Search

Your search keyword '"Soubeyrand, Samuel"' showing total 369 results
Start Over Author "Soubeyrand, Samuel"
369 results on '"Soubeyrand, Samuel"'

54. COVID-19 mortality dynamics: The future modelled as a (mixture of) past(s)

Author

Soubeyrand, Samuel, Ribaud, Mélina, Baudrot, Virgile, Allard, Denis, Pommeret, Denys, Roques, Lionel, Biostatistique et Processus Spatiaux (BioSP), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and Université de Lyon

Subjects
Viral Diseases, Death Rates, Science, Decision Making, Pneumonia, Viral, Social Sciences, Research and Analysis Methods, Global Health, Geographical locations, Medical Conditions, Mathematical and Statistical Techniques, Cognition, Population Metrics, Medicine and Health Sciences, Austria, Global health, COVID-19, Decision making, Sweden, Europe, Forecasting, Death rates, Psychology, Humans, Public and Occupational Health, European Union, Statistical Methods, Pandemics, Population Biology, Statistics, Cognitive Psychology, Biology and Life Sciences, Covid 19, Models, Theoretical, Infectious Diseases, People and Places, Physical Sciences, Cognitive Science, Medicine, Coronavirus Infections, Mathematics, [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology, Research Article, Neuroscience
Abstract

International audience; Discrepancies in population structures, decision making, health systems and numerous other factors result in various COVID-19-mortality dynamics at country scale, and make the forecast of deaths in a country under focus challenging. However, mortality dynamics of countries that are ahead of time implicitly include these factors and can be used as real-life competing predicting models. We precisely propose such a data-driven approach implemented in a publicly available web app timely providing mortality curves comparisons and real-time short-term forecasts for about 100 countries. Here, the approach is applied to compare the mortality trajectories of second-line and front-line European countries facing the COVID-19 epidemic wave. Using data up to mid-April, we show that the second-line countries generally followed relatively mild mortality curves rather than fast and severe ones. Thus, the continuation, after mid-April, of the COVID-19 wave across Europe was likely to be mitigated and not as strong as it was in most of the front-line countries first impacted by the wave (this prediction is corroborated by posterior data).

Published
2020

55. Mobilisation de la FRB par les pouvoirs publics français sur les liens entre Covid-19 et biodiversité

Author

SILVAIN, Jean-François, Goffaux, Robin, Soubelet, Hélène, Sarrazin, Francois, Abbadie, Luc, Albert, Cécile H., ARPIN, Isabelle, Barot, Sébastien, Barthélémy, Daniel, Bazile, Didier, Billet, Philippe, Boudet, Céline, Charbonnel, Nathalie, Charmantier, Anne, Dobigny, Gauthier, Emperaire, Laure, Gaba, Sabrina, Gaubert, Philippe, Gilot-Fromont, Emmanuelle, Grandcolas, Philippe, Guégan, Jean-François, Jactel, Hervé, Laurans, Yann, Leblan, Vincent, Le Gall, Line, LEVREL, Harold, Morand, Serge, Morel, Jean-Louis, Moutou, François, Pham, Jean-Louis, Plantard, Olivier, Pontier, Dominique, Roche, Benjamin, Roger, François, Sainteny, Guillaume, Scemama, Pierre, Soubeyrand, Samuel, Thybaud, Eric, Vittecoq, Marion, Vourc'h, Gwenaël, Fondation pour la recherche sur la Biodiversité (FRB), Sorbonne Université (SU), Centre National de la Recherche Scientifique (CNRS), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut de Recherche pour le Développement (IRD), Direction Générale Déléguée à la Recherche et à la Stratégie (Cirad-Dgdrs), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Gestion des ressources renouvelables et environnement (UPR GREEN), Département Environnements et Sociétés (Cirad-ES), Université Jean Moulin Lyon 3 - Faculté de Droit (UJML3 Droit), Université Jean Moulin - Lyon 3 (UJML), Université de Lyon-Université de Lyon, Institut National de l'Environnement Industriel et des Risques (INERIS), Institut Ecologie et Environnement (INEE), Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), Institut de Systématique, Evolution, Biodiversité (ISYEB ), Muséum national d'Histoire naturelle (MNHN)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université des Antilles (UA), Institut du Développement Durable et des Relations Internationales (IDDRI), Institut d'Études Politiques [IEP] - Paris, Centre International de Recherche sur l'Environnement et le Développement (CIRED), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-École des hautes études en sciences sociales (EHESS)-AgroParisTech-École des Ponts ParisTech (ENPC)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Laboratoire Sols et Environnement (LSE), Université de Lorraine (UL)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Agence nationale de sécurité sanitaire de l'alimentation, de l'environnement et du travail (ANSES), Retraité, Université de Lyon, Animal, Santé, Territoires, Risques et Ecosystèmes (UMR ASTRE), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER), Institut de recherche de la Tour du Valat, Unité Mixte de Recherche d'Épidémiologie des maladies Animales et zoonotiques (UMR EPIA), VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), FRB, Muséum national d'Histoire naturelle (MNHN)-École pratique des hautes études (EPHE), and Institut Français de Recherche pour l'Exploitation de la Mer - Brest (IFREMER Centre de Bretagne)

Subjects
[SDV]Life Sciences [q-bio], [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie
Abstract

AUTRE = Experts sollicités; L’épidémie COVID-19 pose de nombreuses questions. Quels sont les liens de cette crise sanitaire avec la faune sauvage, quels sont ses liens avec l’érosion de la biodiversité que le dernier rapport de l’Ipbes a souligné, quels sont ses liens avec certains systèmes de production alimentaire et plus généralement avec l’anthropisation de la planète ? Pour les éclairer sur ces sujets, les pouvoirs publics se sont tournés vers la recherche.

