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176 results on '"Prague, Mélanie"'

6. Parameter estimation in nonlinear mixed effect models based on ordinary differential equations: an optimal control approach

Author

Clairon, Quentin, Pasin, Chloé, Balelli, Irene, Thiébaut, Rodolphe, and Prague, Mélanie

Subjects
Statistics - Methodology
Abstract

We present a parameter estimation method for nonlinear mixed effect models based on ordinary differential equations (NLME-ODEs). The method presented here aims at regularizing the estimation problem in presence of model misspecifications, practical identifiability issues and unknown initial conditions. For doing so, we define our estimator as the minimizer of a cost function which incorporates a possible gap between the assumed model at the population level and the specific individual dynamic. The cost function computation leads to formulate and solve optimal control problems at the subject level. This control theory approach allows to bypass the need to know or estimate initial conditions for each subject and it regularizes the estimation problem in presence of poorly identifiable parameters. Comparing to maximum likelihood, we show on simulation examples that our method improves estimation accuracy in possibly partially observed systems with unknown initial conditions or poorly identifiable parameters with or without model error. We conclude this work with a real application on antibody concentration data after vaccination against Ebola virus coming from phase 1 trials. We use the estimated model discrepancy at the subject level to analyze the presence of model misspecification.

Published
2021

7. Neutrophil Activation and Immune Thrombosis Profiles Persist in Convalescent COVID-19

Author

Hocini, Hakim, Wiedemann, Aurélie, Blengio, Fabiola, Lefebvre, Cécile, Cervantes-Gonzalez, Minerva, Foucat, Emile, Tisserand, Pascaline, Surenaud, Mathieu, Coléon, Séverin, Prague, Mélanie, Guillaumat, Lydia, Krief, Corinne, Fenwick, Craig, Laouénan, Cédric, Bouadma, Lila, Ghosn, Jade, Pantaleo, Giuseppe, Thiébaut, Rodolphe, Lévy, Yves, Abel, Laurent, Abrous, Amal, Andrejak, Claire, Angoulvant, François, Bachelet, Delphine, Bartoli, Marie, Behilill, Sylvie, Beluze, Marine, Bhavsar, Krishna, Chair, Anissa, Charpentier, Charlotte, Chenard, Léo, Chirouze, Catherine, Couffin-cadiergues, Sandrine, Couffignal, Camille, Castro, Nathalie DE., Debray, Marie-Pierre, Deplanque, Dominique, Descamps, Diane, Diallo, Alpha, Silva, Fernanda Dias DA, Dorival, Céline, Duval, Xavier, Eloy, Philippine, Enouf, Vincent, Esperou, Hélène, Esposito-farese, Marina, Etienne, Manuel, Florence, Aline-Marie, Gaymard, Alexandre, Gigante, Tristan, Gilg, Morgane, Goehringer, François, Guedj, Jérémie, Houas, Ikram, Hoffmann, Isabelle, Hulot, Jean-Sébastien, Jaafoura, Salma, Jamard, Simon, Kafif, Ouifiya, Khalil, Antoine, Lafhej, Nadhem, Laribi, Samira, Le, Minh, Hingrat, Quentin LE., Mestre, Soizic LE., Letrou, Sophie, Lina, Bruno, Lingas, Guillaume, Malvy, Denis, Mentré, France, Mouquet, Hugo, Neant, Nadège, Paul, Christelle, Papadopoulos, Aurélie, Petrov-sanchez, Ventzislava, Peytavin, Gilles, Piquard, Valentine, Picone, Olivier, Rosa-calatrava, Manuel, Rossignol, Bénédicte, Rossignol, Patrick, Roy, Carine, Schneider, Marion, Tardivon, Coralie, Timsit, Jean-François, Tubiana, Sarah, Werf, Sylvie VAN. DER., and Visseaux, Benoit

Published
2023
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8. EpidemiOptim: A Toolbox for the Optimization of Control Policies in Epidemiological Models

Author

Colas, Cédric, Hejblum, Boris, Rouillon, Sébastien, Thiébaut, Rodolphe, Oudeyer, Pierre-Yves, Moulin-Frier, Clément, and Prague, Mélanie

Subjects
Computer Science - Machine Learning, Mathematics - Optimization and Control, Quantitative Biology - Populations and Evolution
Abstract

Epidemiologists model the dynamics of epidemics in order to propose control strategies based on pharmaceutical and non-pharmaceutical interventions (contact limitation, lock down, vaccination, etc). Hand-designing such strategies is not trivial because of the number of possible interventions and the difficulty to predict long-term effects. This task can be cast as an optimization problem where state-of-the-art machine learning algorithms such as deep reinforcement learning, might bring significant value. However, the specificity of each domain -- epidemic modelling or solving optimization problem -- requires strong collaborations between researchers from different fields of expertise. This is why we introduce EpidemiOptim, a Python toolbox that facilitates collaborations between researchers in epidemiology and optimization. EpidemiOptim turns epidemiological models and cost functions into optimization problems via a standard interface commonly used by optimization practitioners (OpenAI Gym). Reinforcement learning algorithms based on Q-Learning with deep neural networks (DQN) and evolutionary algorithms (NSGA-II) are already implemented. We illustrate the use of EpidemiOptim to find optimal policies for dynamical on-off lock-down control under the optimization of death toll and economic recess using a Susceptible-Exposed-Infectious-Removed (SEIR) model for COVID-19. Using EpidemiOptim and its interactive visualization platform in Jupyter notebooks, epidemiologists, optimization practitioners and others (e.g. economists) can easily compare epidemiological models, costs functions and optimization algorithms to address important choices to be made by health decision-makers.

