Homepage Samuel Soubeyrand




Email: samuel.soubeyrand@inra.fr
Phone: +33
Address: INRA - BioSP - Site Agroparc - 84914 Avignon Cedex 9 - France


Offre d'emploi : Ingénieur(e) de Recherche - Conception et mise en oeuvre de systèmes d'information



I am interested in methods --model construction and statistical inference-- allowing the investigation of questions mainly addressed in epidemiology and ecology.

Topics of interest (corresponding publications indicated by numbers between brackets):

Spatio-temporal models describing pathogen and pest dynamics. Case-studies: rusts of wheat, powdery mildew of Plantago Lanceolata, European pine sawfly, foot-and-mouth disease, rabies [5,10,11,r3,14,17,18,23,26,31,36].

Mechanistic-statistical modelling which consists in combining a model for the dynamics under study (e.g. a pest dynamics) and a model for the observation process [5,11,14,18,34,44].

Modelling and estimation of propagule dispersal to learn about dispersal capacities of various organisms and infer processes which perturb the dispersal [3,6,8,10,12,17,22,33,37,41].

Reconstruction of transmission trees for fast-evolving pathogens using spatio-temporal data about infected hosts and genomic data about the pathogen [23,31,39,45].

Hidden processes (or latent processes) incorporated into models to handle unobserved environmental heterogeneity or to describe unobserved components of dynamics [1,3,4,7,8,9,11,14,19,18,23].

Residual analysis to determine what is lacking in a model and to help in specifying more sophisticated models, especially models with hidden processes [1,4,r2].

Estimation methods without likelihood to estimate parameters of models for which writing and/or maximizing the likelihood is difficult, as it is the case for some models with spatial dependences and some hierarchical models [15,24,27,29,34,43].

Dealing with scale discrepancies to conciliate, in the construction of a model, the sampling and phenomenon scales and to deal with datasets collected at different resolutions [5,11,r2].

Describing aggregation of individuals and decomposing the complexity of aggregation to better understand settlement strategies [13,16].



Do not hesitate to request to me the documents mentioned below if you cannot download them.

[62] Alaux C., Soubeyrand S., Prado A., Peruzzi M., Maisonnasse A., Valon J., Hernandez J., Jourdan P., Le Conte Y. (in press). Measuring biological age to assess colony demographics in honeybees. Plos one.

[61] Martinetti D., Soubeyrand S. (in press). Identifying lookouts for epidemio-surveillance: application to the emergence of Xylella fastidiosa in France. Phytopathology. doi:10.1094/PHYTO-07-18-0237-FI

[60] Leyronas C., Morris C. E., Choufany M., Soubeyrand S. (2018). Assessing the Aerial Interconnectivity of Distant Reservoirs of Sclerotinia sclerotiorum. Frontiers in Microbiology 9: 2257. doi:10.3389/fmicb.2018.02257 

