Monitoring sick leave data for early detection of influenza outbreaks

Autor: David R M Smith, Jonathan Bastard, Pearl Anne Ante-Testard, Oumou Salama Daouda, Laura Temime, Tom Duchemin, Helene Neynaud, Kévin Jean, Audrey Duval, Mounia N. Hocine, Rania Assab, Narimane Nekkab, William Dab, Radowan Lounissi, Jérôme-Philippe Garsi

Publikováno v: BMC Infectious Diseases
BMC Infectious Diseases, 2021, 21, ⟨10.1186/s12879-020-05754-5⟩
BMC Infectious Diseases, Vol 21, Iss 1, Pp 1-8 (2021)
BMC Infectious Diseases, BioMed Central, 2021, 21, ⟨10.1186/s12879-020-05754-5⟩

Background Workplace absenteeism increases significantly during influenza epidemics. Sick leave records may facilitate more timely detection of influenza outbreaks, as trends in increased sick leave may precede alerts issued by sentinel surveillance

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::443f65eebc217d45ccb18b0ddedb71c6
https://doi.org/10.1101/2020.05.28.20115782

Gradient-based adaptive importance samplers

Autor: Elvira, Víctor, Chouzenoux, Emilie, Akyildiz, Ömer Deniz, Martino, Luca

Publikováno v: Journal of The Franklin Institute
Journal of The Franklin Institute, 2023, 360 (13), pp.9490-9514. ⟨10.1016/j.jfranklin.2023.06.041⟩
Inria Saclay-Île de France. 2022

International audience; Importance sampling (IS) is a powerful Monte Carlo methodology for the approximation of intractable integrals, very often involving a target probability distribution. The performance of IS heavily depends on the appropriate se

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::65288249d5641ac1f5b55a4b7ae0f4f0
https://inria.hal.science/hal-04165344/document

Multivariate expectile-based distribution: properties, Bayesian inference, and applications

Autor: Julyan Arbel, Stéphane Girard, Hien Duy Nguyen, Antoine Usseglio-Carleve

Publikováno v: Journal of Statistical Planning and Inference
Journal of Statistical Planning and Inference, 2023, 225, pp.146-170. ⟨10.1016/j.jspi.2022.12.001⟩

International audience; Expectiles form a family of risk measures that have recently gained interest over the more common value-at-risk or return levels, primarily due to their capability to be determined by the probabilities of tail values and magni

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::aff56b9c74d8026868b04de7f22ecc5e
https://inria.hal.science/hal-03428827v2/file/main.pdf

Data-driven reconstruction of partially observed dynamical systems

Autor: Pierre Tandeo, Pierre Ailliot, Florian Sévellec

Publikováno v: Nonlinear Processes in Geophysics
Nonlinear Processes in Geophysics, 2023, 30 (2), pp.129-137. ⟨10.5194/npg-30-129-2023⟩
Nonlinear Processes In Geophysics (1023-5809) (Copernicus GmbH), 2023-06, Vol. 30, N. 2, P. 129-137
eISSN

The state of the atmosphere, or of the ocean, cannot be exhaustively observed. Crucial parts might remain out of reach of proper monitoring. Also, defining the exact set of equations driving the atmosphere and ocean is virtually impossible because of

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9823ed8ec01fab247f1ddf1d53560c47
https://imt-atlantique.hal.science/hal-04131312/document

Full inference for the anisotropic fractional Brownian field

Autor: Escande, Paul, Richard, Frédéric

The anisotropic fractional Brownian field (AFBF) is a non-stationary Gaussian random field which has been used for the modeling of textured images. In this paper, we address the open issue of estimating the functional parameters of this field, namely

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::01d584e567a1f307ecbe760e2fee85de
https://hal.science/hal-03922324/file/manuscript.pdf