Published
2020
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9. Robust and Efficient Optimization Using a Marquardt-Levenberg Algorithm with R Package marqLevAlg

Author

Philipps, Viviane, Hejblum, Boris P, Prague, Mélanie, Commenges, Daniel, and Proust-Lima, Cécile

Subjects
Statistics - Computation
Abstract

Implementations in R of classical general-purpose algorithms for local optimization generally have two major limitations which cause difficulties in applications to complex problems: too loose convergence criteria and too long calculation time. By relying on a Marquardt-Levenberg algorithm (MLA), a Newton-like method particularly robust for solving local optimization problems, we provide with marqLevAlg package an efficient and general-purpose local optimizer which (i) prevents convergence to saddle points by using a stringent convergence criterion based on the relative distance to minimum/maximum in addition to the stability of the parameters and of the objective function; and (ii) reduces the computation time in complex settings by allowing parallel calculations at each iteration. We demonstrate through a variety of cases from the literature that our implementation reliably and consistently reaches the optimum (even when other optimizers fail), and also largely reduces computational time in complex settings through the example of maximum likelihood estimation of different sophisticated statistical models., Comment: 20 pages, 4 figures

Published
2020
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10. Effects of interventions and optimal strategies in the stochastic system approach to causality

Author

Commenges, Daniel and Prague, Mélanie

Subjects
Statistics - Methodology, 62A01
Abstract

We consider the problem of defining the effect of an intervention on a time-varying risk factor or treatment for a disease or a physiological marker; we develop here the latter case. So, the system considered is $(Y,A,C)$, where $Y=(Y_t)$, is the marker process of interest, $A=A_t$ the treatment. A realistic case is that the treatment can be changed only at discrete times. In an observational study the treatment attribution law is unknown; however, the physical law can be estimated without knowing the treatment attribution law, provided a well-specified model is available. An intervention is specified by the treatment attribution law, which is thus known. Simple interventions will simply randomize the attribution of the treatment; interventions that take into account the past history will be called "strategies". The effect of interventions can be defined by a risk function $R^{\intr}=\Ee_{\intr}[L(\bar Y_{t_J}, \bar A_{t_{J}},C)]$, where $L(\bar Y_{t_J}, \bar A_{t_{J}},C)$ is a loss function, and contrasts between risk functions for different strategies can be formed. Once we can compute effects for any strategy, we can search for optimal or sub-optimal strategies; in particular we can find optimal parametric strategies. We present several ways for designing strategies. As an illustration, we consider the choice of a strategy for containing the HIV load below a certain level while limiting the treatment burden. A simulation study demonstrates the possibility of finding optimal parametric strategies., Comment: 23 pages, 4 figures

Published
2019

11. Leveraging Contact Network Information in Clustered Randomized Studies of Contagion Processes

Author

Wang, Maxwell H, Staples, Patrick, Prague, Mélanie, De Gruttola, Victor, and Onnela, Jukka-Pekka

Subjects
Statistics - Applications, 62M99, 91D30, 62-07
Abstract

In a randomized study, leveraging covariates related to the outcome (e.g. disease status) may produce less variable estimates of the effect of exposure. For contagion processes operating on a contact network, transmission can only occur through ties that connect affected and unaffected individuals; the outcome of such a process is known to depend intimately on the structure of the network. In this paper, we investigate the use of contact network features as efficiency covariates in exposure effect estimation. Using augmented generalized estimating equations (GEE), we estimate how gains in efficiency depend on the network structure and spread of the contagious agent or behavior. We apply this approach to simulated randomized trials using a stochastic compartmental contagion model on a collection of model-based contact networks and compare the bias, power, and variance of the estimated exposure effects using an assortment of network covariate adjustment strategies. We also demonstrate the use of network-augmented GEEs on a clustered randomized trial evaluating the effects of wastewater monitoring on COVID-19 cases in residential buildings at the the University of California San Diego., Comment: Substantial revision

Published
2016

12. Modeling CD4+ T cells dynamics in HIV-infected patients receiving repeated cycles of exogenous Interleukin 7

Author

Jarne, Ana, Commenges, Daniel, Prague, Mélanie, Levy, Yves, and Thiébaut, Rodolphe

Subjects
Statistics - Applications
Abstract

Combination Antiretroviral Therapy (cART) succeeds to control viral replication in most HIV infected patients. This is normally followed by a reconstitution of the CD4$^+$ T cells pool; however, this does not happen for a substantial proportion of patients. For these patients, an immunotherapy based on injections of Interleukin 7 (IL-7) has been recently proposed as a co-adjutant treatment in the hope of obtaining long-term reconstitution of the T cells pool. Several questions arise as to the long-term efficiency of this treatment and the best protocol to apply. We develop a model based on a system of ordinary differential equations and a statistical model of variability and measurement. We can estimate key parameters of this model using the data from INSPIRE, INSPIRE 2 $\&$ INSPIRE 3 trials. In all three studies, cycles of three injections have been administered; in the last two studies, for the first time, repeated cycles of exogenous IL-7 have been administered. Our aim was to estimate the possible different effects of successive injections in a cycle, to estimate the effect of repeated cycles and to assess different protocols. The use of dynamical models together with our complex statistical approach allow us to analyze major biological questions. We found a strong effect of IL-7 injections on the proliferation rate; however, the effect of the third injection of the cycle appears to be much weaker than the first ones. Also, despite a slightly weaker effect of repeated cycles with respect to the initial one, our simulations show the ability of this treatment of maintaining adequate CD4$^+$ T cells count for years. We were also able to compare different protocols, showing that cycles of two injections should be sufficient in most cases. %Finally, we also explore the possibility of adaptive protocols.