Published
2020

56. Modèle SIR mécanistico-statistique pour l'estimation du nombre d'infectés et du taux de mortalité par COVID-19

Author

Roques, Lionel, Klein, Etienne, Papax, Julien, Sar, Antoine, Soubeyrand, Samuel, Biostatistique et Processus Spatiaux (BioSP), and Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)

Subjects
[STAT.AP]Statistics [stat]/Applications [stat.AP], case fatality rate, mechanistic-statistical model, Bayesian inference, [MATH.MATH-DS]Mathematics [math]/Dynamical Systems [math.DS], infection fatality ratio, COVID-19, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, SIR model
Abstract

International audience; The number of screening tests carried out in France and the methodology used to target the patients tested do not allow for a direct computation of the actual number of cases and the infection fatality ratio (IFR). The main objective of this work is to estimate the actual number of people infected with COVID-19 and to deduce the IFR during the observation window in France. We develop a 'mechanistic-statistical' approach coupling a SIR epidemiological model describing the unobserved epidemiological dynamics, a probabilistic model describing the data acquisition process and a statistical inference method. The actual number of infected cases in France is probably higher than the observations: we find here a factor times 8 (95%-CI: 5-12) which leads to an IFR in France of 0.5% (95%-CI: 0.3-0.8) based on hospital death counting data. Adjusting for the number of deaths in nursing homes, we obtain an IFR of 0.8% (95%-CI: 0.45-1.25). This IFR is consistent with previous findings in China (0.66%) and in the UK (0.9%) and lower than the value previously computed on the Diamond Princess cruise ship data (1.3%).

Published
2020

57. The link between Covid-19 and biodiversity: A report commissioned by the French public authorities

Author

Silvain, Jean-François (ed.), Goffaux, Robin (ed.), Soubelet, H. (ed.), Sarrazin, François (ed.), Abbadie, Luc, Albert, Cécile H., Arpin, Isabelle, Barot, Sébastien, Barthélémy, Daniel, Bazile, Didier, Billet, Philippe, Boudet, Céline, Charbonnel, Nathalie, Charmantier, Anne, Dobigny, Gauthier, Emperaire, Laure, Gaba, Sabrina, Gaubert, Philippe, Gilot-Fromont, Emmanuelle, Grandcolas, Philippe, Guégan, Jean-François, Jactel, Hervé, Laurans, Yann, Leblan, Vincent, Le Gall, Line, Levrel, Harold, Morand, Serge, Morel, Jean-Louis, Moutou, François, Pham, Jean Louis, Plantard, Olivier, Pontier, Dominique, Roche, Benjamin, Roger, François, Sainteny, Guillaume, Scemama, Pierre, Soubeyrand, Samuel, Thybaud, Eric, Vittecoq, Marion, and Vourc'h, Gwenaël

Published
2020

58. The spread of a wild plant pathogen is driven by the road network

Author

Soubeyrand, Samuel, Soubeyrand, S ( Samuel ), Numminen, Elina; https://orcid.org/0000-0002-5956-1094, Laine, Anna-Liisa; https://orcid.org/0000-0002-0703-5850, Soubeyrand, Samuel, Soubeyrand, S ( Samuel ), Numminen, Elina; https://orcid.org/0000-0002-5956-1094, and Laine, Anna-Liisa; https://orcid.org/0000-0002-0703-5850

Published
2020

60. Reproductive consequences of Colletotrichum lindemuthianum (Ascomycota) infection on wild bean plants (Phaseolus vulgaris)

Author

Benard-Capelle, Julien, Soubeyrand, Samuel, and Neema, Claire

Subjects
Fungi -- Properties, Common beans -- Diseases and pests, Plant-pathogen relationships -- Observations, Biological sciences
Abstract

Abstract: Fungal plant parasites can have strong reproductive consequences on their hosts, but little is known about the amount of parasite-induced fitness loss under natural conditions. We present data from [...]

Published
2006

69. One Health concepts and challenges for surveillance, forecasting, and mitigation of plant disease beyond the traditional scope of crop production.

