Ruíz, Mario Ignacio Simoy, Santiago Rosa

Publikováno v: PLoS ONE, Vol 17, Iss 3, p e0264892 (2022)

To represent the complex individual interactions in the dynamics of disease spread informed by data, the coupling of an epidemiological agent-based model with the ensemble Kalman filter is proposed. The statistical inference of the propagation of a d

Externí odkaz: https://doaj.org/article/4192be8804f0404283a2f476c32edbcc

Model error covariance estimation in particle and ensemble Kalman filters using an online expectation-maximization algorithm

Autor: Tadeo Javier Cocucci, Manuel Pulido, Magdalena Lucini, Pierre Tandeo

Publikováno v: Quarterly Journal of the Royal Meteorological Society
Quarterly Journal of the Royal Meteorological Society, Wiley, 2021, 147 (734), pp.526-543. ⟨10.1002/qj.3931⟩

The performance of ensemble-based data assimilation techniques that estimate the state of a dynamical system from partial observations depends crucially on the prescribed uncertainty of the model dynamics and of the observations. These are not usuall

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fba5011350cb93acd5f0e99ba3e0af08
https://hal-imt-atlantique.archives-ouvertes.fr/hal-02499328