Published
2016

13. Correction to: Neutrophil Activation and Immune Thrombosis Profiles Persist in Convalescent COVID‑19

Author

Hocini, Hakim, Wiedemann, Aurélie, Blengio, Fabiola, Lefebvre, Cécile, Cervantes‑Gonzalez, Minerva, Foucat, Emile, Tisserand, Pascaline, Surenaud, Mathieu, Coléon, Séverin, Prague, Mélanie, Guillaumat, Lydia, Krief, Corinne, Fenwick, Craig, Laouénan, Cédric, Bouadma, Lila, Ghosn, Jade, Pantaleo, Giuseppe, Thiébaut, Rodolphe, and Lévy, Yves

Published
2023
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14. Accounting for interactions and complex inter-subject dependency in estimating treatment effect in cluster randomized trials with missing outcomes

Author

Prague, Melanie, Wang, Rui, Stephens, Alisa, Tchetgen, Eric Tchetgen, and DeGruttola, Victor

Subjects
Statistics - Methodology
Abstract

Semi-parametric methods are often used for the estimation of intervention effects on correlated outcomes in cluster-randomized trials (CRTs). When outcomes are missing at random (MAR), Inverse Probability Weighted (IPW) methods incorporating baseline covariates can be used to deal with informative missingness. Also, augmented generalized estimating equations (AUG) correct for imbalance in baseline covariates but need to be extended for MAR outcomes. However, in the presence of interactions between treatment and baseline covariates, neither method alone produces consistent estimates for the marginal treatment effect if the model for interaction is not correctly specified. We propose an AUG-IPW estimator that weights by the inverse of the probability of being a complete case and allows different outcome models in each intervention arm. This estimator is doubly robust (DR), it gives correct estimates whether the missing data process or the outcome model is correctly specified. We consider the problem of covariate interference which arises when the outcome of an individual may depend on covariates of other individuals. When interfering covariates are not modeled, the DR property prevents bias as long as covariate interference is not present simultaneously for the outcome and the missingness. An R package is developed implementing the proposed method. An extensive simulation study and an application to a CRT of HIV risk reduction-intervention in South Africa illustrate the method., Comment: 27 pages, 5 tables

Published
2015

15. Targeting SARS-CoV-2 receptor-binding domain to cells expressing CD40 improves protection to infection in convalescent macaques

Author

Marlin, Romain, Godot, Veronique, Cardinaud, Sylvain, Galhaut, Mathilde, Coleon, Severin, Zurawski, Sandra, Dereuddre-Bosquet, Nathalie, Cavarelli, Mariangela, Gallouët, Anne-Sophie, Maisonnasse, Pauline, Dupaty, Léa, Fenwick, Craig, Naninck, Thibaut, Lemaitre, Julien, Gomez-Pacheco, Mario, Kahlaoui, Nidhal, Contreras, Vanessa, Relouzat, Francis, Fang, Raphaël Ho Tsong, Wang, Zhiqing, Ellis, III, Jerome, Chapon, Catherine, Centlivre, Mireille, Wiedemann, Aurelie, Lacabaratz, Christine, Surenaud, Mathieu, Szurgot, Inga, Liljeström, Peter, Planas, Delphine, Bruel, Timothée, Schwartz, Olivier, Werf, Sylvie van der, Pantaleo, Giuseppe, Prague, Mélanie, Thiébaut, Rodolphe, Zurawski, Gerard, Lévy, Yves, and Grand, Roger Le

Published
2021
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16. Quality of stepped-wedge trial reporting can be reliably assessed using an updated CONSORT: crowd-sourcing systematic review

Author

Dutton, Susan J., Madurasinghe, Vichithranie, Morgan, Katy, Stuart, Beth, Fielding, Katherine, Cornelius, Victoria, Turner, Elizabeth L., Hooper, Richard, Giraudeau, Bruno, Seed, Paul T., Nickless, Alecia, Grayling, Michael, Prague, Melanie, Kerry, Sally, Bell, Lauren, Watson, Eila, Gafoor, Rafael, Marlin, Nadine, Yorganci, Emel, Smith, Lesley, Mbekwe, Murielle, Teerenstra, Steven, Chan, Claire, Moerbeek, Mirjam, Jacobsen, Pamela, Bond, Simon, Jones, Ben, Preisser, John, Kanaan, Mona, Hewitt, Catherine, Easter, Christina, Pellatt-Higgins, Tracy, Pankhurst, Laura, Agbla, Schadrac C., Eldridge, Sandra, Lerner, Robin G., Leyrat, Clémence, Pilling, Mark, Forman, Julia R., Bhattacharya, Indrani, Magill, Nicholas, Candlish, Jane, McDowell, Cliona, Martin, James, Kristunas, Caroline, Allen, Elizabeth, Seward, Nadine, Nicholls, Elaine, Franklin, Bryony Dean, Hemming, Karla, Carroll, Kelly, Thompson, Jennifer, Forbes, Andrew, and Taljaard, Monica

Published
2019
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17. Using a population-based Kalman estimator to model the COVID-19 epidemic in France: estimating associations between disease transmission and non-pharmaceutical interventions.

Author

Collin, Annabelle, Hejblum, Boris P., Vignals, Carole, Lehot, Laurent, Thiébaut, Rodolphe, Moireau, Philippe, and Prague, Mélanie

Subjects
INFECTIOUS disease transmission, COVID-19 pandemic, KALMAN filtering, WEATHER, VACCINATION coverage
Abstract

In response to the COVID-19 pandemic caused by SARS-CoV-2, governments have adopted a wide range of non-pharmaceutical interventions (NPI). These include stringent measures such as strict lockdowns, closing schools, bars and restaurants, curfews, and barrier gestures such as mask-wearing and social distancing. Deciphering the effectiveness of each NPI is critical to responding to future waves and outbreaks. To this end, we first develop a dynamic model of the French COVID-19 epidemics over a one-year period. We rely on a global extended Susceptible-Infectious-Recovered (SIR) mechanistic model of infection that includes a dynamic transmission rate over time. Multilevel data across French regions are integrated using random effects on the parameters of the mechanistic model, boosting statistical power by multiplying integrated observation series. We estimate the parameters using a new population-based statistical approach based on a Kalman filter, used for the first time in analysing real-world data. We then fit the estimated time-varying transmission rate using a regression model that depends on the NPIs while accounting for vaccination coverage, the occurrence of variants of concern (VoC), and seasonal weather conditions. We show that all NPIs considered have an independent significant association with transmission rates. In addition, we show a strong association between weather conditions that reduces transmission in summer, and we also estimate increased transmissibility of VoC. [ABSTRACT FROM AUTHOR]

