no code implementations • WASSA (ACL) 2022 • Joshua Minot, Milo Trujillo, Samuel Rosenblatt, Guillermo De Anda-Jáuregui, Emily Moog, Allison M. Roth, Briane Paul Samson, Laurent Hébert-Dufresne
Inferring group membership of social media users is of high interest in many domains.
1 code implementation • 30 Apr 2024 • Nicholas W. Landry, William Thompson, Laurent Hébert-Dufresne, Jean-Gabriel Young
Network scientists often use complex dynamic processes to describe network contagions, but tools for fitting contagion models typically assume simple dynamics.
no code implementations • 7 Feb 2023 • Mariah C. Boudreau, Andrea J. Allen, Nicholas J. Roberts, Antoine Allard, Laurent Hébert-Dufresne
Forecasting disease spread is a critical tool to help public health officials design and plan public health interventions. However, the expected future state of an epidemic is not necessarily well defined as disease spread is inherently stochastic, contact patterns within a population are heterogeneous, and behaviors change.
1 code implementation • 20 Jan 2023 • B. K. M. Case, Jean-Gabriel Young, Laurent Hébert-Dufresne
Recent outbreaks of monkeypox and Ebola, and worrying waves of COVID-19, influenza and respiratory syncytial virus, have all led to a sharp increase in the use of epidemiological models to estimate key epidemiological parameters.
no code implementations • 16 Nov 2021 • Laurent Hébert-Dufresne, Jean-Gabriel Young, Jamie Bedson, Laura A. Skrip, Danielle Pedi, Mohamed F. Jalloh, Bastian Raulier, Olivier Lapointe-Gagné, Amara Jambai, Antoine Allard, Benjamin M. Althouse
We leverage the data collected by the surveillance and contact tracing protocols of the Sierra Leone Ministry of Health and Sanitation, the US Centers for Disease Control and Prevention, and other responding partners to validate a network epidemiology framework connecting the population (incidence), community (local forecasts), and individual (secondary infections) scales of disease transmission.
no code implementations • 25 Sep 2021 • Blake J. M. Williams, C. Brandon Ogbunugafor, Benjamin M. Althouse, Laurent Hébert-Dufresne
We argue that: (i) genotype networks are driven by mutation and host immunity to explore a subspace of networks predictable in structure, and (ii) genotype networks provide an underlying structure necessary to capture the rich dynamics of multistrain epidemic models.
no code implementations • 7 Jul 2021 • Andrea J. Allen, Mariah C. Boudreau, Nicholas J. Roberts, Antoine Allard, Laurent Hébert-Dufresne
We show how the challenge of inferring the early course of an epidemic falls on the randomness of disease spread more so than on the heterogeneity of contact patterns.
1 code implementation • 18 Jan 2021 • Guillaume St-Onge, Hanlin Sun, Antoine Allard, Laurent Hébert-Dufresne, Ginestra Bianconi
The colocation of individuals in different environments is an important prerequisite for exposure to infectious diseases on a social network.
Physics and Society Adaptation and Self-Organizing Systems
no code implementations • 26 Oct 2020 • Dick Carrillo, Lam Duc Nguyen, Pedro H. J. Nardelli, Evangelos Pournaras, Plinio Morita, Demóstenes Z. Rodríguez, Merim Dzaferagic, Harun Siljak, Alexander Jung, Laurent Hébert-Dufresne, Irene Macaluso, Mehar Ullah, Gustavo Fraidenraich, Petar Popovski
In this sense, we expect active participation of empowered citizens to supplement the more usual top-down management of epidemics.
Distributed, Parallel, and Cluster Computing
no code implementations • 16 Sep 2020 • Joshua Garland, Keyan Ghazi-Zahedi, Jean-Gabriel Young, Laurent Hébert-Dufresne, Mirta Galesic
Citizen-generated counter speech is a promising way to fight hate speech and promote peaceful, non-polarized discourse.
2 code implementations • 6 Aug 2020 • Harrison Hartle, Brennan Klein, Stefan McCabe, Alexander Daniels, Guillaume St-Onge, Charles Murphy, Laurent Hébert-Dufresne
Quantifying the differences between networks is a challenging and ever-present problem in network science.
Physics and Society Social and Information Networks
no code implementations • 15 Jul 2020 • Blake J. M. Williams, Guillaume St-Onge, Laurent Hébert-Dufresne
Mathematical disease modelling has long operated under the assumption that any one infectious disease is caused by one transmissible pathogen spreading among a population.
no code implementations • EMNLP (ALW) 2020 • Joshua Garland, Keyan Ghazi-Zahedi, Jean-Gabriel Young, Laurent Hébert-Dufresne, Mirta Galesic
Hateful rhetoric is plaguing online discourse, fostering extreme societal movements and possibly giving rise to real-world violence.
