arXiv subjects

Charley Presigny

Publications and source records attributed to Charley Presigny.

4 recordsLinked to original sources

arXiv2026

Climate change and human mobility will shape dengue emergence risk in Europe

The risk of local arbovirus outbreaks in Europe is expected to increase due to climate change, as suggested by the multiplication of arbovirus outbreaks in the last decades. Europe has historically been a non-endemic region, making it vital to pinpoint which populations are potentially exposed -and under which conditions- so we can build truly robust epidemic preparedness capabilities. We introduce an integrated, multi-scale model that fuses a mechanistic transmission engine with a vector abundance framework, all embedded in a mobility-driven metapopulation system capturing human, vector, and air-traffic movement. To this end, we combine climate and population projections with mobility data to estimate and map dengue emergence risk in Europe throughout the 21st century. Additionally, we introduce a dedicated migration model that explores how climate-driven population redistribution could alter these risk estimates.Assuming the climate avoids major tipping points, model-derived risk indicators increase substantially under most emissions scenarios. While the spatio-temporal risk will remain largely driven by importation, our results indicate a gradual transition toward an environment-driven regime, particularly under the worst-case emissions scenario. To better anticipate and manage recurrent arbovirus outbreaks, our findings highlight the need to integrate mobility pathways and climate-driven population redistribution into predictive models of vector-borne disease emergence in temperate regions.

physics.soc-ph↗

arXiv2024

Node-layer duality in networked systems

Real-world networks typically exhibit several aspects, or layers, of interactions among their nodes. By permuting the role of the nodes and the layers, we establish a new criterion to construct the dual of a network. This approach allows to examine information from either a node-centric or layer-centric viewpoint. Through rigorous analytical methods and extensive simulations, we demonstrate that nodewise and layerwise connectivity measure different but related aspects of the same system. Leveraging node-layer duality provides complementary insights, enabling a deeper comprehension of diverse networks across social science, technology and biology. Taken together, these findings reveal previously unappreciated features of complex systems and provide a fresh tool for delving into their structure and dynamics.

physics.soc-ph↗

arXiv2022

Multiscale modeling of brain network organization

A complete understanding of the brain requires an integrated description of the numerous scales of neural organization. It means studying the interplay of genes, synapses, and even whole brain regions which ultimately leads to different types of behavior, from perception to action, while asleep or awake. Yet, multiscale brain modeling is challenging, in part because of the difficulty to access simultaneously to information from multiple spatiotemporal scales. While some insights have been gained on the role of specific microcircuits (e.g., thalamocortical), a comprehensive characterization of how changes occurring at one scale can have an impact on other ones, remains poorly understood. Recent efforts to address this gap include the development of new analytical tools mostly adapted from network science and dynamical systems theory. These theoretical contributions provide a powerful framework to analyze and model interconnected complex systems exhibiting interactions within and between different scales, or layers. Here, we present recent advances for the characterization of the multiscale brain organization in terms of structure-function, oscillation frequencies and temporal evolution. Efforts are reviewed on the multilayer network properties underlying higher-order organization of neuronal assemblies, as well as on the identification of multimodal network-based biomarkers of brain pathologies, such as Alzheimer's disease. We conclude this Colloquium with a perspective discussion on how recent results from multilayer network theory, involving generative modeling, controllability and machine learning, could be adopted to address new questions in modern neuroscience.

q-bio.NC↗

arXiv2020

Building surrogate temporal network data from observed backbones

In many data sets, crucial elements co-exist with non-essential ones and noise. For data represented as networks in particular, several methods have been proposed to extract a "network backbone", i.e., the set of most important links. However, the question of how the resulting compressed views of the data can effectively be used has not been tackled. Here we address this issue by putting forward and exploring several systematic procedures to build surrogate data from various kinds of temporal network backbones. In particular, we explore how much information about the original data need to be retained alongside the backbone so that the surrogate data can be used in data-driven numerical simulations of spreading processes. We illustrate our results using empirical temporal networks with a broad variety of structures and properties.

physics.soc-ph↗