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[90] Morris C.E., Radici A., Meynard C.N., Sauvion N., Nedellec C., Geniaux G., Soubeyrand S. (2024). More than food: Why restoring the cycle of organic matter in sustainable plant production is essential for the One Health nexus. CABI Reviews, 19:1. https://doi.org/10.1079/cabireviews.2024.0008

[89] Saubin M., Coville J., Xhaard C., Frey P., Soubeyrand S., Halkett F., Fabre F. (2024). A mechanistic-statistical approach to infer dispersal and demography from invasion dynamics, applied to a plant pathogen. Peer Community Journal, 4:e9. https://doi.org/10.24072/pcjournal.356

[88] Boixel A.-L., Goyeau H., Berder J., Moinard J., Suffert F., Soubeyrand S., Sache I., Vidal T. (2023). A landscape-scale field survey demonstrates the role of wheat volunteers as a local and diversified source of leaf rust inoculum. Scientific Reports, 13:20411. https://doi.org/10.1038/s41598-023-47499-6

[87] Richard H., Martinetti D., Lercier D., Fouillat Y., Hadi B., Elkahky M., Ding J., Michel L., Morris C.E., Berthier K., Maupas F., Soubeyrand S. (2023). Computing geographical networks generated by air-mass movement. GeoHealth 7:e2023GH000885. https://doi.org/10.1029/2023GH000885 

[86] Ribaud M., Gabriel E., Hughes J., Soubeyrand S. (2023). Identifying potential significant factors impacting zero-inflated proportion data. Statistics in Medicine 42:3467-3486. http://doi.org/10.1002/sim.9814 - Preprint: https://hal.inrae.fr/hal-02936779v4

[85] Abboud C., Parent E., Bonnefon O., Soubeyrand S. (2023). Forecasting pathogen dynamics with Bayesian model-averaging: Application to Xylella fastidiosa. Bulletin of Mathematical Biology 85:67. https://doi.org/10.1007/s11538-023-01169-w

[84] Roques L., Boivin T., Papaïx J., Soubeyrand S., Bonnefon O. (2023). Dynamics of Aedes albopictus invasion. Insights from a spatio-temporal model. Biological Invasions 25:2679–2695. https://doi.org/10.1007/s10530-023-03062-y 

[83] Alamil M., Thébaud G., Berthier K., Soubeyrand S. (2022). Characterizing viral within-host diversity in fast and non-equilibrium demo-genetic dynamics. Frontiers in Microbiology 13:983938. https://doi.org/10.3389/fmicb.2022.983938

[82] Roques L., Allard D., Soubeyrand S. (2022). Spatial statistics and stochastic partial differential equations: a mechanistic viewpoint. Spatial Statistics 50:100591. https://doi.org/10.1016/j.spasta.2022.100591

[81] Eck J.L., Barrès B., Soubeyrand S., Sirén J., Numminen E., Laine A.-L. (2022). Strain diversity and spatial distribution are linked to epidemic dynamics in host populations. The American Naturalist 199:59-74. https://doi.org/10.1086/717179

[80] Morris C.E., Géniaux G., Nédellec C., Sauvion N., Soubeyrand S. (2022). One Health concepts and challenges for surveillance, forecasting, and mitigation of plant disease beyond the traditional scope of crop production. Plant Pathology 71:86-97. https://doi.org/10.1111/ppa.13446

[79] Nembot Fomba C.G., Ten Hoopen G.M., Soubeyrand S., Roques L., Ambang Z., Takam Soh, P. (2021). Parameter estimation in a PDE model for the spatial spread of cocoa black pod disease. Bulletin of Mathematical Biology 83: 101. https://doi.org/10.1007/s11538-021-00934-z

[78] Choufany M., Martinetti D., Soubeyrand S., Morris C.E. (2021). Inferring long-distance connectivity shaped by air-mass movement for improved experimental design in aerobiology. Scientific Reports 11: 11093. https://doi.org/10.1038/s41598-021-90733-2

[77] Dechatre H., Michel L., Soubeyrand S., Maisonnasse A., Moreau P., Poquet Y., Pioz M., Vidau C., Basso B., Mondet F., Kretzschmar A. (2021). To treat or not to treat bees? Handy VarLoad: A predictive model for varroa destructor load. Pathogens 10: 678. https://doi.org/10.3390/pathogens10060678

[76] Roques L., Desbiez C., Berthier K., Soubeyrand S., Walker E., Klein E.K., Garnier J., Moury B., Papaïx J. (2021). Emerging strains of watermelon mosaic virus in Southeastern France: model-based estimation of the dates and places of introduction. Scientific Reports 11: 7058. https://doi.org/10.1038/s41598-021-86314-y

[75] Choufany M., Martinetti D., Senoussi R., Morris C.E., Soubeyrand S. (2021). Spatiotemporal large-scale networks shaped by air mass movements. Frontiers in Applied Mathematics and Statistics 6:602621. https://doi.org/10.3389/fams.2020.602621

