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Samuel Soubeyrand

@ssoubeyrand.bsky.social
85 followers 64 following 53 posts

Researcher at INRAE - Model construction, statistical inference, and their application to epidemiology, ecology... BioSP - INRAE - Avignon, France samuel.biosp.org

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Samuel Soubeyrand @ssoubeyrand.bsky.social · 13/11/2025
We are pleased to announce the Biology of Vector-Borne Diseases (BVBD) summer school, 1–5 June 2026, France (sister course of BVBD in Idaho). Apply here bvbd-france.workshop.inrae.fr to bridge disciplines (biology, ecology, epidemiology, modeling, policy) and work on real-world wicked challenges.
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Samuel Soubeyrand @ssoubeyrand.bsky.social · 17/07/2025
New in Mol. Ecol.: integrating multiple data and methods, we show how seasonal fluctuations in pathogen strain composition, diversity and their climate-influenced fitness play a significant role in shaping severity and variability of bacterial disease outbreaks doi.org/10.1111/mec.... #PlantHealth
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Samuel Soubeyrand @ssoubeyrand.bsky.social · 25/06/2025
Discover the newest release in the Tropolink video series: Uncovering the seasonal routes of a plant-pathogen insect vector: www.youtube.com/playlist?lis... This work made by Margaux Darnis et al. is based on tropolink doi.org/10.1029/2023... / pse.mathnum.inrae.fr/tropolink
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Samuel Soubeyrand @ssoubeyrand.bsky.social · 17/06/2025
Which model should be used? Which explanatory variables should be selected?... Let's use a model ensemble instead of a single model to characterize and predict spatio-temporal disease distributions. Read our ms in @phytopathology.bsky.social with application to virus yellows doi.org/10.1094/PHYT...
mportance of variables. Top left: Matrix of correlations between the weighted meansof standardized importance values computed for each model family. Top right: Percentage of to-tal importance computed for each variable classified in variable types (the horizontal dashed lineshows the threshold we considered for selecting the 14 retained important variables). Bottom:Cumulated percentage of total importance computed for each variable with respect to variabletypes. In the bottom panel, the noticeable information is given by the heights (and colors) ofthe bottom slices in each bar (for each variable type, variables are ordered from bottom to topwith respect to importance value). The total height of the bar for each variable type is largelycorrelated with the number of variables included in it and does not bring important information.
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Samuel Soubeyrand @ssoubeyrand.bsky.social · 11/06/2025
Take a First Look at the paper about "Opportunities and Challenges in Combining Optical Sensing and Epidemiological Modelling" by Alexey Mikaberidze et al., very recently published in @phytopathology.bsky.social: doi.org/10.1094/PHYT...
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Samuel Soubeyrand @ssoubeyrand.bsky.social · 03/06/2025
Watch the introduction to the new tropolink video series: youtu.be/L7K88Cz-Ezc, and tutorials showing how to use the tropolink webapp to compute air-mass trajectories and the connectivity between distant sites they generate forgemia.inra.fr/tropo-group/.... Ref. in GeoHealth: doi.org/10.1029/2023...
youtu.be
Tropolink video series - Introduction
YouTube video by Samuel Soubeyrand
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Reposted by Samuel Soubeyrand
Jean-Pierre Rossi @jean-pierre-rossi.bsky.social · 25/11/2024
Très heureux d'annoncer que notre ouvrage "Crises sanitaires en agriculture" aux éditions Editions Quae, a remporté le Prix Jacques Delage, décerné par le Comité des prix de l'Académie vétérinaire de France. www.quae.com/produit/1749... #Bioinvasions #Biosecurity #Agriculture #Health
quae.com
Crises sanitaires en agriculture - Les espèces invasives sous surveillance - (EAN13 : 9782759234837) | Librairie Quae : des livres au coeur des sciences
Crises sanitaires en agriculture - Les espèces invasives sous surveillance - (EAN13 : 9782759234837)
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Samuel Soubeyrand @ssoubeyrand.bsky.social · 25/11/2024
Two axes of the BEYOND project (beyond.paca.hub.inrae.fr) about epidemiological surveillance presented in French (4:30-19:20): natural language processing for knowledge and alerts, and tropolink webapp (doi.org/10.1029/2023...) for long-distance wind-borne dispersal www.youtube.com/watch?v=PoPG...
youtube.com
Avancées à mi-parcours : Développer des indicateurs précoces de surveillance pour la prophylaxie
YouTube video by PPR Cultiver et Protéger Autrement
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Samuel Soubeyrand @ssoubeyrand.bsky.social · 22/11/2024
Explore our bioRxiv preprint where we investigate the contribution of pathogen genetic diversity, climatic variation and their interaction towards disease dynamics, using high resol. sequencing data and multiple analysis techniques (with StrainRanking as a guest tool!). doi.org/10.1101/2024...