Author

Morris, Cindy E., Géniaux, Ghislain, Nédellec, Claire, Sauvion, Nicolas, and Soubeyrand, Samuel

Subjects
AGRICULTURAL productivity, PATTERN recognition systems, AIR masses, FORECASTING, WATER masses, ZOONOSES, PLANT diseases
Abstract

The One Health approach to understanding disease epidemiology and achieving surveillance and prevention is holistic, while focusing on zoonotic diseases. Many of its principles are similar to those espoused in agroecology, begetting the question of what One Health can contribute—in practice—to preventing plant disease. Here we describe four knowledge challenges for plant health management that have arisen from the One Health experience for zoonotic diseases that could boost prospects for novel approaches to plant disease surveillance, prediction, and prevention. The challenges are to (a) uncover reservoirs and revise pathogen life histories, (b) elucidate drivers of virulence beyond the context of direct host–pathogen interactions, (c) account for the natural highways of long‐distance dissemination (i.e., surface water and air mass movement), and (d) update disease forecasts in the face of changing land use, cultivation practices, and climate. Furthermore, we note that implementation of a One Health approach to disease surveillance and prevention will require mobilization of tools to deal with the representation and accessibility of massive and heterogeneous data and knowledge; with knowledge inference, data science, modelling, and pattern recognition; and multi‐actor approaches that unite different sectors of society as well as different scientific disciplines. The infrastructure to build and the obstacles to overcome for a bona fide One Health approach to disease surveillance and prevention are key commonalities where actors in the efforts to prevent zoonotic diseases and plant disease can work together for the management of biodiversity and consequently human, animal, and plant health. [ABSTRACT FROM AUTHOR]

Published
2022
Full Text
View/download PDF

70. De l’inférence de la fonction de dispersion des pucerons à l’optimisation conjointe de la gestion de la sharka et de l’allocation de variétés résistantes

Author

PICARD, Coralie, Pleydell, David, Rimbaud, Loup, Picheny, Victor, Dallot, Sylvie, Jacquot, Emmanuel, Soubeyrand, Samuel, Thébaud, Gael, Biologie et Génétique des Interactions Plante-Parasite (UMR BGPI), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Animal, Santé, Territoires, Risques et Ecosystèmes (UMR ASTRE), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA), Unité de Pathologie Végétale (PV), Institut National de la Recherche Agronomique (INRA), Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Biostatistique et Processus Spatiaux (BioSP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)

Subjects
[SDV.SA]Life Sciences [q-bio]/Agricultural sciences, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, [SDV.BV.PEP]Life Sciences [q-bio]/Vegetal Biology/Phytopathology and phytopharmacy
Abstract

National audience; Les modèles permettent d’estimer des paramètres épidémiologiques, d’évaluer l’efficacité de différentes stratégies de gestion, et de les optimiser. A cette fin, nous avons développé le cadre PESO (parameter estimation–simulation–optimization), que nous avons appliqué à la gestion de la sharka (causée par le Plum pox virus ; PPV), la plus grave maladie des Prunus. Nous avons tout d’abord démontré que les paramètres épidémiologiques de la sharka peuvent être estimés précisément même en présence d’une lutte active contre la maladie. Ainsi, les données de surveillance collectées pendant 15 ans ont, entre autres, permis d’obtenir la première estimation des distances de vol des pucerons à l’échelle du paysage. Un modèle de simulation a ainsi pu être paramétré afin de simuler la dispersion et la gestion de la sharka (incluant la surveillance des vergers, l’arrachage d’arbres à risque, des interdictions de plantation, et le remplacement de vergers sensibles par des variétés résistantes). Nous avons ainsi défini la répartition de vergers résistants minimisant l’impact économique de la sharka dans des paysages plus ou moins agrégés, et nous avons montré que l’optimisation conjointe de la lutte contre la maladie et de la répartition des variétés résistantes peut améliorer l’efficience économique globale de la gestion de la sharka.

Published
2019

71. A statistical learning approach to infer transmissions of infectious diseases from deep sequencing data

Author

Alamil, Maryam, Hughes, Joseph, Berthier, Karine, Desbiez, Cecile, Thébaud, Gaël, Soubeyrand, Samuel, Biostatistique et Processus Spatiaux (BioSP), Institut National de la Recherche Agronomique (INRA), University of Glasgow, Unité de Pathologie Végétale (PV), Biologie et Génétique des Interactions Plante-Parasite (UMR BGPI), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)

Subjects
[SDV]Life Sciences [q-bio], [SDE]Environmental Sciences
Abstract

National audience; Pathogen sequence data have been exploited to infer who infected whom, by using empirical and model-based approaches. Most of these approaches exploit one pathogen sequence per infected host unit (e.g., individual, household, field). However, data collected with deep sequencing techniques, providing a subsample of the pathogen variants at each sampling time, are expected to give more insight on epidemiological links than a single sequence per host unit. A mechanistic viewpoint to transmission and micro-evolution has generally been followed to infer epidemiological links from these data. Here, we investigate an alternative statistical learning approach for estimating epidemiological links, which consists of learning the structure of epidemiological links with a pseudo-evolutionary model and training data before inferring links for the whole data set. We designed the pseudo-evolutionary model as a semi-parametric regression function where the response variable is the set of sequences observed from a recipient host unit and the explanatory variable is the set of sequences observed from a putative source. We derived from this model a penalized pseudo-likelihood that is used for selecting who infected whom or who is closely related to whom, where the penalization is calibrated on training data. In order to assess the efficiency of the pseudo-evolutionary model and the associated inference approach for estimating epidemiological links, we applied it to simulated data generated with diverse sampling efforts, sequencing techniques (corresponding to diverse depths and read lengths), and stochastic models of viral evolution and transmission. Then, we applied it to three real epidemics: swine Influenza, Ebola and a potyvirus of wild salsify. Such an approach has the potential to be particularly valuable in the case of a risk of erroneous mechanistic assumptions and sequencing errors, it is sufficiently parsimonious to allow handling big data sets in the future, and it can be applied to very different contexts from animal, human and plant epidemiology.

