Homepage Samuel Soubeyrand


Samuel Soubeyrand

INRA - BioSP - 228 rte de l'aérodrome
84914 Avignon Cedex 9 - France

Packages, web tools, and scripts
SLAFEEL: Statistical Learning Approach For Estimating Epidemiological Links  from deep sequencing data
RainfallFeedbackMaps: Maps to explore where rainfall feedback could be occurring
briskaR: Biological risk assessment with R
GMCPIC: Testing of the equality of vectors of probabilities (also available in StrainRanking)
StrainRanking: Ranking of pathogen strains
FeedbackTS: Analysis of feedback in time series
CloNcaSe: Estimation of sex rate and effective size
Anisotropic dispersal: Script for parameter estimation and sample design
BIOBAYES: Scripts associated with the eponymous book

Collectif BIOBAYES (2015) - Editions Ellipses  

Main projects 
SMITID (ANR, 2016-2020; PI)
BriskaR-NTL (EFSA, 2018-2020)
XF-ACTORS (EU, 2016-2020)
AMIGA (EU, 2011-2016)
PlantFoodSec (EU, 2011-2016)
EMILE (ANR, 2009-2013; WP coord.)
Group Dispersal (SPE, 2010-2012; PI)
Research interests - Statistics, data analysis,  spatio-temporal modeling, spatial point processes, epidemiological models, mechanistic-statistical modeling, outbreak reconstruction from genomic data, pathogen spread, particle dispersal, exposure-hazard models, pathogen emergence

Research position
Since 2017: Director of research from the Plant Health and Environment (SPE) department of INRA, located in the BioSP research unit at Avignon, France
Earlier positions and education

Administrative roles
Since 2019: Director of the BioSP research unit 
Since 2018: Member of the coordination team of the French national platform for epidemiosurveillance in plant health

ModStatSAP network - funded by the SPE, MIA and SA departments of INRA - Modeling and statistics for animal and crop health

Morelli et al. (2012) - PLoS Computational Biology The accurate identification of the route of transmission taken by an infectious agent through a host population is critical to understanding its epidemiology and informing measures for its control [...].  We present here a framework leading to a Bayesian inference scheme that combines genetic and epidemiological data, able to reconstruct most likely transmission patterns and infection dates... doi:10.1371/journal.pcbi.1002768

Soubeyrand et al. (2009) - The American Naturalist - The ecological and evolutionary dynamics of species are influenced by spatiotemporal variation in population size. Unfortunately, we are usually limited in our ability to investigate the numerical dynamics of natural populations across large spatial scales and over long periods of time. Here we combine mechanistic and statistical approaches to reconstruct continuous‐time infection dynamics of an obligate fungal pathogen... doi:10.1086/603624

PhD students
Maria Choufany (main supervisor)
Maryam Alamil (main supervisor)
Candy Abboud (main supervisor)
Ismaël Houillon
Coralie Picard
Loup Rimbaud
Vera Georgescu

Virgile Baudrot
Emily Walker
Melen Leclerc
Vincent Garetta
David Pleydell
Constance Xhaard
Full publication list - Click here

Martinetti and Soubeyrand (2019) - Phytopathology - Recent detections of Xylella fastidiosa in Corsica Island, France, has raised concerns on its possible spread to mainland France and the rest of the Mediterranean Basin. [...] We present a new methodological approach for the design of risk-based surveillance strategies... doi:10.1094/PHYTO-07-18-0237-FI

SMITID project (2016-2020) - ANR grant ANR-16-CE35-0006 - Statistical Methods for Inferring Transmissions of Infectious Diseases from deep sequencing data

Mrkvicka and Soubeyrand (2017) - Spatial Statistics - Nowadays, spatial inhomogeneity and clustering are two important features frequently observed in point patterns. These features often reveal heterogeneity of processes/factors involved in the point pattern formation and interaction determining the relative locations of points. [...] In this article, we consider cluster point processes with double inhomogeneity... doi:10.1016/j.spasta.2017.03.005

Soubeyrand (2016) - Journal de la SFdS - Identifying transmission links of an infectious disease through a host population is critical to understanding its epidemiology and informing measures for its control. To infer more reliably who-transmitted-to-whom [...], we present [an approach] that combines (i) an individual-based, spatial, semi-Markov SEIR model for the spatio-temporal dynamics of the pathogen, and (ii) a Markovian evolutionary model for the temporal evolution of genetic sequences of the pathogen... http://journal-sfds.fr/article/view/524

Mrkvicka et al. (2016) - Spatial Statistics - This paper reviews recent advances made in testing in spatial statistics and discussed at the Spatial Statistics conference in Avignon 2015. The rank and directional quantile envelope tests are discussed and practical rules for their use are provided...  doi:10.1016/j.spasta.2016.04.005

Soubeyrand and Haon-Lasportes (2015) - Statistics and Probability Letters - The weak convergence of posterior distributions conditional on maximum pseudo-likelihood estimates (MPLE) is studied and exploited to justify the use of MPLE as summary statistics in approximate Bayesian computation (ABC). Our study could be generalized by replacing the pseudo-likelihood by other estimating functions (e.g. quasi-likelihoods and contrasts)... doi:10.1016/j.spl.2015.08.003
Walker et al. (2019) - Risk Analysis - 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... doi:10.1111/risa.12941
See also the R package briskaR (Biological Risk Assessment with R)  

BriskaR-NTL project (2018-2020) - EFSA grant OC/EFSA/GMO/2018/01 - Development of a spatially and temporally explicit model to quantify risks to non-target Lepidoptera

Soubeyrand et al. (2018) - New Phytologist - Unravelling the ecological structure of emerging plant pathogens persisting in multi‐host systems is challenging. In such systems, observations are often heterogeneous [...]. We designed a mechanistic‐statistical approach to help understand the ecology of emerging pathogens [...]. We applied our approach to French surveillance data on Xylella fastidiosa [...]. Xylella fastidiosa was probably introduced into Corsica much earlier than its discovery... doi:10.1111/nph.15177

Leyronas et al. (2018) - Frontiers in Microbiology - Many phytopathogenic fungi are disseminated as spores via the atmosphere from short to long distances. The distance of dissemination determines the extent to which plant diseases can spread and novel genotypes of pathogens can invade new territories. [...] The objective of the present study was to determine the interconnectivity of reservoirs of S. sclerotiorum from distant regions based on networks of air mass movement... doi:10.3389/fmicb.2018.02257

Picard et al. (2017) - Annual Review of Phytopathology - During the past decade, knowledge of pathogen life history has greatly benefited from the advent and development of molecular epidemiology. [...] Here, we review molecular epidemiology approaches that have been developed to trace plant virus dispersal in landscapes... doi:10.1146/annurev-phyto-080516-035616

Mollentze et al. (2014) - Proceedings of the Royal Society B We describe a statistical framework for reconstructing the sequence of transmission events between observed cases of an endemic infectious disease using genetic, temporal and spatial information... doi:10.1098/rspb.2013.3251 

Soubeyrand et al. (2014) - Environmental Modelling & Software Identifying and characterizing feedbacks in environmental processes may help in improving predictions for some environmental systems. The statistical study of time series is a manner to approach these feedbacks... doi:10.1016/j.envsoft.2014.07.003
See also the R package FeedbackTS and the web tool RainfallFeedbackMaps 

Soubeyrand et al. (2008) - Journal of the Royal Statistical Society C - A spatiotemporal model is developed to analyse epidemics of airborne plant diseases which are spread by spores [...]. Maximum likelihood combined with a parametric bootstrap is proposed to estimate model parameters... doi:10.1111/j.1467-9876.2007.00612.x