[74] Baudrot V., Walker E., Lang A., Stefanescu C, Rey J.-F., Soubeyrand S., Messéan A. (2021). When the average effect hides the risk of Bt-corn pollen on Non-Target Lepidoptera: application to Aglais io in Catalonia. Ecotoxicology and Environmental Safety 207: 111215.https://doi.org/10.1016/j.ecoenv.2020.111215

[73] Martin O., Fernandez-Diclo Y., Coville J., Soubeyrand S. (2021). Equilibrium and sensitivity analysis of a spatio-temporal host-vector epidemic model. Nonlinear Analysis: Real World Applications 57: 103194.https://doi.org/10.1016/j.nonrwa.2020.103194

[72] Roques L., Bonnefon O., Baudrot V., Soubeyrand S., Berestycki H. (2020). A parsimonious approach for spatial transmission and heterogeneity in the COVID-19 propagation. Royal Society Open Science 7: 201382.https://doi.org/10.1098/rsos.201382

[71] Soubeyrand S., Demongeot J., Roques L. (2020). Towards unified and real-time analyses of outbreaks at country-level during pandemics. One Health 11: 100187.https://doi.org/10.1016/j.onehlt.2020.100187

[70] Soubeyrand S., Ribaud M., Baudrot V., Allard D., Pommeret D., Roques L. (2020). COVID-19 mortality dynamics: The future modelled as a (mixture of) past(s). PLoS ONE 15(9): e0238410. https://doi.org/10.1371/journal.pone.0238410

[69] Roques L., Klein E.K., Papaïx J., Sar A., Soubeyrand S. (2020). Impact of lockdown on the epidemic dynamics of COVID-19 in France. Frontiers in Medicine 7: 274. https://doi.org/10.3389/fmed.2020.00274

[68] Roques L., Klein E.K., Papaïx J., Sar A., Soubeyrand S. (2020). Using early data to estimate the actual infection fatality ratio from COVID-19 in France. Biology 9: 97. https://doi.org/10.3390/biology9050097

[67] Dvořák J., Mrkvička T., Møller J., Soubeyrand S. (2019). Quick inference for log Gaussian Cox processes with non-stationary underlying random fields. Spatial Statistics 33: 100388. https://doi.org/10.1016/j.spasta.2019.100388

[66] Picard C., Soubeyrand S., Jacquot E., Thébaud G. (2019). Analyzing the Influence of Landscape Aggregation on Disease Spread to Improve Management Strategies. Phytopathology, PHYTO-05. https://doi.org/10.1094/PHYTO-05-18-0165-R

[65] Abboud C., Bonnefon O., Parent E., Soubeyrand S. (2019). Dating and localizing an invasion from post-introduction data and a coupled reaction-diffusion-absorption model. Journal of Mathematical Biology 79: 765-789. https://doi.org/10.1007/s00285-019-01376-x

[64] Alamil M, Hughes J., Berthier K., Desbiez C., Thébaud G., Soubeyrand S. (2019). Inferring epidemiological links from deep sequencing data: a statistical learning approach for human, animal and plant diseases. Philosophical Transactions of the Royal Society B: Biological Sciences 374: 20180258. https://doi.org/10.1098/rstb.2018.0258

[63] Rimbaud L., Dallot S., Bruchou C., Thoyer S., Jacquot E., Soubeyrand S., Thébaud G. (2019). Improving management strategies of plant diseases using sequential sensitivity analyses. Phytopathology 109: 1184-1197. https://doi.org/10.1094/PHYTO-06-18-0196-R

[62] Martinetti D., Soubeyrand S. (2019). Identifying lookouts for epidemio-surveillance: application to the emergence of Xylella fastidiosa in France. Phytopathology 109: 265-276. https://doi.org/10.1094/PHYTO-07-18-0237-FI

[61] Walker E., Leclerc M, Rey J.-F., Beaudouin R., Soubeyrand S., Messéan A. (2019). A spatio-temporal exposure-hazard model for assessing biological risk and impact. Risk Analysis 39: 54-70. https://doi.org/10.1111/risa.12941

Associated R package: briskaR

[60] Alaux C., Soubeyrand S., Prado A., Peruzzi M., Maisonnasse A., Valon J., Hernandez J., Jourdan P., Le Conte Y. (2018). Measuring biological age to assess colony demographics in honeybees. PLoS ONE 13(12): e0209192. https://doi.org/10.1371/journal.pone.0209192

[59] 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. https://doi.org/10.3389/fmicb.2018.02257

[58] 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. https://doi.org/10.1111/nph.15177

[57] 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. https://doi.org/10.1371/journal.pcbi.1006085

[56] 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. https://doi.org/10.1098/rsos.171435

[55] Leclerc M., Walker E., Messéan A., Soubeyrand S. (2018). 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. https://doi.org/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. https://doi.org/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. https://doi.org/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. https://doi.org/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. https://doi.org/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. https://www.jstor.org/stable/44685831.