[59] Soubeyrand S., de Jerphanion P., Martin O., Saussac M., Manceau C., Hendrikx P., Lannou C. (2018). Inferring pathogen dynamics from temporal count data: the emergence of Xylella fastidiosa in France is probably not recent. New Phytologist 219: 824-836. doi:10.1111/nph.15177
[58] Pleydell D.R.J., Soubeyrand S., Dallot S., Labonne G., Chadoeuf J., Jacquot E., Thébaud G. (2018). Estimation of the dispersal distances of an aphid-borne virus in a patchy landscape. Plos Computational Biology 14(4): e1006085. doi:10.1371/journal.pcbi.1006085
[57] Rimbaud L., Bruchou C., Dallot S., Pleydell D., Jacquot E., Soubeyrand S., Thébaud G. (2018). Using sensitivity analysis to identify key factors for the propagation of a plant epidemic. Royal Society Open Science 5: 171435. doi:
[56] Walker E., Leclerc M, Rey J.-F., Beaudouin R., Soubeyrand S., Messéan A. (in press). A spatio-temporal exposure-hazard model for assessing biological risk and impact. Risk Analysis. doi:10.1111/risa.12941
Associated R package: briskaR
[55] Leclerc M., Walker E., Messéan A., Soubeyrand S. (2017). Spatial exposure-hazard and landscape models for assessing the impact of GM crops on non-target organisms. Science of the Total Environment 624: 470-479. doi:10.1016/j.scitotenv.2017.11.329
[54] Soubeyrand S., Garetta V., Monteil C., Suffert F., Goyeau H., Berder J., Moinard J., Fournier E., Tharreau D., Morris C., Sache I. (2017). Testing differences between pathogen compositions with small samples and sparse data. Phytopathology 107: 1199-1208. doi:10.1094/PHYTO-02-17-0070-FI
Associated R package: GMCPIC, also available on the CRAN in the StrainRanking package 
[53] Picard C., Dallot S., Brunker K., Berthier K., Roumagnac P., Soubeyrand S., Jacquot E., Thébaud G. (2017). Exploiting Genetic Information to Trace Plant Virus Dispersal in Landscapes. Annual Review of Phytopathology 55. doi:10.1146/annurev-phyto-080516-035616
[52] Bordier C., Dechatre H., Suchail S., Peruzzi M., Soubeyrand S., Pioz M., Pélissier M., Crauser D., Le Conte Y., Alaux C. (2017). Colony adaptive response to simulated heat waves and consequences at the individual level in honeybees (Apis mellifera). Scientific Reports 7: 3760. doi:10.1038/s41598-017-03944-x
[51] Mrkvicka T., Soubeyrand S. (2017). On parameter estimation for doubly inhomogeneous cluster point processes. Spatial Statistics 20: 191-205. doi:10.1016/j.spasta.2017.03.005
[50] Soubeyrand S., Laine A.-L. (2017). When group dispersal and Allee effect shape metapopulation dynamics. Annales Zoologici Fennici 54: 123-138. PDF file.
[49] Morris C.E., Soubeyrand S., Bigg E.K., Creamean J.M., Sands D.C. (2017). Mapping rainfall feedback to reveal the potential sensitivity of precipitation to biological aerosols. Bulletin of the American Meteorological Society 98: 1109-1118. doi:10.1175/BAMS-D-15-00293.1
[48] Parisey N., Bourhis Y., Roques L., Soubeyrand S., Ricci B., Poggi S. (2016). Rearranging agricultural landscapes towards habitat quality optimisation: in silico application to pest regulation. Ecological Complexity 28: 113-122. doi:10.1016/j.ecocom.2016.07.003 
[47] Mrkvicka T., Soubeyrand S., Myllymäki M., Grabarnik P., Hahn U. (2016). Monte Carlo testing in spatial statistics, with applications to spatial residuals. Spatial Statistics 18: 40-53. doi:10.1016/j.spasta.2016.04.005
[46] Sajid A., Soubeyrand S., Gladieux P., Giraud T., Leconte M., Gautier A., Mboup M., Chen W., de Vallavieille-Pope C., Enjalbert J. (2016). CloNcaSe: Estimation of sex frequency and effective population size by clonemate re-sampling in partially clonal organisms. Molecular Ecology Resources 16: 845-861. doi:10.1111/1755-0998.12511
Associated R package: CloNcaSe.
[45] Soubeyrand S. (2016). Construction of semi-Markov genetic-space-time SEIR models and inference. Journal de la Société Française de Statistique 157: 129-152. PDF file.
[44] Roques L., Walker E., Franck P., Soubeyrand S., Klein E.K. (2016). Using genetic data to estimate diffusion rates in heterogeneous landscapes. Journal of Mathematical Biology 73: 397-422. doi:10.1007/s00285-015-0954-4
[43] Soubeyrand S., Haon-Lasportes E. (2015). Weak convergence of posteriors conditional on maximum pseudo-likelihood estimates and implications in ABC. Statistics and Probability Letters 107: 84-92. doi:10.1016/j.spl.2015.08.003
[42] Rimbaud L., Dallot S., Delaunay A., Borron S., Soubeyrand S., Thébaud G., Jacquot E. (2015). Assessing the mismatch between incubation and latency for vector-borne diseases: the case of sharka. Phytopathology 115: 1408-1416. doi:10.1094/PHYTO-01-15-0014-R
[41] Soubeyrand S., Sache I., Hamelin F., Klein E. K. (2015). Evolution of dispersal in asexual populations: to be independent, clumped or grouped? Evolutionary Ecology 29:947-963. doi:10.1007/s10682-015-9768-5
[40] Rimbaud L., Dallot S., Gottwald T., Decroocq V., Soubeyrand S., Jacquot E., Thébaud G. (2015). Sharka epidemiology and worldwide management strategies: learning lessons to optimize disease control in perennial plants. Annual Review of Phytopathology 53: 357-378. doi:10.1146/annurev-phyto-080614-120140
[39] Valdazo-Gonzalez B., Kim J. T., Soubeyrand S., Wadsworth J., Knowles N. J., Haydon D. T., King D. P. (2015). The impact of within-herd genetic variation upon inferred transmission trees for foot-and-mouth disease virus. Infection, Genetics and Evolution 32: 440-448. doi:10.1016/j.meegid.2015.03.032
[38] Bigg E. K., Soubeyrand S., Morris C. E. (2015). Persistent after-effects of heavy rain on concentrations of ice nuclei and rainfall suggest a biological cause. Atmospheric Chemistry and Physics 15: 2313-2326. doi:10.5194/acp-15-2313-2015
[37] Bousset L., Jumel S., Garreta V., Picault H., Soubeyrand S. (2015). Transmission of Leptosphaeria maculans from a cropping season to the following one. Annals of Applied Biology 166: 530-543. doi: 10.1111/aab.12205
[36] Penczykowski R. M., Walker E., Soubeyrand S., Laine A.-L. (2015). Linking winter conditions to regional disease dynamics in a wild plant–pathogen metapopulation. New Phytologist 205: 1142-1152. doi:10.1111/nph.13145
[35] Soubeyrand S., Morris C. E., Bigg E. K. (2014). Analysis of fragmented time directionality in time series to elucidate feedbacks in climate data. Environmental Modelling & Software 61: 78-86. doi:10.1016/j.envsoft.2014.07.003