Published
2019

72. Model-based estimation of aphid dispersal distances and its use to co-optimize sharka management strategies and the allocation of resistant cultivars

Author

PICARD, Coralie, Pleydell, David, Rimbaud, Loup, Picheny, Victor, Dallot, Sylvie, Jacquot, Emmanuel, Soubeyrand, Samuel, Thébaud, Gael, Biologie et Génétique des Interactions Plante-Parasite (UMR BGPI), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Animal, Santé, Territoires, Risques et Ecosystèmes (UMR ASTRE), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA), Unité de Pathologie Végétale (PV), Institut National de la Recherche Agronomique (INRA), Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Biostatistique et Processus Spatiaux (BioSP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)

Subjects
[SDV.SA]Life Sciences [q-bio]/Agricultural sciences, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, ComputingMilieux_MISCELLANEOUS, [SDV.BV.PEP]Life Sciences [q-bio]/Vegetal Biology/Phytopathology and phytopharmacy
Abstract

National audience

Published
2019

73. A statistical learning approach to infer transmissions of infectious diseases from deep sequencing data

Author

Hughes, Joseph, Berthier, Karine, Desbiez, Cecile, Thébaud, Gaël, Soubeyrand, Samuel, and Alamil, Maryam

Subjects
transmission des maladies, analyse statistique, maladie infectieuse, analyse de séquences, pathologie animale, pathologie végétale, pathologie humaine, modèle d'évolution, modélisation
Abstract

Pathogen sequence data have been exploited to infer who infected whom, by using empirical and model-based approaches. Most of these approaches exploit one pathogen sequence per infected host unit (e.g., individual, household, field). However, data collected with deep sequencing techniques, providing a subsample of the pathogen variants at each sampling time, are expected to give more insight on epidemiological links than a single sequence per host unit. A mechanistic viewpoint to transmission and micro-evolution has generally been followed to infer epidemiological links from these data. Here, we investigate an alternative statistical learning approach for estimating epidemiological links, which consists of learning the structure of epidemiological links with a pseudo-evolutionary model and training data before inferring links for the whole data set. We designed the pseudo-evolutionary model as a semi-parametric regression function where the response variable is the set of sequences observed from a recipient host unit and the explanatory variable is the set of sequences observed from a putative source. We derived from this model a penalized pseudo-likelihood that is used for selecting who infected whom or who is closely related to whom, where the penalization is calibrated on training data. In order to assess the efficiency of the pseudo-evolutionary model and the associated inference approach for estimating epidemiological links, we applied it to simulated data generated with diverse sampling efforts, sequencing techniques (corresponding to diverse depths and read lengths), and stochastic models of viral evolution and transmission. Then, we applied it to three real epidemics: swine Influenza, Ebola and a potyvirus of wild salsify. Such an approach has the potential to be particularly valuable in the case of a risk of erroneous mechanistic assumptions and sequencing errors, it is sufficiently parsimonious to allow handling big data sets in the future, and it can be applied to very different contexts from animal, human and plant epidemiology.

Published
2019

74. Quick inference for log Gaussian Cox processes with non-stationary underlying random fields

Author

Dvo����k, Ji����, M��ller, Jesper, Mrkvi��ka, Tom����, and Soubeyrand, Samuel

Subjects
FOS: Mathematics, Statistics::Methodology, Statistics Theory (math.ST)
Abstract

For point patterns observed in natura, spatial heterogeneity is more the rule than the exception. In numerous applications, this can be mathematically handled by the flexible class of log Gaussian Cox processes (LGCPs); in brief, a LGCP is a Cox process driven by an underlying log Gaussian random field (log GRF). This allows the representation of point aggregation, point vacuum and intermediate situations, with more or less rapid transitions between these different states depending on the properties of GRF. Very often, the covariance function of the GRF is assumed to be stationary. In this article, we give two examples where the sizes (that is, the number of points) and the spatial extents of point clusters are allowed to vary in space. To tackle such features, we propose parametric and semiparametric models of non-stationary LGCPs where the non-stationarity is included in both the mean function and the covariance function of the GRF. Thus, in contrast to most other work on inhomogeneous LGCPs, second-order intensity-reweighted stationarity is not satisfied and the usual two step procedure for parameter estimation based on e.g. composite likelihood does not easily apply. Instead we propose a fast three step procedure based on composite likelihood. We apply our modelling and estimation framework to analyse datasets dealing with fish aggregation in a reservoir and with dispersal of biological particles.

