ORCID Profile
0000-0002-8208-9920
Current Organisation
Université Laval
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Publisher: Oxford University Press (OUP)
Date: 18-04-2023
DOI: 10.1093/PNASNEXUS/PGAD136
Abstract: Over the last decade, random hyperbolic graphs have proved successful in providing geometric explanations for many key properties of real-world networks, including strong clustering, high navigability, and heterogeneous degree distributions. These properties are ubiquitous in systems as varied as the internet, transportation, brain or epidemic networks, which are thus unified under the hyperbolic network interpretation on a surface of constant negative curvature. Although a few studies have shown that hyperbolic models can generate community structures, another salient feature observed in real networks, we argue that the current models are overlooking the choice of the latent space dimensionality that is required to adequately represent clustered networked data. We show that there is an important qualitative difference between the lowest-dimensional model and its higher-dimensional counterparts with respect to how similarity between nodes restricts connection probabilities. Since more dimensions also increase the number of nearest neighbors for angular clusters representing communities, considering only one more dimension allows us to generate more realistic and erse community structures.
Publisher: Public Library of Science (PLoS)
Date: 12-11-2020
DOI: 10.1371/JOURNAL.PBIO.3000897
Abstract: Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the etiological agent of the Coronavirus Disease 2019 (COVID-19) disease, has moved rapidly around the globe, infecting millions and killing hundreds of thousands. The basic reproduction number, which has been widely used—appropriately and less appropriately—to characterize the transmissibility of the virus, hides the fact that transmission is stochastic, often dominated by a small number of in iduals, and heavily influenced by superspreading events (SSEs). The distinct transmission features of SARS-CoV-2, e.g., high stochasticity under low prevalence (as compared to other pathogens, such as influenza), and the central role played by SSEs on transmission dynamics cannot be overlooked. Many explosive SSEs have occurred in indoor settings, stoking the pandemic and shaping its spread, such as long-term care facilities, prisons, meat-packing plants, produce processing facilities, fish factories, cruise ships, family gatherings, parties, and nightclubs. These SSEs demonstrate the urgent need to understand routes of transmission, while posing an opportunity to effectively contain outbreaks with targeted interventions to eliminate SSEs. Here, we describe the different types of SSEs, how they influence transmission, empirical evidence for their role in the COVID-19 pandemic, and give recommendations for control of SARS-CoV-2.
Publisher: Cold Spring Harbor Laboratory
Date: 02-2022
DOI: 10.1101/2022.01.31.477297
Abstract: Despite an increasing number of studies highlighting the impacts of climate change on boreal species, the main factors that will drive changes in species assemblages remain ambiguous. We quantify two climate-induced pathways based on direct and indirect effects on species occupancy and assemblage dis-similarity under different harvest management scenarios. The direct climate effects illustrate the impact of climate variables while the indirect effects are reflected through the changes in land cover composition. To understand the main causes in assemblage dissimilarity, we analyze the regional and the latitudinal species assemblage dissimilarity by decomposing it into balanced variation in species occupancy and occurrence and occupancy and occurrence gradient . We develop empirical models to predict the distribution of more than 100 bird and beetle species in the Côte-Nord region of Québec over the next century. Our results show that the two pathways are complementary and alter bio ersity, mainly caused by balanced variation in species occupancy and occurrence. At the regional scale, both effects have an impact on decreasing the number of winning species. Yet, responses are much larger in magnitude under mixed climate effects (a mixture of direct and indirect effects). Regional assemblage dissimilarity reached 0.77 and 0.69 under mixed effects versus 0.09 and 0.10 under indirect effects for beetles and birds, respectively, between RCP8.5 and baseline climate scenarios when considering harvesting. Therefore, inclusion of climatic variables considers aspects other than just those related to forest landscapes, such as life cycles of animal species. Latitudinally, assemblage dissimilarity increased following the climate conditions pattern. Our analysis contributes to the understanding of how climate change alters bio ersity by reshaping community composition and highlights the importance of climate variables in bio ersity prediction.
Publisher: Oxford University Press (OUP)
Date: 05-2023
DOI: 10.1093/PNASNEXUS/PGAD150
Abstract: Dimension reduction is a common strategy to study nonlinear dynamical systems composed by a large number of variables. The goal is to find a smaller version of the system whose time evolution is easier to predict while preserving some of the key dynamical features of the original system. Finding such a reduced representation for complex systems is, however, a difficult task. We address this problem for dynamics on weighted directed networks, with special emphasis on modular and heterogeneous networks. We propose a two-step dimension-reduction method that takes into account the properties of the adjacency matrix. First, units are partitioned into groups of similar connectivity profiles. Each group is associated to an observable that is a weighted average of the nodes’ activities within the group. Second, we derive a set of equations that must be fulfilled for these observables to properly represent the original system’s behavior, together with a method for approximately solving them. The result is a reduced adjacency matrix and an approximate system of ODEs for the observables’ evolution. We show that the reduced system can be used to predict some characteristic features of the complete dynamics for different types of connectivity structures, both synthetic and derived from real data, including neuronal, ecological, and social networks. Our formalism opens a way to a systematic comparison of the effect of various structural properties on the overall network dynamics. It can thus help to identify the main structural driving forces guiding the evolution of dynamical processes on networks.
No related grants have been discovered for Antoine Allard.