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Uncertainty quantification in mechanistic epidemic models via cross-entropy approximate Bayesian computation

Autor: Cunha Jr, Americo, Barton, David A. W., Ritto, Thiago G.

Publikováno v: Nonlinear Dynamics 2023

This paper proposes a data-driven approximate Bayesian computation framework for parameter estimation and uncertainty quantification of epidemic models, which incorporates two novelties: (i) the identification of the initial conditions by using plaus

Externí odkaz: http://arxiv.org/abs/2207.12111

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ABC for model selection and parameter estimation of drill-string bit-rock interaction models and stochastic stability

Autor: Castello, Daniel A, Ritto, Thiago G

The bit-rock interaction considerably affects the dynamics of a drill string. One critical condition is the stick-slip oscillations, where torsional vibrations are high; the bit angular speed varies from zero to about two times (or more) the top driv

Externí odkaz: http://arxiv.org/abs/2206.14609

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Post-COVID-19 condition: systemic inflammation and low functional exercise capacity

Publikováno v: Frontiers in Nutrition, Vol 11 (2024)

IntroductionPost-COVID-19 condition (PCC) is characterised by a plethora of symptoms, with fatigue appearing as the most frequently reported. The alterations that drive both the persistent and post-acute disease newly acquired symptoms are not yet fu

Externí odkaz: https://doaj.org/article/47cad3b52d754942bd466160abfafb75

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Complete modeling of hydrodynamic bearings with a boundary parameterization approach

Autor: Mota, J. A., Maldonado, D. J. G., Valério, J. V., Ritto, T. G.

The present work aims to revisit the simplifications made in the Navier-Stokes equations for the flow between two cylinders with a small thickness of lubricating oil film. Through a dimensionless analysis, the terms of these equations are mapped and

Externí odkaz: http://arxiv.org/abs/2105.00118

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Data driven Dirichlet sampling on manifolds

Autor: Prado, Luan S, Ritto, Thiago G

This article presents a novel method to sampling on manifolds based on the Dirichlet distribution. The proposed strategy allows to completely respect the underlying manifold around which data is observed, and to do massive samplings with low computat

Externí odkaz: http://arxiv.org/abs/2101.00947

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Data-driven, cross-disciplinary collaboration: lessons learned at the largest academic health center in Latin America during the COVID-19 pandemic

Autor: Ana Paula Ritto, Adriana Ladeira de Araujo, Carlos Roberto Ribeiro de Carvalho, Heraldo Possolo De Souza, Patricia Manga e Silva Favaretto, Vivian Renata Boldrim Saboya, Michelle Louvaes Garcia, Leslie Domenici Kulikowski, Esper Georges Kallás, Antonio José Rodrigues Pereira, Vilson Cobello Junior, Katia Regina Silva, Eidi Raquel Franco Abdalla, Aluisio Augusto Cotrim Segurado, Ester Cerdeira Sabino, Ulysses Ribeiro Junior, Rossana Pulcineli Vieira Francisco, Anna Miethke-Morais, Anna Sara Shafferman Levin, Marcio Valente Yamada Sawamura, Juliana Carvalho Ferreira, Clovis Artur Silva, Thais Mauad, Nelson da Cruz Gouveia, Leila Suemi Harima Letaif, Marco Antonio Bego, Linamara Rizzo Battistella, Alberto José da Silva Duarte, Marilia Cerqueira Leite Seelaender, Julio Marchini, Orestes Vicente Forlenza, Vanderson Geraldo Rocha, Maria Cassia Mendes-Correa, Silvia Figueiredo Costa, Giovanni Guido Cerri, Eloisa Silva Dutra de Oliveira Bonfá, Roger Chammas, Tarcisio Eloy Pessoa de Barros Filho, Geraldo Busatto Filho

Publikováno v: Frontiers in Public Health, Vol 12 (2024)

IntroductionThe COVID-19 pandemic has prompted global research efforts to reduce infection impact, highlighting the potential of cross-disciplinary collaboration to enhance research quality and efficiency.MethodsAt the FMUSP-HC academic health system

Externí odkaz: https://doaj.org/article/8d83301c52a54c59979047782c02c604

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Digital twin, physics-based model, and machine learning applied to damage detection in structures

Autor: Ritto, TG, Rochinha, FA

This work is interested in digital twins, and the development of a simplified framework for them, in the context of dynamical systems. Digital twin is an ingenious concept that helps on organizing different areas of expertise aiming at supporting eng

Externí odkaz: http://arxiv.org/abs/2005.14360

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