Published
2019
Full Text
View/download PDF

75. Modélisation et optimisation de la gestion d’une épidemie : quel impact du paysage ?

Author

PICARD, Coralie, Picheny, Victor, Hendrikx, Pascal, Jacquot, Emmanuel, Soubeyrand, Samuel, Thébaud, Gael, Biologie et Génétique des Interactions Plante-Parasite (UMR BGPI), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Institut National de la Recherche Agronomique (INRA), Agence nationale de sécurité sanitaire de l'alimentation, de l'environnement et du travail (ANSES), Biostatistique et Processus Spatiaux (BioSP), Végéphyl. FRA., Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)

Subjects
[SDV.MP]Life Sciences [q-bio]/Microbiology and Parasitology, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, ComputingMilieux_MISCELLANEOUS
Abstract

National audience

Published
2018

77. Optimisation in silico de la gestion des maladies des plantes à l'échelle du paysage

Author

PICARD, Coralie, Picheny, Victor, Hendrikx , Pascal, Soubeyrand, Samuel, Jacquot, Emmanuel, Thébaud, Gael, Biologie et Génétique des Interactions Plante-Parasite (UMR BGPI), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Institut National de la Recherche Agronomique (INRA), Agence nationale de sécurité sanitaire de l'alimentation, de l'environnement et du travail (ANSES), Biostatistique et Processus Spatiaux (BioSP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)

Subjects
[SDV.MP]Life Sciences [q-bio]/Microbiology and Parasitology, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, ComputingMilieux_MISCELLANEOUS
Abstract

National audience

Published
2018

78. From local to large spatial scales: analyses of environmental factors that influence bacteria canker disease severity in kiwifruit orchards in France

Author

Lacroix, Christelle, Brachet, Marie-Lisa, Soubeyrand, Samuel, Morris, Cindy E., Unité de Pathologie Végétale (PV), Institut National de la Recherche Agronomique (INRA), Ctifl - Centre de Lanxade (Ctifl - Centre de Lanxade ), Centre Technique Interprofessionnel des Fruits et Légumes (CTIFL), Biostatistique et Processus Spatiaux (BioSP), and Societe Francaise de Phytopathologie (SFP). FRA. Institut National de la Recherche Agronomique (INRA), FRA. Centre National de la Recherche Scientifique (CNRS), FRA.

Subjects
[SDV.SA]Life Sciences [q-bio]/Agricultural sciences, Canker disease severity, Kiwifruit orchards, P. syringae, Local and landscape scales, ComputingMilieux_MISCELLANEOUS, [SDV.BV.PEP]Life Sciences [q-bio]/Vegetal Biology/Phytopathology and phytopharmacy
Abstract

National audience

Published
2018

79. Correction: Estimation of the dispersal distances of an aphid-borne virus in a patchy landscape

Author

Pleydell, David R.J., Pleydell, David, Soubeyrand, Samuel, Dallot, Sylvie, LABONNE, Gérard, Chadoeuf, Joel, Jacquot, Emmanuel, Thébaud, Gael, Animal, Santé, Territoires, Risques et Ecosystèmes (UMR ASTRE), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA), Biologie et Génétique des Interactions Plante-Parasite (UMR BGPI), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Biostatistique et Processus Spatiaux (BioSP), Institut National de la Recherche Agronomique (INRA), European Project: 204429,EC:FP7:KBBE,FP7-KBBE-2007-1,SHARCO(2008), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Contrôle des maladies animales exotiques et émergentes (UMR CMAEE), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Biostatistique et Processus Spatiaux (BIOSP), and University of Zagreb

Subjects
0106 biological sciences, [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, Epidemiology, Kernel Functions, Virologie, Plant Science, 01 natural sciences, Medicine and Health Sciences, Pox virus, Biology (General), Operator Theory, pathologie végétale, [SDV.EE]Life Sciences [q-bio]/Ecology, environment, 0303 health sciences, Aphid, [STAT.AP]Statistics [stat]/Applications [stat.AP], biology, Ecology, food and beverages, Agriculture, Disease control, 3. Good health, Infectious Diseases, Physical Sciences, [SDV.MP.VIR]Life Sciences [q-bio]/Microbiology and Parasitology/Virology, aphidae, Prunus, Orchards, Algorithms, Research Article, Farms, Infectious Disease Control, QH301-705.5, Phytopathology and phytopharmacy, [MATH.MATH-DS]Mathematics [math]/Dynamical Systems [math.DS], Disease Surveillance, Models, Biological, Infectious Disease Epidemiology, 03 medical and health sciences, Virology, Animals, Computer Simulation, plum pox virus, Plant Diseases, 030304 developmental biology, Estimation, Correction, Computational Biology, Biology and Life Sciences, Bayes Theorem, Plant Pathology, biology.organism_classification, Probability Theory, Probability Distribution, Phytopathologie et phytopharmacie, Insect Vectors, [SDV.BV.PEP]Life Sciences [q-bio]/Vegetal Biology/Phytopathology and phytopharmacy, [SDV.BA.ZI]Life Sciences [q-bio]/Animal biology/Invertebrate Zoology, Probability Density, Aphids, Infectious Disease Surveillance, maladie virale, Biological dispersal, Bayesian framework, Scale (map), [SDV.AEN]Life Sciences [q-bio]/Food and Nutrition, Mathematics, 010606 plant biology & botany, [SDV.EE.IEO]Life Sciences [q-bio]/Ecology, environment/Symbiosis
Abstract

