Biology subjects

Jombart, T.

Publications and source records attributed to Jombart, T..

5 recordsLinked to original sources

bioRxiv2026

geoGraph: Connectivity modelling across geographic space with hexagonal discrete global grids

1. Estimating the movement and connectivity of organisms across landscapes is central to research in ecology and evolutionary biology. Although graph-based approaches representing geographic space as weighted networks provide a flexible and computationally efficient framework for such analyses, existing tools either perform their computations only on rectangular raster grids, thus introducing systematic directional and area distortions at global scales, or rely on bespoke pipelines that limit the general applicability of recent hexagonal-grid analyses. 2. To overcome these shortcomings, here we present geoGraph, an R package that brings graph-based spatial modelling to discrete global hexagonal grids. By representing landscapes on an equal-area hexagonal tessellation of the sphere, geoGraph enables global-scale analysis without projection distortion while retaining the flexibility of the graph framework and the performance of the graph and RBGL/Boost libraries. The package integrates both raster- and vector-based GIS data into a single cost surface, supports custom grids at various resolutions, and provides a complete pipeline from raw GIS layers to graph-algorithmic outputs, together with tools for visualising and interactively editing graphs. 3. We illustrate geoGraph by revisiting previous studies of isolation by distance among human populations in the Human Genome Diversity Project panel. At a global scale, restricting movement to land raised the correlation between geographic and genetic distance from r = 0.74 (great-circle) to r = 0.84. On a finer grid within Asia, accounting for terrain ruggedness improved the fit, with the best-fitting models implying reduced movement across the Himalaya and Tibetan Plateau. These analyses demonstrate that geoGraph can model movement and connectivity appropriate to the desired spatial scale and that the level of geographic realism required is itself scale-dependent. 4. By combining discrete global grids with the power of graph algorithms, geoGraph enables seamless connectivity analysis from landscape to global scales. It is broadly applicable across landscape genetics, phylogeography, conservation planning, and studies of human dispersal, and is particularly suited to large-scale problems that rectangular-raster tools cannot address without distortion.

bioinformatics↗

bioRxiv2026

To cull or not to cull: a model-based evaluation of response strategies against Lumpy Skin Disease outbreaks

With high risk of sustained transmission and substantial associated morbidity, Lumpy Skin Disease (LSD) of cattle presents a major emerging threat to bovine herds worldwide, and has the potential to cause huge losses for farmers, producers, and the wider bovine and dairy trade industries. The epidemic which started in June 2025 in France illustrates this threat, as the government-imposed mass culling strategy has led to protests by farmers and social unrest throughout the country. Here, we developed an infectious disease modelling framework for evaluating different intervention strategies in an effort to critically evaluate the evidence behind the drastic measures being taken. By simulating epidemics under different response settings, we found that control strategies employing targeted culling were essentially as effective as those requiring mass culling, and did not require significantly more vaccination. These results question the response strategy currently imposed by the French government, and suggest avenues for alternative response strategies which could gain better support from the affected communities. Our results are entirely reproducible using a new free, open-source simulation tool which can be used for further evaluation of public health interventions against LSD epidemics.

ecology↗

bioRxiv2021

Measuring the unknown: an estimator and simulation study for assessing case reporting during epidemics

The fraction of cases reported, known as reporting, is a key performance indicator in an outbreak response, and an essential factor to consider when modelling epidemics and assessing their impact on populations. Unfortunately, its estimation is inherently difficult, as it relates to the part of an epidemic which is, by definition, not observed. We introduce a simple statistical method for estimating reporting, initially developed for the response to Ebola in Eastern Democratic Republic of the Congo (DRC), 2018-2020. This approach uses transmission chain data typically gathered through case investigation and contact tracing, and uses the proportion of investigated cases with a known, reported infector as a proxy for reporting. Using simulated epidemics, we study how this method performs for different outbreak sizes and reporting levels. Results suggest that our method has low bias, reasonable precision, and despite sub-optimal coverage, usually provides estimates within close range (5-10%) of the true value. Being fast and simple, this method could be useful for estimating reporting in real-time in settings where person-to-person transmission is the main driver of the epidemic, and where case investigation is routinely performed as part of surveillance and contact tracing activities. Author summaryWhen responding to epidemics of infectious diseases, it is essential to estimate how many cases are not being reported. Unfortunately reporting, the proportion of cases actually observed, is difficult to estimate during an outbreak, as it typically requires large surveys to be conducted on the affected populations. Here, we introduce a method for estimating reporting from case investigation data, using the proportion of cases with a known, reported infector. We used simulations to test the performance of our approach by mimicking features of a recent Ebola epidemic in the Democratic Republic of the Congo. We found that despite some uncertainty in smaller outbreaks, our approach can be used to obtain informative ballpark estimates of reporting under most settings. This method is simple and computationally inexpensive, and can be used to inform the response to any epidemic in which transmission events can be uncovered by case investigation.

bioinformatics↗

bioRxiv2019

A systematic review of MERS-CoV (Middle East Respiratory Syndrome Coronavirus) seroprevalence and viral RNA prevalence in dromedary camels: implications for animal vaccination

Human infection with Middle East Respiratory Syndrome Coronavirus (MERS-CoV) is driven by recurring dromedary-to-human spill-over events, leading decision-makers to consider dromedary vaccination. Dromedary vaccine candidates in the development pipeline are showing hopeful results, but gaps in our understanding of the epidemiology of MERS-CoV in dromedaries must be addressed to design and evaluate potential vaccination strategies. We systematically reviewed the published literature reporting seroprevalence and/or prevalence of active MERS-CoV infection in dromedary populations from both cross-sectional and longitudinal studies, including 60 studies in our qualitative syntheses. MERS-CoV seroprevalence increased with age up to 80-100% in adult dromedaries supporting geographically wide spread endemicity of MERS-CoV in dromedaries in both the Arabian Peninsula and countries exporting dromedaries from Africa. The high prevalence of active infection measured in juveniles and at sites where dromedary populations mix should guide further investigation - particularly of dromedary movement - and inform vaccination strategy design.

epidemiology↗

bioRxiv2017

An Eigenvalue Test for spatial Principal Component Analysis

BackgroundThe spatial Principal Component Analysis (sPCA, Jombart 2008) is designed to investigate non-random spatial distributions of genetic variation. Unfortunately, the associated tests used for assessing the existence of spatial patterns (global and local test; Jombart et al. 2008) lack statistical power and may fail to reveal existing spatial patterns. Here, we present a non-parametric test for the significance of specific patterns recovered by sPCA.\n\nResultsWe compared the performance of this new test to the original global and local tests using datasets simulated under classical population genetic models. Results show that our test outperforms the original global and local tests, exhibiting improved statistical power while retaining similar, and reliable type I errors. Moreover, by allowing to test various sets of axes, it can be used to guide the selection of retained sPCA components.\n\nConclusionsAs such, our test represents a valuable complement to the original analysis, and should prove useful for the investigation of spatial genetic patterns.

evolutionary biology↗