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Samuel Soubeyrand @ssoubeyrand.bsky.social · 05/10/2022
Interested in fast & non-equilibrium dynamics of within-host pathogen populations? Read our paper just published by @FrontiersIn Microbiology:Associated R code to simulate such dynamics coupling viral kinetics and microevolution: doi.org/10.5281/zenodo… doi.org/10.3389/fmicb.…
doi.org
Frontiers | Characterizing viral within-host diversity in fast and non-equilibrium demo-genetic dynamics
High-throughput sequencing has opened the route for a deep assessment of within-host genetic diversity that can be used, e.g., to characterize microbial comm...
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Samuel Soubeyrand @ssoubeyrand.bsky.social · 30/03/2021
Connecting places: inferring spatiotemporal #networks generated by the movements of air masses, with potential applications in #aerobiology - #AtmosphericHighways - #LongDistanceDispersal doi.org/10.3389/fams.2…
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Samuel Soubeyrand @ssoubeyrand.bsky.social · 12/09/2020
Our approach for predicting #COVID19 mortality dynamics using data from abroad and comparing country-level dynamics is now published in @PLoS ONE doi.org/10.1371/journa…
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Samuel Soubeyrand @ssoubeyrand.bsky.social · 05/06/2020
A mechanistic-stat approach yields a factor-7 reduction of the effective reproduction number Re of COVID-19 during lockdown in FR (@FrontMedicine:. The post-lockdown very-mild infection dynamics certainly partly explained by remaining distancing behaviors doi.org/10.3389/fmed.2…
doi.org
Frontiers | Impact of Lockdown on the Epidemic Dynamics of COVID-19 in France
The COVID-19 epidemic was reported in the Hubei province in China in December 2019 and then spread around the world reaching the pandemic stage at the beginn...
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Samuel Soubeyrand @ssoubeyrand.bsky.social · 22/04/2020
Visit the BioSP "data blog" about #COVID19 (posts are in French but several preprints in English are available):It includes inferences about #COVID19 epidemiological parameters and the effect of lockdown, as well as forecasts of mortality dynamics. informatique-mia.inrae.fr/biosp/covid-19
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Samuel Soubeyrand @ssoubeyrand.bsky.social · 16/10/2019
In natura, spatial heterogeneity is more the rule than the exception. We precisely propose a class of log-gaussian Cox processes with high degree of spatial non-stationarity and an accompanying fast estimation approach doi.org/10.1016/j.spas…
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Samuel Soubeyrand @ssoubeyrand.bsky.social · 22/05/2019
A Bayesian inference of the spatiotemporal spread of #Xylella in South Corsica, France,, to look beyond our temporal analysis- @xf_actors doi.org/10.1111/nph.15… doi.org/10.1007/s00285…
doi.org
Dating and localizing an invasion from post-introduction data and a coupled reaction–diffusion–absorption model
Journal of Mathematical Biology - Invasion of new territories by alien organisms is of primary concern for environmental and health agencies and has been a core topic in mathematical modeling, in...