Published
2024
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19. Universal test and treat and the HIV epidemic in rural South Africa: a phase 4, open-label, community cluster randomised trial

Author

Bärnighausen, Till, Herbst, Kobus, Iwuji, Collins, Makowa, Thembisa, Naidu, Kevi, Newell, Marie-Louise, Okesola, Nonhlanhla, de Oliveira, Tulio, Pillay, Deenan, Rochat, Tamsen, Tanser, Frank, Viljoen, Johannes, Zuma, Thembelihle, McGrath, Nuala, Balestre, Eric, Dabis, François, Karcher, Sophie, Orne-Gliemann, Joanna, Plazy, Melanie, Prague, Mélanie, Thiébaut, Rodolphe, Tiendrebeogo, Thierry, Boyer, Sylvie, Donfouet, Hermann, Gosset, Andrea, March, Laura, Protopopescu, Camelia, Spire, Bruno, Calmy, Alexandra, Larmarange, Joseph, Inghels, Maxime, Diallo, Hassimiou, Calvez, Vincent, Derache, Anne, Marcelin, Anne-Geneviève, Dray-Spira, Rosemary, Lert, France, El Farouki, Kamal, Lessells, Richard, Freedberg, Kenneth, Imrie, John, Chaix, Marie-Laure, Newell, Colin, Hontelez, Jan, Bazin, Brigitte, Rekacewicz, Claire, Iwuji, Collins C, Thiebaut, Rodolphe, Dreyer, Jaco, and De Oliveira, Tulio

Published
2018
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22. Impact of variants of concern on SARS-CoV-2 viral dynamics in non-human primates

Author

Marc, Aurélien, primary, Marlin, Romain, additional, Donati, Flora, additional, Prague, Mélanie, additional, Kerioui, Marion, additional, Hérate, Cécile, additional, Alexandre, Marie, additional, Dereuddre-bosquet, Nathalie, additional, Bertrand, Julie, additional, Contreras, Vanessa, additional, Behillil, Sylvie, additional, Maisonnasse, Pauline, additional, Van Der Werf, Sylvie, additional, Le Grand, Roger, additional, and Guedj, Jérémie, additional

Published
2023
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25. Leveraging Contact Network Information in Clustered Randomized Studies of Contagion Processes.

Author

Wang, Maxwell, Wang, Maxwell, Staples, Patrick, Prague, Mélanie, DeGruttola, Victor, Onnela, Jukka-Pekka, Goyal, Ravi, Wang, Maxwell, Wang, Maxwell, Staples, Patrick, Prague, Mélanie, DeGruttola, Victor, Onnela, Jukka-Pekka, and Goyal, Ravi

Abstract

In a randomized study, leveraging covariates related to the outcome (e.g. disease status) may produce less variable estimates of the effect of exposure. For contagion processes operating on a contact network, transmission can only occur through ties that connect affected and unaffected individuals; the outcome of such a process is known to depend intimately on the structure of the network. In this paper, we investigate the use of contact network features as efficiency covariates in exposure effect estimation. Using augmented generalized estimating equations (GEE), we estimate how gains in efficiency depend on the network structure and spread of the contagious agent or behavior. We apply this approach to simulated randomized trials using a stochastic compartmental contagion model on a collection of model-based contact networks and compare the bias, power, and variance of the estimated exposure effects using an assortment of network covariate adjustment strategies. We also demonstrate the use of network-augmented GEEs on a clustered randomized trial evaluating the effects of wastewater monitoring on COVID-19 cases in residential buildings at the the University of California San Diego.

Published
2023

28. Within-host models of SARS-CoV-2: What can it teach us on the biological factors driving virus pathogenesis and transmission?

Author

Prague, Mélanie, Alexandre, Marie, Thiébaut, Rodolphe, Guedj, Jérémie, Vaccine Research Institute (VRI), Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12), Statistics In System biology and Translational Medicine (SISTM), Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)- Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Infection, Anti-microbiens, Modélisation, Evolution (IAME (UMR_S_1137 / U1137)), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité)-Université Sorbonne Paris Nord, This work has received funding from the French Agency for Research on AIDS and Emerging Infectious Diseases via the EMERGEN project (ANRS0151)., and Prague, Mélanie

Subjects
Mathematical modelling, SARS-CoV-2, Virus dynamics, Host–pathogen interaction, [MATH.MATH-DS]Mathematics [math]/Dynamical Systems [math.DS], Immunity, [MATH.MATH-DS] Mathematics [math]/Dynamical Systems [math.DS], COVID-19, [SDV.IMM.IMM]Life Sciences [q-bio]/Immunology/Immunotherapy, General Medicine, Critical Care and Intensive Care Medicine, Antiviral Agents, Biological Factors, [SDV.IMM.VAC] Life Sciences [q-bio]/Immunology/Vaccinology, Anesthesiology and Pain Medicine, Antiviral treatment, [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], Humans, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, [SDV.IMM.IMM] Life Sciences [q-bio]/Immunology/Immunotherapy, [SDV.IMM.VAC]Life Sciences [q-bio]/Immunology/Vaccinology, [MATH.MATH-ST] Mathematics [math]/Statistics [math.ST], ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2022

29. Impact of non-pharmaceutical interventions, weather, vaccination, and variants on COVID-19 transmission across departments in France: a modelling study

