Samuel Soubeyrand
@ssoubeyrand.bsky.social
72 followers 59 following 52 posts
Researcher at INRAE - Model construction, statistical inference, and their application to epidemiology, ecology... BioSP - INRAE - Avignon, France https://samuel.biosp.org
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ssoubeyrand.bsky.social
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
ssoubeyrand.bsky.social
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
ssoubeyrand.bsky.social
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.
ssoubeyrand.bsky.social
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...
ssoubeyrand.bsky.social
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...
Tropolink video series - Introduction
YouTube video by Samuel Soubeyrand
youtu.be
Reposted by Samuel Soubeyrand
jean-pierre-rossi.bsky.social
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
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)
www.quae.com
ssoubeyrand.bsky.social
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...
Avancées à mi-parcours : Développer des indicateurs précoces de surveillance pour la prophylaxie
YouTube video by PPR Cultiver et Protéger Autrement
www.youtube.com
ssoubeyrand.bsky.social
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...
ssoubeyrand.bsky.social
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…
ssoubeyrand.bsky.social
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…
ssoubeyrand.bsky.social
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…
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...
doi.org
ssoubeyrand.bsky.social
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
ssoubeyrand.bsky.social
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…
ssoubeyrand.bsky.social
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…
ssoubeyrand.bsky.social
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-…
ssoubeyrand.bsky.social
Linking aerial connectivity with genetic compositions of a pathogen: the case of Sclerotinia sclerotiorum

frontiersin.org/articles/10.33…
ssoubeyrand.bsky.social
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…
ssoubeyrand.bsky.social
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…
ssoubeyrand.bsky.social
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-…
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.
doi.org
ssoubeyrand.bsky.social
Our review for characterizing plant virus spread using molecular epidemiology in Annual Review of Phytopathology:

doi.org/10.1146/annure…