Associated R package: FeedbackTS.

[34] Roques L., Chekroun M. D., Cristofol M., Soubeyrand S., Ghil M. (2014). Parameter estimation for energy balance models with memory. Proceedings of the Royal Society A 470: 20140349. doi:10.1098/rspa.2014.0349

[33] Rieux A., Soubeyrand S., Bonnot F., Klein E. K., Ngando J. E., Mehl A., Ravigné V., Carlier J., De Lapeyre de Bellaire L. (2014). Long-distance wind-dispersal of spores in a fungal plant pathogen: estimation of anisotropic dispersal kernels from an extensive field experiment. PLOS ONE 9(8): e103225. doi:10.1371/journal.pone.0103225

[32] Jombart T, Aanensen D, Baguelin M, Birrell P, Cauchemez S, Camacho A, Colijn C, Collins C, Cori A, Didelot X, Fraser C, Frost S, Hens N, Hugues J, Höhle M, Opatowski L, Rambaut A, Ratmann O, Soubeyrand S, Suchard MA, Wallinga J, Ypma R, Ferguson N (2014). OutbreakTools: a new platform for disease outbreak analysis using the R software. Epidemics 7: 28-34. doi:10.1016/j.epidem.2014.04.003

[31] Mollentze N., Nel L. H., Townsend S., le Roux K., Hampson K., Haydon D. T., Soubeyrand S. (2014). A Bayesian approach for inferring the dynamics of partially observed endemic infectious diseases from space-time-genetic data. Proceedings of the Royal Society B 281: 20133251. doi:10.1098/rspb.2013.3251

[30] Soubeyrand S., Tollenaere C., Haon-Lasportes E., Laine A.-L. (2014). Regression-based ranking of pathogen strains with respect to their contributions to natural epidemics. PLOS ONE 9(1): e86591. doi:10.1371/journal.pone.0086591
Associated R package: StrainRanking

[29] Soubeyrand S., Roques L. (2014). Parameter estimation for reaction-diffusion models of biological invasions. Population Ecology 56: 427-434. doi:10.1007/s10144-013-0415-0