Characterising the spatio-temporal dynamics of pathogens in natura is key to ensuring their efficient prevention and control. However, it is notoriously difficult to estimate dispersal parameters at scales that are relevant to real epidemics. Epidemiological surveys can provide informative data, but parameter estimation can be hampered when the timing of the epidemiological events is uncertain, and in the presence of interactions between disease spread, surveillance, and control. Further complications arise from imperfect detection of disease and from the huge number of data on individual hosts arising from landscape-level surveys. Here, we present a Bayesian framework that overcomes these barriers by integrating over associated uncertainties in a model explicitly combining the processes of disease dispersal, surveillance and control. Using a novel computationally efficient approach to account for patch geometry, we demonstrate that disease dispersal distances can be estimated accurately in a patchy (i.e. fragmented) landscape when disease control is ongoing. Applying this model to data for an aphid-borne virus (Plum pox virus) surveyed for 15 years in 605 orchards, we obtain the first estimate of the distribution of flight distances of infectious aphids at the landscape scale. About 50% of aphid flights terminate beyond 90 m, which implies that most infectious aphids leaving a tree land outside the bounds of a 1-ha orchard. Moreover, long-distance flights are not rare–10% of flights exceed 1 km. By their impact on our quantitative understanding of winged aphid dispersal, these results can inform the design of management strategies for plant viruses, which are mainly aphid-borne., Author summary In spatial epidemiology, dispersal kernels quantify how the probability of pathogen dissemination varies with distance from an infection source. Spatial models of pathogen spread are sensitive to kernel parameters; yet these parameters have rarely been estimated using field data gathered at relevant scales. Robust estimation is rendered difficult by practical constraints limiting the number of surveyed individuals, and uncertainties concerning their disease status. Here, we present a framework that overcomes these barriers to permit inference for a between-patch transmission model. Extensive simulations show that dispersal kernels can be estimated from epidemiological surveillance data. When applied to such data collected from more than 600 orchards during 15 years of a plant virus epidemic our approach enables the estimation of the dispersal kernel of infectious winged aphids. This kernel is long-tailed, as 50% of infectious aphids leaving a tree terminate their infectious flight beyond 90 m whilst 10% fly beyond 1 km. This first estimate of flight distances at the landscape scale for aphids–a group of vectors transmitting numerous viruses–is crucial for the science-based design of control strategies targeting plant virus epidemics.

Published
2018

80. Snooping into the invisible trajectory of airborne fungal inoculum

Author

Choufany, Maria, Martin, Olivier, Morris, Cindy E., Nicot, Philippe C., Soubeyrand, Samuel, and Leyronas, Christel

Subjects
Phytopathology and phytopharmacy, Mycology, champignon phytopathogène, inoculum aérien, génotype moléculaire, Phytopathologie et phytopharmacie, Agricultural sciences, Mycologie, paramètre climatique, dissémination longue distance, Sclerotinia sclerotiorum, aérobiologie, botrytis cinerea, pathologie végétale, Sciences agricoles
Published
2018

81. Measuring biological age to assess colony demographics in honeybees

Author

Alaux, Cedric, Soubeyrand, Samuel, Prado, Alberto, Peruzzi, Mathilde, Maisonnasse, Alban, Vallon, Julien, Hernandez, Julie, Jourdan, Pascal, Le Conte, Yves, Abeilles & Environnement (UR 406 ), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), UMT PrADE, Biostatistique et Processus Spatiaux (BioSP), Institut National de la Recherche Agronomique (INRA), Association pour le Développement de l'Apiculture Provençale (ADAPI), Institut Technique et Scientifique de l'Apiculture et de la Pollinisation (ITSAP-Institut de l'Abeille), and 14-01-R

Subjects
Aging, [SDV]Life Sciences [q-bio], Social Sciences, Gene Expression, Plant Science, Biochemistry, Vitellogenins, apis mellifera, Animal Products, Medicine and Health Sciences, Psychology, Foraging, [MATH]Mathematics [math], Mites, Animal Behavior, Plant Anatomy, Eukaryota, Agriculture, Gene Pool, Honey, Bees, Spring, Insects, âge physiologique, [SDE]Environmental Sciences, Pollen, Insect Proteins, Medicine, varroa, Seasons, Honey Bees, biomarqueur, Algorithms, Research Article, Arthropoda, durée de vie, Varroidae, Science, Models, Biological, Stress, Physiological, Genetics, Animals, [INFO]Computer Science [cs], Nutrition, vitellogenine, Behavior, Evolutionary Biology, Population Biology, démographie de population, Organisms, Biology and Life Sciences, Invertebrates, Hymenoptera, Diet, Food, saisonnalité, Earth Sciences, développement comportemental, Zoology, Beekeeping, Biomarkers, Population Genetics
Abstract