Author

Paireau, Juliette, Charpignon, Marie-Laure, Larrieu, Sophie, Calba, Clémentine, Hozé, Nathanaël, Boëlle, Pierre-Yves, Thiébaut, Rodolphe, Prague, Mélanie, Cauchemez, Simon, Direction des maladies infectieuses - Infectious Diseases Division [Saint-Maurice], Santé publique France - French National Public Health Agency [Saint-Maurice, France], Modélisation mathématique des maladies infectieuses - Mathematical modelling of Infectious Diseases, Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), MIT Institute for Data, Systems, and Society [Cambridge, MA] (IDSS), Massachusetts Institute of Technology (MIT), Boston Children's Hospital, Harvard Medical School [Boston] (HMS), Statistics In System biology and Translational Medicine (SISTM), Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)- Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Santé publique France Nouvelle-Aquitaine [Bordeaux], Santé publique France Provence-Alpes-Côte d'azur et Corse - Provence-Alps-French Riviera and Corsica [Marseille], Institut Pierre Louis d'Epidémiologie et de Santé Publique (iPLESP), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU), We acknowledge financial support from the Investissement d’Avenir program, the Laboratoire d’Excellence Integrative Biology of Emerging Infectious Diseases program (grant ANR-10-LABX-62-IBEID), Santé publique France, the INCEPTION project (PIA/ANR16-CONV-0005), the European Union’s Horizon 2020 research and innovation program under grants 101003589 (RECOVER) and 874735 (VEO), AXA, Groupama, the French Agency for Research on AIDS and Emerging Infectious Diseases via the EMERGEN project (ANRS0151), and the National Research Agency (ANR) through the ANR-Flash call for COVID-19 (grant ANR-20-COVI-0018)., ANR-10-LABX-0062,IBEID,Integrative Biology of Emerging Infectious Diseases(2010), ANR-16-CONV-0005,INCEPTION,Institut Convergences pour l'étude de l'Emergence des Pathologies au Travers des Individus et des populatiONs(2016), ANR-20-COVI-0018,TheraCoV,Dynamique virale au niveau individuel et populationnel : implications pour l'optimisation des stratégies antivirales(2020), European Project: 101003589, H2020-SC1-PHE-CORONAVIRUS-2020,RECOVER(2020), European Project: 874735,H2020-SC1-2019-Single-Stage-RTD,VEO(2020), PAIREAU, Juliette, Integrative Biology of Emerging Infectious Diseases - - IBEID2010 - ANR-10-LABX-0062 - LABX - VALID, Institut Convergences pour l'étude de l'Emergence des Pathologies au Travers des Individus et des populatiONs - - INCEPTION2016 - ANR-16-CONV-0005 - CONV - VALID, Dynamique virale au niveau individuel et populationnel : implications pour l'optimisation des stratégies antivirales - - TheraCoV2020 - ANR-20-COVI-0018 - COVID-19 - VALID, Rapid European COVID-19 Emergency Response research - RECOVER - - H2020-SC1-PHE-CORONAVIRUS-20202020-02-14 - 2022-02-13 - 101003589 - VALID, and Versatile Emerging infectious disease Observatory - VEO - - H2020-SC1-2019-Single-Stage-RTD2020-01-01 - 2024-12-31 - 874735 - VALID

Subjects
variants, [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, SARS-CoV-2, COVID-19, reproduction number, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, vaccination, non-pharmaceutical interventions, climate, multivariable regression model
Abstract

Background: Multiple factors shape the temporal dynamics of the COVID-19 pandemic. Quantifying their relative contributions is key to guide future control strategies. Our objective was to disentangle the individual effects of non-pharmaceutical interventions (NPIs), weather, vaccination, and variants of concern (VOC) on local SARS-CoV-2 transmission.Methods: We developed a log-linear model for the weekly reproduction number (R) of hospital admissions in 92 French metropolitan departments. We leveraged (i) the homogeneity in data collection and NPI definitions across departments, (ii) the spatial heterogeneity in the timing of NPIs, and (iii) an extensive observation period (14 months) covering different meteorological conditions, VOC proportions, and vaccine coverage levels.Results: Three lockdowns reduced R by 72.9% (95%CI: 71.4-74.2), 70.4% (69.2-71.6) and 60.4% (56.1-64.3), respectively. Curfews implemented at 6/7pm and 8/9pm reduced R by 34.5% (28.1-40.4) and 18.4% (11.4-24.8), respectively. School closures reduced R by only 4.6% (1.6-7.4). We estimated that vaccination of the entire population would have reduced R by 74.0% (59.4-83.3), whereas the emergence of VOC (mainly Alpha during the study period) increased transmission by 46.9% (38.2-56.0) compared with the historical variant. Winter weather conditions (lower temperature and absolute humidity) increased R by 41.7% (37.0-46.7) compared to summer weather conditions. Additionally, we explored counterfactual scenarios (absence of VOC or vaccination) to assess their impact on hospital admissions.Conclusions: Our study demonstrates the strong effectiveness of NPIs and vaccination and quantifies the role of meteorological factors while adjusting for other confounders. It highlights the importance of retrospective evaluation of interventions to inform future decision-making.

Published
2022

30. Impact of variants of concern on SARS-CoV-2 viral dynamics in non-human primates

Author

Marc, Aurélien, primary, Marlin, Romain, additional, Donati, Flora, additional, Prague, Mélanie, additional, Kerioui, Marion, additional, Hérate, Cécile, additional, Alexandre, Marie, additional, Dereuddre-bosquet, Nathalie, additional, Bertrand, Julie, additional, Contreras, Vanessa, additional, Behillil, Sylvie, additional, Maisonnasse, Pauline, additional, Van Der Werf, Sylvie, additional, Grand, Roger Le, additional, and Guedj, Jérémie, additional

Published
2022
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32. SAMBA: A novel method for fast automatic model building in nonlinear mixed-effects models

Author

Prague, Mélanie, Lavielle, Marc, Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Statistics In System biology and Translational Medicine (SISTM), Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)- Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Modélisation en pharmacologie de population (XPOP), Centre de Mathématiques Appliquées - Ecole Polytechnique (CMAP), École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), This study has received funding from the Nipah virus project financed by the French Ministry of Higher Education, Research and Innovation., and Prague, Mélanie