[28] Soubeyrand S., Mrkvicka T., Penttinen A. (2014). A nonstationary cylinder-based model describing group dispersal in a fragmented habitat. Stochastic Models 30: 48-67. doi:10.1080/15326349.2014.868734

[27] Georgescu V., Desassis N., Soubeyrand S., Kretzschmar A., Senoussi R. (2014). An automated MCEM algorithm for hierarchical models with multivariate and multitype response variables. Communications in Statistics – Theory and Methods 43: 3698-3719. doi:10.1080/03610926.2012.700372

[26] Crété R., Pumo, B., Soubeyrand S., Didelot F., Caffier V. (2013). A continuous time-and-state epidemic model fitted to ordinal categorical data observed on a lattice at discrete times. Journal of Agricultural, Biological, and Environmental Statistics 18: 538-555. doi:10.1007/s13253-013-0138-x

[25] Dussaubat C., Maisonnasse A., Crauser D., Beslay D., Costagliola G., Soubeyrand S., Kretzchmar A., Le Conte Y. (2013). Flight behavior and pheromone changes associated to Nosema ceranae infection of honey bee workers (Apis mellifera) in field conditions. Journal of Invertebrate Pathology 113: 42-51. doi:10.1016/j.jip.2013.01.002

[24] Soubeyrand S., Carpentier F., Guiton F., Klein E. K. (2013). Approximate Bayesian computation with functional statistics. Statistical Applications in Genetics and Molecular Biology 12: 17-37. doi:10.1515/sagmb-2012-0014

[23] Morelli M. J., Thébaud G., Chadoeuf J., King, D. P., Haydon D. T., Soubeyrand S. (2012). A Bayesian inference framework to reconstruct transmission trees using epidemiological and genetic data. PLOS Computational Biology 8(11): e1002768. doi:10.1371/journal.pcbi.1002768

[22] Allard D., Soubeyrand S. (2012). Skew-normality for climatic data and dispersal models for plant epidemiology: when application fields drive spatial statistics. Spatial Statistics 1: 50-64. doi:10.1016/j.spasta.2012.03.001

[21] Monteil C., Guilbaud C., Glaux C., Lafolie F., Soubeyrand S., Morris C. (2012). Emigration of the plant pathogen Pseudomonas syringae from leaf litter contributes to its population dynamics in alpine snowpack. Environmental Microbiology 14: 2099-2112. doi:10.1111/j.1462-2920.2011.02680.x

[20] Bourgeois A., Gaba S., Munier-Jolain N., Borgy B., Monestiez, Soubeyrand S. (2012). Inferring weed spatial distribution from multi-type data. Ecological Modelling 226: 92-98. Download real data set and simulated data set analyzed in the article. doi:10.1016/j.ecolmodel.2011.10.010

[19] Mrkvicka T., Soubeyrand S., Chadœuf J. (2012). Goodness-of-fit test of the mark distribution in a point process with non-stationary marks. Statistics and Computing 22: 931-943. doi:10.1007/s11222-011-9263-y

[18] Roques L., Soubeyrand S., Rousselet J. (2011). A statistical-reaction-diffusion approach for analyzing expansion processes. Journal of Theoretical Biology 274: 43-51. doi:10.1016/j.jtbi.2011.01.006

[17] Soubeyrand S., Roques L., Coville J., Fayard J. (2011). Patchy patterns due to group dispersal. Journal of Theoretical Biology 271: 87-99. doi:10.1016/j.jtbi.2010.11.047

[16] Kretzschmar A., Soubeyrand S., Desassis N. (2010). Aggregation patterns in hierarchy/proximity spaces. Ecological Complexity 7: 21-31. doi:10.1016/j.ecocom.2009.03.012

[15] Soubeyrand S., Carpentier F., Desassis N., Chadœuf J. (2009). Inference with a contrast-based posterior distribution and application in spatial statistics. Statistical Methodology 6: 466-477. doi:10.1016/j.stamet.2009.03.003 (previous version in French)

[14] Soubeyrand S., Laine A.L., Hanski I., Penttinen A. (2009). Spatio-temporal structure of host-pathogen interactions in a metapopulation. The American Naturalist 174: 308-320. doi:10.1086/603624