[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. https://doi.org/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. https://doi.org/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. https://doi.org/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. https://doi.org/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. https://doi.org/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. https://doi.org/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. https://doi.org/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. https://doi.org/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. https://doi.org/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. https://doi.org/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. https://doi.org/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. https://doi.org/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. https://doi.org/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. https://doi.org/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. https://doi.org/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. https://doi.org/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. https://doi.org/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. https://doi.org/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. https://doi.org/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. https://doi.org/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. https://doi.org/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. https://doi.org/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. https://doi.org/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. https://doi.org/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. https://doi.org/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. https://doi.org/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. https://doi.org/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. https://doi.org/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. https://doi.org/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. https://doi.org/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. https://doi.org/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. https://doi.org/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. https://doi.org/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. https://doi.org/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. https://doi.org/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. https://doi.org/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. https://doi.org/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. https://doi.org/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. https://doi.org/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. https://doi.org/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. https://doi.org/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. https://doi.org/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. https://doi.org/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. https://doi.org/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. https://doi.org/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. https://doi.org/10.6339/JDS.2007.05(1).315      

[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. https://doi.org/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. https://doi.org/10.1016/j.stamet.2005.12.005


Books and book chapters

Lannou C., Rasplus J.-Y., Soubeyrand S., Gautier M., Rossi J.-P., eds. (2023). Crises sanitaires en agriculture: Les espèces invasives sous surveillance. Editions Quæ, Versailles, 326 p. ISBN: 978-2-7592-3482-0

Sache I., Carpentier F., Desprez-Loustau M.-L., Papaïx J., Savary S., Soubeyrand S., Suffert F. (2023). Partie 4: Epidémiologie. In Phytopathology, 2ème édition (eds Reignault P., Sache I., Choquet M., Corio-Costet M.-F., Dellagi A., Suffert F.). De Boeck Supérieur, Louvain-la-Neuve, 384 p. ISBN: 9782807302884

Roques L., Soubeyrand S. (2023). Les invasions biologiques à la lumière des modèles. In Crises sanitaires en agriculture: Les espèces invasives sous surveillance (eds Lannou C., Rasplus J.-Y., Soubeyrand S., Gautier M., Rossi J.-P.). Editions Quæ, Versailles, Chap. 11. ISBN: 978-2-7592-3482-0

Jacques M.-A., Soubeyrand S., Dupas E. (2023). Décrypter l'émergence de Xylella fastidiosa en Europe et surveiller son expansion. In Crises sanitaires en agriculture: Les espèces invasives sous surveillance (eds Lannou C., Rasplus J.-Y., Soubeyrand S., Gautier M., Rossi J.-P.). Editions Quæ, Versailles, Chap. 14. ISBN: 978-2-7592-3482-0

Papaïx J., Soubeyrand S., Bonnefon O., Walker E., Louvrier J., Klein E., Roques L. (2022). Inferring Mechanistic Models in Spatial Ecology Using a Mechanistic-Statistical Approach. In Statistical Approaches for Hidden Variables in Ecology (eds N. Peyrard and O. Gimenez). https://doi.org/10.1002/9781119902799.ch4

Abboud C., Senoussi R., Soubeyrand S. (2018). Piecewise‐deterministic Markov Processes for Spatio‐temporal Population Dynamics. In Azais R., Bouguet F. (eds) Statistical Inference for Piecewise-deterministic Markov Processes. John Wiley & Sons, pp. 209-255. Preprint pdf filehttps://doi.org/10.1002/9781119507338.ch7

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.

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. https://doi.org/10.1007/978-94-017-9340-7_5

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.

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.

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.

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.

R code for the application of sampling-importance-resampling to a simulated metapopulation data set.

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. and Sache I. (Eds.) Les maladies émergentes. Epidémiologies chez le végétal, l'animal et l'homme. QUAE Editions. Pdf file.



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

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.



Soubeyrand S., Martinez C. (2023) Récents progrès sur l’utilisation des images satellitaires pour évaluer la sévérité de la jaunisse au niveau parcellaire. https://hal.inrae.fr/hal-04254586

Chauvin D., Soubeyrand S. (2023). Explorer les facteurs de risque de la jaunisse de la betterave. hal-03923229. https://hal.inrae.fr/hal-03923229

Baudrot V., Lang A., Stefanescu C., Soubeyrand S., Messéan A. (2021). Extension of the spatially- and temporally-explicit “briskaR-NTL” model to assess potential adverse effects of Bt-maize pollen on non-target Lepidoptera at landscape level. EFSA supporting publication. 18:EN-6443. https://doi.org/10.2903/sp.efsa.2021.EN-6443

Silvain J.-F., Goffaux R., Soubelet H., Sarrazin F., Abbadie L., et al.. Mobilisation de la FRB par les pouvoirs publics français sur les liens entre Covid-19 et biodiversité. FRB. 2020, 57 p. https://hal.inrae.fr/hal-02951526

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. https://doi.org/10.1007/s11004-016-9668-4

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. https://doi.org/10.1111/epp.12375

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

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

Georgescu V., Desassis N., Soubeyrand S., Kretzschmar A. and Senoussi R. (2010). Clustering based on multivariate mixed-mode mixtures.

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.

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.