International audience; Honeybee colonies are increasingly exposed to environmental stress factors, which can lead to their decline or failure. However, there are major gaps in stressor risk assessment due to the difficulty of assessing the honeybee colony state and detecting abnormal events. Since stress factors usually induce a demographic disturbance in the colony (e.g. loss of foragers, early transition from nurse to forager state), we suggest that disturbances could be revealed indirectly by measuring the age- and task-related physiological state of bees, which can be referred to as biological age (an indicator of the changes in physiological state that occur throughout an individual lifespan). We therefore estimated the biological age of bees from the relationship between age and biomarkers of task specialization (vitellogenin and the adipokinetic hormone receptor). This relationship was determined from a calibrated sample set of known-age bees and mathematically modelled for biological age prediction. Then, we determined throughout the foraging season the evolution of the biological age of bees from colonies with low (conventional apiary) or high Varroa destructor infestation rates (organic apiary). We found that the biological age of bees from the conventional apiary progressively decreased from the spring (17 days) to the fall (6 days). However, in colonies from the organic apiary, the population aged from spring (13 days) to summer (18.5 days) and then rejuvenated in the fall (13 days) after Varroa treatment. Biological age was positively correlated with the amount of brood (open and closed cells) in the apiary with low Varroa pressure, and negatively correlated with Varroa infestation level in the apiary with high Varroa pressure. Altogether, these results show that the estimation of biological age is a useful and effective method for assessing colony demographic state and likely detrimental effects of stress factors.

Published
2018

82. From local to large spatial scales: analyses of environmental factors that influence bacteria canker disease severity in kiwifruit orchards in France

Author

Brachet, Marie-Lisa, Soubeyrand, Samuel, Morris, Cindy E., and Lacroix, Christelle

Subjects
bactérie phytopathogène, sévérité de la maladie, Phytopathology and phytopharmacy, facteur environnemental, pseudomonas syringae, culture fruitière, Phytopathologie et phytopharmacie, Agricultural sciences, P. syringae, Kiwifruit orchards, Canker disease severity, Local and landscape scales, chancre bactérien du kiwi, épidémiologie végétale, pathologie végétale, france, Sciences agricoles
Published
2018

87. A Spatio-Temporal Exposure-Hazard Model for Assessing Biological Risk and Impact

Author

Walker, Emilie, Leclerc, Melen, Rey, Jean-François, Beaudouin, Rémy, Soubeyrand, Samuel, Messean, Antoine, Biostatistique et Processus Spatiaux (BioSP), Institut National de la Recherche Agronomique (INRA), Unité Impacts Ecologiques des Innovations en Production Végétale (ECO-INNOV), Institut de Génétique, Environnement et Protection des Plantes (IGEPP), Institut National de la Recherche Agronomique (INRA)-Université de Rennes (UR)-AGROCAMPUS OUEST, Institut National de l'Environnement Industriel et des Risques (INERIS), 289706, European Commission, European Project: 289706,EC:FP7:KBBE,FP7-KBBE-2011-5,AMIGA(2011), Institut National de la Recherche Agronomique (INRA)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), and Biostatistique et Processus Spatiaux (BIOSP)

Subjects
Crops, Agricultural, Livestock, Toxicology, Models, Biological, Risk Assessment, Zea mays, Xenobiotics, Animals, Humans, Computer Simulation, Plant Diseases, Proportional Hazards Models, Ecology, Organisms, Genetically Modified, GMO, ENVIRONMENTAL RISK ASSESSMENT, Agriculture, STOCHASTIC GEOMETRY, [SDV.TOX]Life Sciences [q-bio]/Toxicology, Pollen, PARTICLE DISPERSAL, Genetic Engineering, Butterflies, Algorithms, Software, LANDSCAPE MANAGEMENT
Abstract

International audience; We developed a simulation model for quantifying the spatio-temporal distribution of contaminants (e.g., xenobiotics) and assessing the risk of exposed populations at the landscape level. The model is a spatio-temporal exposure-hazard model based on (i) tools of stochastic geometry (marked polygon and point processes) for structuring the landscape and describing the exposed individuals, (ii) a dispersal kernel describing the dissemination of contaminants from polygon sources, and (iii) an (eco)toxicological equation describing the toxicokinetics and dynamics of contaminants in affected individuals. The model was implemented in the briskaR package (biological risk assessment with R) of the R software. This article presents the model background, the use of the package in an illustrative example, namely, the effect of genetically modified maize pollen on nontarget Lepidoptera, and typical comparisons of landscape configurations that can be carried out with our model (different configurations lead to different mortality rates in the treated example). In real case studies, parameters and parametric functions encountered in the model will have to be precisely specified to obtain realistic measures of risk and impact and accurate comparisons of landscape configurations. Our modeling framework could be applied to study other risks related to agriculture, for instance, pathogen spread in crops or livestock, and could be adapted to cope with other hazards such as toxic emissions from industrial areas having health effects on surrounding populations. Moreover, the R package has the potential to help risk managers in running quantitative risk assessments and testing management strategies.