Subjects
Nonlinear models, [STAT.ME] Statistics [stat]/Methodology [stat.ME], [MATH.MATH-DS]Mathematics [math]/Dynamical Systems [math.DS], Modeling, [MATH.MATH-DS] Mathematics [math]/Dynamical Systems [math.DS], RM1-950, Stochastic algorithm, Covariate model selection, Nonlinear Dynamics, [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, Population PKPD, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], Research Design, Modeling and Simulation, [SDV.SP.PHARMA] Life Sciences [q-bio]/Pharmaceutical sciences/Pharmacology, [SDV.SP.PHARMA]Life Sciences [q-bio]/Pharmaceutical sciences/Pharmacology, Humans, Pharmacology (medical), [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, Therapeutics. Pharmacology, [MATH.MATH-ST] Mathematics [math]/Statistics [math.ST], mixed-effects model, [STAT.ME]Statistics [stat]/Methodology [stat.ME], Algorithms
Abstract

The success of correctly identifying all the components of a nonlinear mixed‐effects model is far from straightforward: it is a question of finding the best structural model, determining the type of relationship between covariates and individual parameters, detecting possible correlations between random effects, or also modeling residual errors. We present the Stochastic Approximation for Model Building Algorithm (SAMBA) procedure and show how this algorithm can be used to speed up this process of model building by identifying at each step how best to improve some of the model components. The principle of this algorithm basically consists in “learning something” about the “best model,” even when a “poor model” is used to fit the data. A comparison study of the SAMBA procedure with Stepwise Covariate Modeling (SCM) and COnditional Sampling use for Stepwise Approach (COSSAC) show similar performances on several real data examples but with a much reduced computing time. This algorithm is now implemented in Monolix and in the R package Rsmlx.

Published
2021

33. Modelling the response to vaccine in non-human primates to define SARS-CoV-2 mechanistic correlates of protection

Author

Alexandre, Marie, primary, Marlin, Romain, additional, Prague, Mélanie, additional, Coleon, Severin, additional, Kahlaoui, Nidhal, additional, Cardinaud, Sylvain, additional, Naninck, Thibaut, additional, Delache, Benoit, additional, Surenaud, Mathieu, additional, Galhaut, Mathilde, additional, Dereuddre-Bosquet, Nathalie, additional, Cavarelli, Mariangela, additional, Maisonnasse, Pauline, additional, Centlivre, Mireille, additional, Lacabaratz, Christine, additional, Wiedemann, Aurelie, additional, Zurawski, Sandra, additional, Zurawski, Gerard, additional, Schwartz, Olivier, additional, Sanders, Rogier W, additional, Le Grand, Roger, additional, Levy, Yves, additional, and Thiébaut, Rodolphe, additional

Published
2022
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34. Design, immunogenicity, and efficacy of a pan-sarbecovirus dendritic-cell targeting vaccine

Author

Coléon, Séverin, primary, Wiedemann, Aurélie, additional, Surénaud, Mathieu, additional, Lacabaratz, Christine, additional, Hue, Sophie, additional, Prague, Mélanie, additional, Cervantes-Gonzalez, Minerva, additional, Wang, Zhiqing, additional, Ellis, Jerome, additional, Sansoni, Amandine, additional, Pierini, Camille, additional, Bardin, Quentin, additional, Fabregue, Manon, additional, Sharkaoui, Sarah, additional, Hoest, Philippe, additional, Dupaty, Léa, additional, Picard, Florence, additional, El Hajj, Marwa, additional, Centlivre, Mireille, additional, Ghosn, Jade, additional, Thiébaut, Rodolphe, additional, Cardinaud, Sylvain, additional, Malissen, Bernard, additional, Zurawski, Gérard, additional, Zarubica, Ana, additional, Zurawski, Sandra M., additional, Godot, Véronique, additional, and Lévy, Yves, additional

Published
2022
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37. Author response: Modelling the response to vaccine in non-human primates to define SARS-CoV-2 mechanistic correlates of protection

Author

Alexandre, Marie, primary, Marlin, Romain, additional, Prague, Mélanie, additional, Coleon, Severin, additional, Kahlaoui, Nidhal, additional, Cardinaud, Sylvain, additional, Naninck, Thibaut, additional, Delache, Benoit, additional, Surenaud, Mathieu, additional, Galhaut, Mathilde, additional, Dereuddre-Bosquet, Nathalie, additional, Cavarelli, Mariangela, additional, Maisonnasse, Pauline, additional, Centlivre, Mireille, additional, Lacabaratz, Christine, additional, Wiedemann, Aurelie, additional, Zurawski, Sandra, additional, Zurawski, Gerard, additional, Schwartz, Olivier, additional, Sanders, Rogier W, additional, Le Grand, Roger, additional, Levy, Yves, additional, and Thiébaut, Rodolphe, additional

Published
2022
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38. Using Population Based Kalman Estimator to Model COVID-19 Epidemic in France: Estimating the Effects of Non-Pharmaceutical Interventions on the Dynamics of Epidemic

Author

Collin, Annabelle, Hejblum, Boris P., Vignals, Carole, Lehot, Laurent, Thiébaut, Rodolphe, Moireau, Philippe, Prague, MéLanie, Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Statistics In System biology and Translational Medicine (SISTM), Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)- Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Vaccine Research Institute (VRI), Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12), CHU de Bordeaux Pellegrin [Bordeaux], Laboratoire de mécanique des solides (LMS), École polytechnique (X)-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Modélisation Mathématique pour l'Oncologie (MONC), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], UNICANCER-UNICANCER-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Mathematical and Mechanical Modeling with Data Interaction in Simulations for Medicine (M3DISIM), École polytechnique (X)-Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, and Hejblum, Boris

Subjects
[SDV.MHEP.ME]Life Sciences [q-bio]/Human health and pathology/Emerging diseases, [STAT.AP]Statistics [stat]/Applications [stat.AP], Epidemic modeling, [SDV.MHEP.MI]Life Sciences [q-bio]/Human health and pathology/Infectious diseases, [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], Non-pharmaceutical interventions, COVID-19, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, Population estimation, COVID-19 Epidemic modeling Non-pharmaceutical interventions Kalman filters Population estimation, Kalman filters, [MATH.MATH-ST] Mathematics [math]/Statistics [math.ST]
Abstract