[13] Georgescu V., Soubeyrand S., Kretzschmar A., Laine A.-L. (2009). Exploring spatial and multitype assemblages of species abundances. Biometrical Journal 51: 979-995. doi:10.1002/bimj.200900055
[12] Soubeyrand S., Enjalbert J., Kretzschmar A., Sache I. (2009). Building anisotropic sampling schemes for the estimation of anisotropic dispersal. Annals of Applied Biology 154: 399-411. doi:110.1111/j.1744-7348.2008.00310.x
Associated R code.
[11] Soubeyrand S., Neuvonen S., Penttinen A. (2009). Mechanical-statistical modeling in ecology: from outbreak detections to pest dynamics. Bulletin of Mathematical Biology 71: 318-338. doi:10.1007/s11538-008-9363-9

[10] Soubeyrand S., Held L., Hohle M., Sache I. (2008). Modelling the spread in space and time of an airborne plant disease. Journal of the Royal Statistical Society C 57: 253-272. doi:10.1111/j.1467-9876.2007.00612.x
[9] Lannou C., Soubeyrand S., Frezal L., Chadœuf J. (2008). Autoinfection in wheat leaf rust epidemics. The New Phytologist 177: 1001-1011. doi:10.1111/j.1469-8137.2007.02337.x

[8] Soubeyrand S., Enjalbert J. & Sache I. (2008). Accounting for roughness of circular processes: Using Gaussian random processes to model the anisotropic spread of airborne plant disease. Theoretical Population Biology 73: 92-103. doi:10.1016/j.tpb.2007.09.005

[7] Soubeyrand S., Beaudouin R., Desassis N., Monod G. (2007). Model-based estimation of the link between the daily survival probability and a time-varying covariate, application to mosquitofish survival data. Mathematical Biosciences 210: 508-522. doi:10.1016/j.mbs.2007.06.005

[6] Soubeyrand S., Enjalbert J., Sanchez A., Sache I. (2007). Anisotropy, in density and in distance, of the dispersal of yellow rust of wheat: Experiments in large field plots and estimation. Phytopathology 97: 1315-1324. doi:10.1094/PHYTO-97-10-1315

[5] Soubeyrand S., Thébaud G., Chadœuf J. (2007). Accounting for biological variability and sampling scale: a multi-scale approach to building epidemic models. Journal of the Royal Society Interface 4: 985-997. doi:10.1098/rsif.2007.1154

[4] Soubeyrand S., Chadœuf J. (2007). Residual-based specification of a hidden random field included in a hierarchical spatial model. Computational Statistics and Data Analysis 51: 6404-6422. doi:10.1016/j.csda.2007.02.008

[3] Soubeyrand S., Sache I., Lannou C., Chadœuf J. (2007). A frailty model to assess plant disease spread from individual count data. Journal of Data Science 5: 67-83. Pdf file

[2] Bénard-Capelle J., Soubeyrand S., Neema C. (2006). Reproductive consequences of Colletotrichum lindemuthianum (Ascomycota) infection on wild bean plants (Phaseolus vulgaris). Canadian Journal of Botany 84: 1542-1547. doi:10.1139/B06-114

[1] Soubeyrand S., Chadœuf J., Sache I., Lannou C. (2006). Residual-based specification of the random-effects distribution for cluster data. Statistical Methodology 3: 464-482. doi:10.1016/j.stamet.2005.12.005

Research Reports and Books:

[r16] Soubeyrand S. (2017). Review of Hierarchical Modeling and Analysis for Spatial Data by Banerjee, S., Carlin, BP, and Gelfand, AE. Mathematical Geosciences, 49: 677-678. doi:10.1007/s11004-016-9668-4

[r15] Picard C., Rimbaud L., Hendrikx P., Soubeyrand S., Jacquot E. and Thébaud G. (2017). PESO: a modelling framework to help improve management strategies for epidemics – application to sharka. EPPO Bulletin 47: 231-236. doi:10.1111/epp.12375

[r14] Soubeyrand S. (2016). Contributions to Statistical Plant and Animal Epidemiology. Mémoire d'HDR, Aix-Marseille Université. PDF file.