Published
2017

88. Contributions to Statistical Plant and Animal Epidemiology

Author

Soubeyrand, Samuel and Soubeyrand, Samuel

Subjects
Dispersion de particules, [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, Géométrie stochastique, Stochastic geometry, Genetic-space-time models, Mechanistic-statistical modelling, Particle dispersal, Modélisation mécanistico-statistique, Modèles génético-spatio-temporels, [MATH.MATH-ST] Mathematics [math]/Statistics [math.ST], Statistical inference, Inférence statistique
Published
2016

89. Predicting abundance of Botrytis cinerea airborne inoculum to forecast grey mould epidemics

Author

Leyronas, Christel, Martin, Olivier, Nicot, Philippe C., Soubeyrand, Samuel, Unité de Pathologie Végétale (PV), Institut National de la Recherche Agronomique (INRA), Biostatistique et Processus Spatiaux (BioSP), SMaCH, Copairnic, Societe Francaise de Phytopathologie (SFP). FRA., Université du Littoral Côte d’Opale (ULCO). FRA., Station de Pathologie Végétale (AVI-PATHO), and Biostatistique et Processus Spatiaux (BIOSP)

Subjects
[SDV.SA]Life Sciences [q-bio]/Agricultural sciences, aerobiology, fungi, statistical model, food and beverages, champignon phytopathogène, inoculum aérien, phytopathogenic fungus, pourriture grise, respiratory tract diseases, [SDV.BV.PEP]Life Sciences [q-bio]/Vegetal Biology/Phytopathology and phytopharmacy, air mass trajectory, modelling, paramètre climatique, épidémiologie végétale, masse d'air, aérobiologie, pathologie végétale, climatic parameter, [SDV.MP.MYC]Life Sciences [q-bio]/Microbiology and Parasitology/Mycology, modélisation
Abstract

International audience; Predicting abundance of [i]Botrytis cinerea[/i] airborne inoculum to forecast grey mould epidemics. 10. Conférence de la Société française de phytopathologie. 12. European foundation for plant pathology conference

Published
2017

90. Optimisation in silico de la gestion d'une maladie des plantes à l'échelle du paysage

Author

PICARD, Coralie, Soubeyrand, Samuel, Jacquot, Emmanuel, Thébaud, Gael, Biologie et Génétique des Interactions Plante-Parasite (UMR BGPI), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Biostatistique et Processus Spatiaux (BioSP), Institut National de la Recherche Agronomique (INRA), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Biostatistique et Processus Spatiaux (BIOSP), and Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)

Subjects
optimisation, maladie virale, échelle du paysage, [SDV.MP.VIR]Life Sciences [q-bio]/Microbiology and Parasitology/Virology, pathologie végétale
Abstract

BGPI : équipe 6; Optimisation in silico de la gestion d'une maladie des plantes à l'échelle du paysage. 3. Rencontres du GdR Ecologie Statistique

Published
2017

94. Predicting abundance of Botrytis cinerea airborne inoculum to forecast grey mould epidemics

Author

Martin, Olivier, Nicot, Philippe C., Soubeyrand, Samuel, and Leyronas, Christel

Subjects
Phytopathology and phytopharmacy, Mycology, champignon phytopathogène, inoculum aérien, Phytopathologie et phytopharmacie, pourriture grise, Agricultural sciences, Mycologie, paramètre climatique, aerobiology, air mass trajectory, climatic parameter, statistical model, épidémiologie végétale, masse d'air, aérobiologie, pathologie végétale, Sciences agricoles, modélisation
Published
2017

95. Model-based identification and optimization of key parameters for sharka management strategy

Author

Thébaud, Gael, Rimbaud, Loup, Bruchou, Claude, Thoyer, Sophie, Dallot, Sylvie, PICARD, Coralie, Soubeyrand, Samuel, Jacquot, Emmanuel, Biologie et Génétique des Interactions Plante-Parasite (UMR BGPI), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Agriculture and Food, Universidad de La Rioja (UR), Biostatistique et Processus Spatiaux (BioSP), Institut National de la Recherche Agronomique (INRA), Laboratoire Montpelliérain d'Économie Théorique et Appliquée (LAMETA), Université Montpellier 1 (UM1)-Université Paul-Valéry - Montpellier 3 (UPVM)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA), Biostatistique et Processus Spatiaux (BIOSP), Université Montpellier 1 (UM1)-Université Paul-Valéry - Montpellier 3 (UM3)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), and Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)

Subjects
sharka du prunier, maladie virale, [SDV]Life Sciences [q-bio], plum pox virus, gestion des maladies, pathologie végétale
Abstract

BGPI : équipe 6; Strategies for disease control are often based on expert opinions rather than on the formal demonstration that they are, at least in theory, effective. An alternative and promising approach consists in modeling both the epidemic processes and control measures in order to optimize disease management. In this way, the most influential parameters can be identified, and alternative control strategies can be proposed and tested in silico in order to assess their potential efficiency. To this end, we developed a spatially-realistic stochastic model simulating disease dynamics and management. We used this model to carry out generic sensitivity analyses with parameter ranges large enough to encompass values that are typical of many perennial plant diseases and of their management. These analyses revealed the importance of the latent period duration. Then, we specifically scrutinized the main processes affecting sharka epidemics, caused by Plum pox virus, a quarantine pathogen of prunus trees (especially apricot, peach and plum) in many areas of the world (Rimbaud et al., 2015). Using realistic parameter ranges given the present knowledge of sharka epidemiology, another sensitivity analysis on the most promising control parameters enabled the theoretical economic optimization of sharka management strategy. The identified optimized control strategies are discussed with the organizations responsible for sharka control in order to help the design of durable and cost-effective strategies.

Published
2016

Catalog

Books, media, physical & digital resources
See catalog results