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Samuel Soubeyrand @ssoubeyrand.bsky.social · 07/05/2019
We use #StatisticalLearning to infer epidemiological links from Deep Sequencing Data and test the approach on Ebola, Swine influenza and a plant potyvirus. See our paper in Philosophical Transactions B royalsocietypublishing.org/doi/10.1098/rs…
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Samuel Soubeyrand @ssoubeyrand.bsky.social · 21/01/2019
When machine learning and network analysis are used to understand the main drivers of #Xylella fastidiosa infections, produce risk maps and identify lookouts for the design of future surveillance plans doi.org/10.1094/PHYTO-…
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Samuel Soubeyrand @ssoubeyrand.bsky.social · 17/01/2019
Linking aerial connectivity with genetic compositions of a pathogen: the case of Sclerotinia sclerotiorum frontiersin.org/articles/10.33…
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Samuel Soubeyrand @ssoubeyrand.bsky.social · 07/11/2018
Open positions at @Inra_PACA, BioSP, for an applied statistician and a computer scientist in information system: informatique-mia.inra.fr/biosp/pesv-cdd… informatique-mia.inra.fr/biosp/pesv-cdd…
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Samuel Soubeyrand @ssoubeyrand.bsky.social · 16/08/2018
Offre d'emploi à BioSP (@Inra_France, @Inra_PACA, Avignon) dans le cadre de la création de la plateforme nationale d'épidémiosurveillance en santé végétale: Ingénieur de Recherche en épidémiologie et analyse de l’information / Pilotage d’équipe informatique-mia.inra.fr/biosp/pilote-p…
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Samuel Soubeyrand @ssoubeyrand.bsky.social · 03/05/2018
How to unravel the hidden side(s) of pathogen emergence? Our paper about the emergence of #xylella in Corsica, France, published by @NewPhyt, is available at doi.org/10.1111/nph.15…
doi.org
Inferring pathogen dynamics from temporal count data: the emergence of Xylella fastidiosa in France is probably not recent
Unravelling the ecological structure of emerging plant pathogens persisting in multi-host systems is challenging. In such systems, observations are often heterogeneous with respect to time, space ...
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Samuel Soubeyrand @ssoubeyrand.bsky.social · 16/12/2017
Our simulation tool for assessing biological risk and impact in space and time has been published in Risk Analysis:- The associated R package briskaR is on the cran: cran.r-project.org/web/packages/b… doi.org/10.1111/risa.1…
doi.org
A Spatio‐Temporal Exposure‐Hazard Model for Assessing Biological Risk and Impact
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...
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Samuel Soubeyrand @ssoubeyrand.bsky.social · 16/12/2017
GMCPIC: Testing differences between pathogen compositions with small samples and sparse data. MS in @PhytopathologyJ:- Code embedded in the StrainRanking package: cran.r-project.org/web/packages/S… doi.org/10.1094/PHYTO-…
doi.org
Testing Differences Between Pathogen Compositions with Small Samples and Sparse Data | Phytopathology®
The structure of pathogen populations is an important driver of epidemics affecting crops and natural plant communities. Comparing the composition of two pathogen populations consisting of assemblages of genotypes or phenotypes is a crucial, recurrent question encountered in many studies in plant disease epidemiology. Determining whether there is a significant difference between two sets of proportions is also a generic question for numerous biological fields. When samples are small and data are sparse, it is not straightforward to provide an accurate answer to this simple question because routine statistical tests may not be exactly calibrated. To tackle this issue, we built a computationally intensive testing procedure, the generalized Monte Carlo plug-in test with calibration test, which is implemented in an R package available at https://doi.org/10.5281/zenodo.635791. A simulation study was carried out to assess the performance of the proposed methodology and to make a comparison with standard statistical tests. This study allows us to give advice on how to apply the proposed method, depending on the sample sizes. The proposed methodology was then applied to real datasets and the results of the analyses were discussed from an epidemiological perspective. The applications to real data sets deal with three topics in plant pathology: the reproduction of Magnaporthe oryzae, the spatial structure of Pseudomonas syringae, and the temporal recurrence of Puccinia triticina.