1 code implementation • 27 May 2020 • Benjamin M. Althouse, Edward A. Wenger, Joel C. Miller, Samuel V. Scarpino, Antoine Allard, Laurent Hébert-Dufresne, Hao Hu
SARS-CoV-2 causing COVID-19 disease has moved rapidly around the globe, infecting millions and killing hundreds of thousands.
no code implementations • 12 Mar 2020 • Guillaume St-Onge, Vincent Thibeault, Antoine Allard, Louis J. Dubé, Laurent Hébert-Dufresne
Recommendations around epidemics tend to focus on individual behaviors, with much less efforts attempting to guide event cancellations and other collective behaviors since most models lack the higher-order structure necessary to describe large gatherings.
Physics and Society Adaptation and Self-Organizing Systems
2 code implementations • 10 Feb 2020 • Laurent Hébert-Dufresne, Benjamin M. Althouse, Samuel V. Scarpino, Antoine Allard
Lastly, we demonstrate that without data on the heterogeneity in secondary infections for emerging infectious diseases like COVID-19, the uncertainty in outbreak size ranges dramatically.
Populations and Evolution Applied Physics Physics and Society
1 code implementation • 25 Jun 2019 • Antoine Allard, Laurent Hébert-Dufresne
In fact we find that the closer a non-tree network is to a tree, the worse the MPA accuracy becomes.
Physics and Society Statistical Mechanics
1 code implementation • 4 Jun 2019 • Laurent Hébert-Dufresne, Samuel V. Scarpino, Jean-Gabriel Young
From fake news to innovative technologies, many contagions spread via a process of social reinforcement, where multiple exposures are distinct from prolonged exposure to a single source.
Physics and Society Dynamical Systems Populations and Evolution
2 code implementations • 1 Oct 2018 • Laurent Hébert-Dufresne, Antoine Allard
Our results shed light not only on the nature of the percolation transition in complex systems, but also provide two important insights on the numerical and analytical tools we use to study them.
Physics and Society Disordered Systems and Neural Networks
3 code implementations • 15 Aug 2018 • Guillaume St-Onge, Jean-Gabriel Young, Laurent Hébert-Dufresne, Louis J. Dubé
Efficient stochastic simulation algorithms are of paramount importance to the study of spreading phenomena on complex networks.
Physics and Society Social and Information Networks
1 code implementation • 25 Apr 2018 • Antoine Allard, Laurent Hébert-Dufresne
Analytical approaches to model the structure of complex networks can be distinguished into two groups according to whether they consider an intensive (e. g., fixed degree sequence and random otherwise) or an extensive (e. g., adjacency matrix) description of the network structure.
Physics and Society Statistical Mechanics
1 code implementation • 25 Mar 2018 • Jean-Gabriel Young, Guillaume St-Onge, Edward Laurence, Charles Murphy, Laurent Hébert-Dufresne, Patrick Desrosiers
Network growth processes can be understood as generative models of the structure and history of complex networks.
1 code implementation • 31 Dec 2016 • Jean-Gabriel Young, Patrick Desrosiers, Laurent Hébert-Dufresne, Edward Laurence, Louis J. Dubé
We then distinguish the concept of average detectability from the concept of instance-by-instance detectability and give explicit formulas for both definitions.
Physics and Society Information Theory Information Theory
1 code implementation • 7 Apr 2016 • Andrew Berdahl, Christa Brelsford, Caterina De Bacco, Marion Dumas, Vanessa Ferdinand, Joshua A. Grochow, Laurent Hébert-Dufresne, Yoav Kallus, Christopher P. Kempes, Artemy Kolchinsky, Daniel B. Larremore, Eric Libby, Eleanor A. Power, Caitlin A. Stern, Brendan Tracey
Third, in the context of dynamic social networks, we find that preferences for increased global infection accelerate spread and produce superexponential fixation, but preferences for local assortativity halt epidemics by disconnecting the infected from the susceptible.
Physics and Society Multiagent Systems Social and Information Networks Adaptation and Self-Organizing Systems Populations and Evolution
1 code implementation • 29 Oct 2015 • Laurent Hébert-Dufresne, Joshua A. Grochow, Antoine Allard
The onion spectrum is exactly as easy to compute as the k-cores: It is based on the stages at which each vertex gets removed from a graph in the standard algorithm for computing the k-cores.
Physics and Society Disordered Systems and Neural Networks Discrete Mathematics Social and Information Networks Combinatorics