[r13] Walker E. and Soubeyrand S. (2016). Hamiltonian Monte Carlo in practice. BioSP research report N°49. PDF file.

[r12] Collectif BIOBAYES (2015). Initiation à la Statistique Bayésienne - Bases Théoriques et Applications en Alimentation, Environnement, Epidémiologie et Génétique. Editions Ellipses.
Collectif BIOBAYES: Albert I., Ancelet S., David O., Denis J.-B., Makowski D., Parent E., Rau A. and Soubeyrand S.

Associated programs: click on this link.

[r11] Rimbaud L., Delaunay A., Soubeyrand S., Jacquot E. & Thébaud, G. (2015). Model-based optimization of an experimental protocol to assess the mismatch between incubation and latency periods for plum pox virus. Acta Horticulturae (ISHS) 1063:159-166. http://www.actahort.org/books/1063/1063_22.htm

[r10] Roques L., Rossi J.-P., Berestycki H., Rousselet J., Garnier J., Roquejoffre J.-M., Rossi L., Soubeyrand S. & Robinet C. (2015). Modeling the spatio-temporal dynamics of the pine processionary moth (pp. 227-263). In Roques A. (Ed.) Processionary Moths and Climate Change: An Update. Springer Netherlands. doi:10.1007/978-94-017-9340-7_5
[r9] Soubeyrand S. (2014). Qui a infecté qui ? La statistique enquête sur le temps, l'espace et la génétique (pp. 64-65). In Andler M., Bel L., Benzoni S., Goudon T., Imbert C. & Rousseau A. (Eds.) Brèves de Maths - Mathématiques de la Planète Terre. Nouveau Monde Editions, Paris. ISBN: 978-2-36583-896-2.PDF file.
[r8] Soubeyrand S. & Roques L. (2013). Problèmes inverses et estimations de paramètres. PDF file. In: Roques L. (Author). Modèles de Réaction-Diffusion pour l'Ecologie Spatiale. Editions QUAE, Versailles. ISBN: 9782759220298.
[r7] Lannou C. & Soubeyrand S. (2015). Measure of life-cycle traits of a biotrophic pathogen (pp.149-152). In Stevenson K.L. and Jeger M.J. (Eds.) Exercices in Plant Disease Epidemiology, 2nd edition. The American Phytopathological Society, St. Paul, Minnesota. PDF file.
[r6] Soubeyrand S. (2012). Evaluation des distributions a posteriori à l'aide de méthodes numériques (pp. 91-119). In Makowski D. (Ed.) Méthodes statistiques bayésiennes - Bases théoriques et applications en alimentation, environnement et génétique. INRA FormaSciences.
[r5] Georgescu V., Desassis N., Soubeyrand S., Kretzschmar A. and Senoussi R. (2010). Clustering based on multivariate mixed-mode mixtures.
[r4] Haon-Lasportes E., Carpentier F., Martin O., Klein E. K. and Soubeyrand S. (2011). Conditioning on parameter point estimates in approximate Bayesian computation. BioSP research report N°45. PDF file.
[r3] Soubeyrand S. & Sache I. (2010). Analyse des phases précoces d'une épidémie affectant les organes aériens des plantes: Application aux rouilles du blé. Chapitre 14 dans Barnouin J. & Sache I. (Eds.) Les maladies émergentes. Epidémiologies chez le végétal, l'animal et l'homme. QUAE Editions. Pdf file.
[r2] Soubeyrand S. (2005). Spécifier un processus caché non modélisé en déterminant le lien asymptotique entre résidus et processus caché. Application à l'analyse de la variabilité dans les expériences de propagation des rouilles du blé. Thèse de doctorat, Université Montpellier 2. Pdf file.
[r1] Chadœuf J., Fady B., Goreaud F., Pontailler J. Y. & Soubeyrand S. (2005). Tests non-paramétriques d'indépendance de la répartition d'objets complètement observables distribués dans le plan. Compte rendu #220517001, Institut de l'Elevage. Pdf file.