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Samuel Soubeyrand @ssoubeyrand.bsky.social · 31/08/2017
PhD position at @Inra_PACA: Statistics and modeling for dispersal networks - Application to epidemiosurveillance informatique-mia.inra.fr/biosp/node/62
informatique-mia.inra.fr
Bio-Bayes-Book | Biostatistique & Processus Spatiaux
Suite à l'école chercheur BioBayes (organisée en novembre 2011 à La Rochelle et renouvelée en octobre 2013 à Mandelieu) par un collectif de l'Inra, le projet d'un livre associé a émergé sous la coordination d'Eric Parent. Le titre projeté en est :
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Samuel Soubeyrand @ssoubeyrand.bsky.social · 07/06/2017
Our review for characterizing plant virus spread using molecular epidemiology in Annual Review of Phytopathology: doi.org/10.1146/annure…
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Samuel Soubeyrand @ssoubeyrand.bsky.social · 23/05/2017
Special issue in Ann. Zool. Fennicifor Ilkka Hanski: The Legacy of a Multifaceted Ecologist annzool.net/anz/anz541-4.h… annzool.net
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Samuel Soubeyrand @ssoubeyrand.bsky.social · 27/04/2017
T. Mrckvicka and I proposed a MCMC algorithm for estimating doubly inhomogeneous cluster point processes ... doi.org/10.1016/j.spas…
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Samuel Soubeyrand @ssoubeyrand.bsky.social · 01/12/2016
PhD position in Statistics for Molecular Epidemiology available at BioSP, INRA. Request offer in English if needed. informatique-mia.inra.fr/biosp/sites/in…
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Samuel Soubeyrand @ssoubeyrand.bsky.social · 19/09/2016
What future for the bees? Abeilles Road, the trailer: In Fr:- In En: youtube.com/watch?v=2Jm86U… youtube.com/watch?v=41WvRm…
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Abeilles Road - trailer
Depuis neuf ans l'Observatoire des lavandes en collaboration avec l'INRA d'Avignon et l'ADAPI suit des centaines de ruches pour déterminer les paramètres qui peuvent influer sur la production de miel et la santé des abeilles...
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Samuel Soubeyrand @ssoubeyrand.bsky.social · 31/08/2016
Antti Penttinen & Gavin Gibson will present their recent works at @Inra_PACA, Avignon, Sept.13, 10h-11h30, Salle Garance. Come and join us.
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Samuel Soubeyrand @ssoubeyrand.bsky.social · 28/07/2016
My HDR defense (@univamu) will take place at @Inra_PACA Avignon on Sept12. Title: Contributions to Statistical Plant and Animal Epidemiology
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Samuel Soubeyrand @ssoubeyrand.bsky.social · 27/07/2016
Nicolas Parisey et al. rearrange agricultural landscapes in silico for regulating pests dx.doi.org/10.1016/j.ecoc…
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Samuel Soubeyrand @ssoubeyrand.bsky.social · 19/05/2016
Our review paper about global envelope tests and competitors used in spatial statistics is available at dx.doi.org/10.1016/j.spas…
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Samuel Soubeyrand @ssoubeyrand.bsky.social · 13/04/2016
A statistical presentation of genetic-space-time SEIR models used for inferring transmissions of infectious diseases journal-sfds.fr/index.php/J-SF…
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Samuel Soubeyrand @ssoubeyrand.bsky.social · 12/03/2016
New method (and R package) for jointly estimating sex rate and population size of partially clonal organisms: dx.doi.org/10.1111/1755-0…
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Samuel Soubeyrand @ssoubeyrand.bsky.social · 16/11/2015
Spatial limits & fragmentation shape freq. of indep., clump and group dispersal strategies dx.doi.org/10.1007/s10682…
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Samuel Soubeyrand @ssoubeyrand.bsky.social · 16/10/2015
Internship for a master student: when: 4-6 months in 2016; where: Avignon; topic: statistical epidemiology; details: informatique-mia.inra.fr/biosp/sites/ci…
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Samuel Soubeyrand @ssoubeyrand.bsky.social · 28/08/2015
My Tweet from 2015-08-28 in other words: x.com/ABC_Research/s…
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Samuel Soubeyrand @ssoubeyrand.bsky.social · 28/08/2015
See our BvM-like theorem showing asymptotic normality of ABC-posteriors conditional on MPLE: dx.doi.org/10.1016/j.spl.…
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Samuel Soubeyrand @ssoubeyrand.bsky.social · 11/08/2015
A need for a review paper about sharka? Loup Rimbaud and colleagues made it: dx.doi.org/10.1146/annure…
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Samuel Soubeyrand @ssoubeyrand.bsky.social · 26/06/2015
New introductory textbook about Bayesian stats that follows 2 @Inra_France training workshops editions-ellipses.fr/product_info.p…
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Samuel Soubeyrand @ssoubeyrand.bsky.social · 29/05/2015
Our US rainfall-feedback mapsleading to open questions, e.g. land use influence on #drought? w3.avignon.inra.fr/rainfallfeedba…
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Samuel Soubeyrand @ssoubeyrand.bsky.social · 31/03/2015