International audience; In response to the ongoing COVID-19 pandemic caused by SARS-CoV-2, governments are taking a wide range of non-pharmaceutical interventions (NPI). These measures include interventions as stringent as strict lockdown but also school closure, bar and restaurant closure, curfews and barrier gestures i.e . social distancing. Disentangling the effectiveness of each NPI is crucial to inform response to future outbreaks. To this end, we first develop a multi-level estimation of the French COVID-19 epidemic over a period of one year. We rely on a global extended Susceptible-Infectious-Recovered (SIR) mechanistic model of the infection including a dynamical (over time) transmission rate containing a Wiener process accounting for modeling error. Random effects are integrated following an innovative population approach based on a Kalman-type filter where the log-likelihood functional couples data across French regions. We then fit the estimated time-varying transmission rate using a regression model depending on NPI, while accounting for vaccination coverage, apparition of variants of concern (VoC) and seasonal weather conditions. We show that all NPI considered have an independent significant effect on the transmission rate. We additionally demonstrate a strong effect from weather conditions which decrease transmission during the summer period, and also estimate increased transmissibility of VoCs.

Published
2021

40. Estimation for dynamical systems using a population-based Kalman filter – Applications in computational biology

Author

Collin, Annabelle, Prague, Mélanie, Moireau, Philippe, Mathematical and Mechanical Modeling with Data Interaction in Simulations for Medicine (M3DISIM), Laboratoire de mécanique des solides (LMS), École polytechnique (X)-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Statistics In System biology and Translational Medicine (SISTM), Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)- Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Modélisation Mathématique pour l'Oncologie (MONC), Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], UNICANCER-UNICANCER-Inria Bordeaux - Sud-Ouest, Institut Polytechnique de Bordeaux (Bordeaux INP), Centre National de la Recherche Scientifique (CNRS), Vaccine Research Institute (VRI), Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12), École polytechnique (X)-Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], Centre National de la Recherche Scientifique (CNRS)-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École polytechnique (X)-Inria Saclay - Ile de France, and Collin, Annabelle

Subjects
Epidemiology, Kalman Filters, COVID-19, [MATH.MATH-OC] Mathematics [math]/Optimization and Control [math.OC], Mixed-effect estimation, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Non linear mixed-effect models, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], [SDV.SP.PHARMA] Life Sciences [q-bio]/Pharmaceutical sciences/Pharmacology, population-based sequential estimation, [SDV.SP.PHARMA]Life Sciences [q-bio]/Pharmaceutical sciences/Pharmacology, Pharmacokinetics, [INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation, [MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC], [MATH.MATH-ST] Mathematics [math]/Statistics [math.ST], data assimilation
Abstract

International audience; Estimation of dynamical systems - in particular, identification of their parameters - is fundamental in computational biology, e.g., pharmacology, virology, or epidemiology, to reconcile model runs with available measurements. Unfortunately, the mean and variance priorities of the parameters must be chosen very appropriately to balance our distrust of the measurements when the data are sparse or corrupted by noise. Otherwise, the identification procedure fails. One option is to use repeated measurements collected in configurations with common priorities - for example, with multiple subjects in a clinical trial or clusters in an epidemiological investigation. This shared information is beneficial and is typically modeled in statistics using nonlinear mixed-effects models. In this paper, we present a data assimilation method that is compatible with such a mixed-effects strategy without being compromised by the potential curse of dimensionality. We define population-based estimators through maximum likelihood estimation. We then develop an equivalent robust sequential estimator for large populations based on filtering theory that sequentially integrates data. Finally, we limit the computational complexity by defining a reduced-order version of this population-based Kalman filter that clusters subpopulations with common observational backgrounds. The performance of the resulting algorithm is evaluated against classical pharmacokinetics benchmarks. Finally, the versatility of the proposed method is tested in an epidemiological study using real data on the hospitalisation of COVID-19 patients in the regions and departments of France.

Published
2021

41. Between group comparison of AUC in clinical trials with censored follow-up: Application to HIV therapeutic vaccines

Author

Alexandre, Marie, Prague, Mélanie, Thiébaut, Rodolphe, Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Statistics In System biology and Translational Medicine (SISTM), Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)- Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Vaccine Research Institute (VRI), Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12), and Alexandre, Marie

Subjects
study drop out, [STAT]Statistics [stat], longitudinal data, [STAT.ME] Statistics [stat]/Methodology [stat.ME], area under the curve, statistical test, left-censoring, mixed-effects model, [STAT.ME]Statistics [stat]/Methodology [stat.ME], [STAT] Statistics [stat]
Abstract

International audience; In clinical trials, longitudinal data are commonly analyzed and compared between groups using a single summary statistic such as area under the outcome versus time curve (AUC). However, incomplete data, arising from censoring due to a limit of detection or missing data, can bias these analyses. In this article, we present a statistical test based on splines-based mixed-model accounting for both the censoring and missingness mechanisms in the AUC estimation. Inferential properties of the proposed method were evaluated and compared to adhoc approaches and to a nonparametric method through a simulation study based on two-armed trial where trajectories and the proportion of missing data were varied. Simulation results highlights that our approach has significant advantages over the other methods. A real working example from two HIV therapeutic vaccine trials is presented to illustrate the applicability of our approach.