Accreditation to supervise research in sciences (HDR en Sciences, 2016, Université Aix-Marseille, France)

Doctorate in biostatistics (2002-2005, University of Montpellier 2, France)

Research master in fundamental mathematics and applications, specialization in statistics (2001-2002, University of Rennes 1, France)
Master in statistics and information analysis (1999-2002, ENSAI, Rennes, France)
Licence in economical sciences (2000-2001, University of Rennes 1, France)
Undergraduation in mathematics and physics (1996-1999, Classe préparatoire MP, Lycée Paul Cézanne, Aix-en-Provence, France)



Director of research (2017-present) from the Plant Health and Environment (SPE) department of INRA, located in the "Biostatistics and Spatial Processes" research unit of INRA Avignon, France


Junior researcher (2008-2016) from the Plant Health and Environment (SPE) department of INRA, located in the "Biostatistics and Spatial Processes" research unit of INRA Avignon, France

- visiting researcher at the Institute of Biodiversity, Animal Helath and Comparative Medicine, University of Glasgow, with Daniel Haydon (Sep. 2012 - Feb. 2013)

Postdoctoral position (2005-2008) funded by INRA and spent at

- the "Biostatistics and Spatial Processes" research unit of INRA Avignon, France
- the Dpt. of Statistics of the university of Munich, Germany, with Leonhard Held (Apr. 2006 - Aug. 2006)
- the Dpt. of Mathematics and Statistics of the University of Jyväskylä, Finland, with Antti Penttinen (Jan. 2007 - Dec. 2007)

Doctoral position (2002-2005) funded by INRA and spent at

- the "Botanical Epidemiology and Population Biology" research unit of INRA Versailles-Grignon, France, under the supervision of Dr. I. Sache
- the "Biostatistics and Spatial Processes" research unit of INRA Avignon, France under the supervision of Dr. J. Chadœuf

Visiting student (March 2002 - Aug. 2002) at the University of Chicago, USA, under the supervision of Pr. M. L. Stein.

Visiting student (Summer 2001) at the "Biometrics" research unit of INRA Avignon, France, under the supervision of Dr. J. Chadœuf.



R packages, scripts, ...

RainfallFeedbackMaps [49]

GMCPIC package: Test the equality of vectors of probabilities [54]

StrainRanking package: Ranking of pathogen strains [30]

FeedbackTS package: Analysis of feedback in time series [35]

CloNcaSe package: Estimation of sex rate and effective size [r6]

Anisotropic dispersal: Script for parameter estimation and sample design [12]



SMITID Project (ANR): Statistical Methods to Infer Transmissions of Infectious Diseases from deep sequencing data

ModStatSAP Network (SPE--MIA--SA): Modeling and statistics for animal and crop health

STrATEGE Network (MIA): Modeling and statistics for animal and crop health

AMIGA Project (Europe): Assessing and monitoring the impact of genetically modified plants on agro-ecosystems

PLANTFOODSEC Project (Europe): Plant and Food Biosecurity

EMILE Project (ANR): Inference methods and software for evolutionary studies

GROUP DISPERSAL Project (SPE): Building a body of theory for group dispersal in plant epidemiology



Abeille Road: A movie presenting a scientific study on bees coordinated by André Kretzschmar from BioSP. The trailer. The complete movie.


Illustration. Textbook by the Collectif BIOBAYES published by Editions Ellipses in 2015 [r12].

Illustration. Estimated exogenous transmissions (orange dots) and endogenous transmissions (blue arrows) in a set of observed rabid animals in South Africa; see research report [31].

Couverture Roques L. (2013)

Illustration. Textbook by Lionel Roques published by QUAE in 2013 and including reference [r8].

Population pattern with group dispersal

Population pattern without group dispersal

Illustration. Population patterns obtained under two models: one with group dispersal (top) and the other without (bottom). Both models have the same marginal dispersal function. See publication [17].

Simulated spred of an epidemic

Illustration. Simulated spread of an airborne plant disease in a metapopulation. See publication [14] about the dynamics of the powdery mildew of Plantago lanceolata in Åland Islands, Finland.

Anisotropic dispersal (von Mises functions)

Anisotropic dispersal (Gaussian processes)

Illustration. Estimation of the anisotropic dispersal of wheat rust spores by a point source. Top: model with circular von Mises functions; Bottom: model with circular Gaussian processes. See publication [8].