Persistent after-effects of heavy rain on concentrations of ice nuclei and rainfall suggest a biological cause atmos-chem-phys.net/15/2313/2015/a…
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Persistent after-effects of heavy rain on concentrations of ice nuclei and rainfall suggest a biological cause
Abstract. Rainfall is one of the most important aspects of climate, but the extent to which atmospheric ice nuclei (IN) influence its formation, quantity, frequency, and location is not clear. Microorganisms and other biological particles are released following rainfall and have been shown to serve as efficient IN, in turn impacting cloud and precipitation formation. Here we investigated potential long-term effects of IN on rainfall frequency and quantity. Differences in IN concentrations and rainfall after and before days of large rainfall accumulation (i.e., key days) were calculated for measurements made over the past century in southeastern and southwestern Australia. Cumulative differences in IN concentrations and daily rainfall quantity and frequency as a function of days from a key day demonstrated statistically significant increasing logarithmic trends (R2 > 0.97). Based on observations that cumulative effects of rainfall persisted for about 20 days, we calculated cumulative differences for the entire sequence of key days at each site to create a historical record of how the differences changed with time. Comparison of pre-1960 and post-1960 sequences most commonly showed smaller rainfall totals in the post-1960 sequences, particularly in regions downwind from coal-fired power stations. This led us to explore the hypothesis that the increased leaf surface populations of IN-active bacteria due to rain led to a sustained but slowly diminishing increase in atmospheric concentrations of IN that could potentially initiate or augment rainfall. This hypothesis is supported by previous research showing that leaf surface populations of the ice-nucleating bacterium Pseudomonas syringae increased by orders of magnitude after heavy rain and that microorganisms become airborne during and after rain in a forest ecosystem. At the sites studied in this work, aerosols that could have initiated rain from sources unrelated to previous rainfall events (such as power stations) would automatically have reduced the influences on rainfall of those whose concentrations were related to previous rain, thereby leading to inhibition of feedback. The analytical methods described here provide means to map and delimit regions where rainfall feedback mediated by microorganisms is suspected to occur or has occurred historically, thereby providing rational means to establish experimental set-ups for verification.
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Samuel Soubeyrand @ssoubeyrand.bsky.social · 06/02/2015
A convolution b/n a lognormal random field and a dispersal kernel to estimate inter-field dispersal of plant fungi doi.org/10.1111/aab.12…
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Transmission of Leptosphaeria maculans from a cropping season to the following one
Current modelling of inoculum transmission from a cropping season to the following one relies on the extrapolation of kernels estimated on data at short distances from punctual sources, because data ...
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Samuel Soubeyrand @ssoubeyrand.bsky.social · 18/08/2014
Lionel Roques's article in Proceedings A dealing with parameter estimation for energy balance models with memory: dx.doi.org/10.1098/rspa.2…
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Parameter estimation for energy balance models with memory | Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences
We study parameter estimation for one-dimensional energy balance models with memory (EBMMs) given localized and noisy temperature measurements. Our results apply to a wide range of nonlinear, parabolic partial differential equations with integral memory ...
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Samuel Soubeyrand @ssoubeyrand.bsky.social · 18/08/2014
Starting a new set of works with @bio_ice_nuclei and K Bigg about rainfall feedback using historical Australian data dx.doi.org/10.1016/j.envs…
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Samuel Soubeyrand @ssoubeyrand.bsky.social · 24/03/2014
Our tutorial to fit reaction-diffusion models to biological invasions data: doi.org/10.1007/s10144…
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Samuel Soubeyrand @ssoubeyrand.bsky.social · 12/03/2014
Our new article about transmission tree reconstruction based on space-time-genetic data published by @RSocPublishing: doi.org/10.1098/rspb.2…
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A Bayesian approach for inferring the dynamics of partially observed endemic infectious diseases from space-time-genetic data | Proceedings of the Royal Society B: Biological Sciences
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. Previous approaches to reconstructing transmission trees ...
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Samuel Soubeyrand @ssoubeyrand.bsky.social · 19/02/2014
Example of dispersal pattern obtained with the model for group dispersal in a fragmented habitat
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