Published
2021

42. SARS-CoV-2 mechanistic correlates of protection: insight from modelling response to vaccines

Author

Alexandre, Marie, primary, Marlin, Romain, additional, Prague, Mélanie, additional, Coleon, Séverin, additional, Kahlaoui, Nidhal, additional, Cardinaud, Sylvain, additional, Naninck, Thibaut, additional, Delache, Benoit, additional, Surenaud, Mathieu, additional, Galhaut, Mathilde, additional, Dereuddre-Bosquet, Nathalie, additional, Cavarelli, Mariangela, additional, Maisonnasse, Pauline, additional, Centlivre, Mireille, additional, Lacabaratz, Christine, additional, Wiedemann, Aurelie, additional, Zurawski, Sandra, additional, Zurawski, Gerard, additional, Schwartz, Olivier, additional, Sanders, Rogier W, additional, Le Grand, Roger, additional, Levy, Yves, additional, and Thiébaut, Rodolphe, additional

Published
2021
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47. Parameter estimation in nonlinear mixed effect models based on ordinary differential equations: an optimal control ap- proach

Author

Clairon, Quentin, Pasin, Chloé, Balelli, Irene, Thiébaut, Rodolphe, Prague, Mélanie, Clairon, Quentin, Statistics In System biology and Translational Medicine (SISTM), Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)- Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Department of Infectious Diseases and Hospital Epidemiology [Zurich], University hospital of Zurich [Zurich], Institute of Virology (Vienna), Medizinische Universität Wien = Medical University of Vienna, E-Patient : Images, données & mOdèles pour la médeciNe numériquE (EPIONE), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), This work has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under projects EBOVAC1 and EBOVAC3 (respectively grant agreement No 115854 and No 800176). The IMI2 Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and the European Federation of Pharmaceutical Industries and Association., Vaccine Research Institute (VRI), and Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)

Subjects
Methodology (stat.ME), FOS: Computer and information sciences, Dynamic population models, Mechanistic models, [STAT.ME] Statistics [stat]/Methodology [stat.ME], Clinical trial analysis, Optimal control theory, Nonlinear mixed effects models, [STAT.ME]Statistics [stat]/Methodology [stat.ME], Statistics - Methodology, Ordinary differential equations
Abstract

We present a parameter estimation method for nonlinear mixed effect models based on ordinary differential equations (NLME-ODEs). The method presented here aims at regularizing the estimation problem in presence of model misspecifications, practical identifiability issues and unknown initial conditions. For doing so, we define our estimator as the minimizer of a cost function which incorporates a possible gap between the assumed model at the population level and the specific individual dynamic. The cost function computation leads to formulate and solve optimal control problems at the subject level. This control theory approach allows to bypass the need to know or estimate initial conditions for each subject and it regularizes the estimation problem in presence of poorly identifiable parameters. Comparing to maximum likelihood, we show on simulation examples that our method improves estimation accuracy in possibly partially observed systems with unknown initial conditions or poorly identifiable parameters with or without model error. We conclude this work with a real application on antibody concentration data after vaccination against Ebola virus coming from phase 1 trials. We use the estimated model discrepancy at the subject level to analyze the presence of model misspecification.

Published
2021

48. Modelling the response to vaccine in nonhuman primates to define SARS-CoV-2 mechanistic correlates of protection.

Author

Alexandre, Marie, Marlin, Romain, Prague, Mélanie, Coleon, Severin, Kahlaoui, Nidhal, Cardinaud, Sylvain, Naninck, Thibaut, Delache, Benoit, Surenaud, Mathieu, Galhaut, Mathilde, Dereuddre-Bosquet, Nathalie, Cavarelli, Mariangela, Maisonnasse, Pauline, Centlivre, Mireille, Lacabaratz, Christine, Wiedemann, Aurelie, Zurawski, Sandra, Zurawski, Gerard, Schwartz, Olivier, and Sanders, Rogier W.

Published
2022
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49. Comparison of AUC in clinical trials with follow up censoring: Application to HIV therapeutic vaccines

Author

Alexandre, Marie, Prague, Mélanie, Lévy, Yves, Rodolphe, Thiebaut, Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Statistics In System biology and Translational Medicine (SISTM), Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)- Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Vaccine Research Institute [Créteil, France] (VRI), Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12), Epidémiologie et Biostatistique [Bordeaux], Université Bordeaux Segalen - Bordeaux 2-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Vaccine Research Institute (VRI), Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Université Bordeaux Segalen - Bordeaux 2, and Alexandre, Marie

Subjects
[STAT.ME] Statistics [stat]/Methodology [stat.ME], [STAT.ME]Statistics [stat]/Methodology [stat.ME], ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2020

50. Estimation for dynamical systems using a population-based Kalman filter - Applications to pharmacokinetics models

Author

Collin, Annabelle, Prague, Mélanie, Moireau, Philippe, Mathematical and Mechanical Modeling with Data Interaction in Simulations for Medicine (M3DISIM), Laboratoire de mécanique des solides (LMS), École polytechnique (X)-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Statistics In System biology and Translational Medicine (SISTM), Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)- Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), and Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Université de Bordeaux (UB)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Université de Bordeaux (UB)

Subjects
[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], [SDV.SP.PHARMA]Life Sciences [q-bio]/Pharmaceutical sciences/Pharmacology, [MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC], [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation
Abstract

Many methods exist to identify parameters of dynamical systems. Unfortunately, in addition to the classical measurement noise and under-sampling drawbacks, mean and variance priors of the estimated parameters can be very vague. These difficulties can lead the estimation procedure to underfitting. In clinical studies, a circumvention consists in using the fact that multiple independent patients are observed as proposed by nonlinear mixed-effect models. However, these very effective approaches can turn to be time-consuming or even intractable when the model complexity increases. Here, we propose an alternative strategy of controlled complexity. We first formulate a population least square estimator and its associated a Kalman based filter, hence defining a robust large population sequential estimator. Then, to reduce and control the computational complexity, we propose a reduced-order version of this population Kalman filter based on a clustering technique applied to the observations. Using simulated pharmacokinetics data and the theophylline pharmacokinetics data, we compare the proposed approach with literature methods. We show that using the population filter improves the estimation performance compared to the classical and fast patient-by-patient Kalman filter and leads to estimation results comparable to state-of-the-art population-based approaches. Then, the reduced-order version allows to drastically reduce the computational time for equivalent estimation and prediction.

Published
2020

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