@article{pmid39939526,

title = {Pandemic monitoring with global aircraft-based wastewater surveillance networks},

author = {Guillaume St-Onge and Jessica T Davis and Laurent Hébert-Dufresne and Antoine Allard and Alessandra Urbinati and Samuel V Scarpino and Matteo Chinazzi and Alessandro Vespignani},

doi = {10.1038/s41591-025-03501-4},

issn = {1546-170X},

year  = {2025},

date = {2025-03-01},

journal = {Nat Med},

volume = {31},

number = {3},

pages = {788--796},

abstract = {Aircraft wastewater surveillance has been proposed as a new approach to monitor the global spread of pathogens. Here we develop a computational framework providing actionable information for the design and estimation of the effectiveness of global aircraft-based wastewater surveillance networks (WWSNs). We study respiratory diseases of varying transmission potential and find that networks of 10-20 strategically placed wastewater sentinel sites can provide timely situational awareness and function effectively as an early warning system. The model identifies potential blind spots and suggests optimization strategies to increase WWSN effectiveness while minimizing resource use. Our findings indicate that increasing the number of sentinel sites beyond a critical threshold does not proportionately improve WWSN capabilities, emphasizing the importance of resource optimization. We show, through retrospective analyses, that WWSNs can notably shorten detection time for emerging pathogens. The approach presented offers a realistic analytic framework for the analysis of WWSNs at airports.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid40161997,

title = {Firing rate distributions in plastic networks of spiking neurons},

author = {Marina Vegué and Antoine Allard and Patrick Desrosiers},

doi = {10.1162/netn_a_00442},

issn = {2472-1751},

year  = {2025},

date = {2025-01-01},

journal = {Netw Neurosci},

volume = {9},

number = {1},

pages = {447--474},

abstract = {In recurrent networks of leaky integrate-and-fire neurons, the mean-field theory has been instrumental in capturing the statistical properties of neuronal activity, like firing rate distributions. This theory has been applied to networks with either homogeneous synaptic weights and heterogeneous connections per neuron or vice versa. Our work expands mean-field models to include networks with both types of structural heterogeneity simultaneously, particularly focusing on those with synapses that undergo plastic changes. The model introduces a spike trace for each neuron, a variable that rises with neuron spikes and decays without activity, influenced by a degradation rate   and the neuron's firing rate . When the ratio  = /  is significantly high, this trace effectively estimates the neuron's firing rate, allowing synaptic weights at equilibrium to be determined by the firing rates of connected neurons. This relationship is incorporated into our mean-field formalism, providing exact solutions for firing rate and synaptic weight distributions at equilibrium in the high  regime. However, the model remains accurate within a practical range of degradation rates, as demonstrated through simulations with networks of excitatory and inhibitory neurons. This approach sheds light on how plasticity modulates both activity and structure within neuronal networks, offering insights into their complex behavior.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid39425431,

title = {Network compression with configuration models and the minimum description length},

author = {Laurent Hébert-Dufresne and Jean-Gabriel Young and Alexander Daniels and Alec Kirkley and Antoine Allard},

doi = {10.1103/PhysRevE.110.034305},

issn = {2470-0053},

year  = {2024},

date = {2024-09-01},

journal = {Phys Rev E},

volume = {110},

number = {3-1},

pages = {034305},

abstract = {Random network models, constrained to reproduce specific statistical features, are often used to represent and analyze network data and their mathematical descriptions. Chief among them, the configuration model constrains random networks by their degree distribution and is foundational to many areas of network science. However, configuration models and their variants are often selected based on intuition or mathematical and computational simplicity rather than on statistical evidence. To evaluate the quality of a network representation, we need to consider both the amount of information required to specify a random network model and the probability of recovering the original data when using the model as a generative process. To this end, we calculate the approximate size of network ensembles generated by the popular configuration model and its generalizations, including versions accounting for degree correlations and centrality layers. We then apply the minimum description length principle as a model selection criterion over the resulting nested family of configuration models. Using a dataset of over 100 networks from various domains, we find that the classic configuration model is generally preferred on networks with an average degree above 10, while a layered configuration model constrained by a centrality metric offers the most compact representation of the majority of sparse networks.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid39132478,

title = {Optimization and performance analytics of global aircraft-based wastewater surveillance networks},

author = {Guillaume St-Onge and Jessica T Davis and Laurent Hébert-Dufresne and Antoine Allard and Alessandra Urbinati and Samuel V Scarpino and Matteo Chinazzi and Alessandro Vespignani},

doi = {10.1101/2024.08.02.24311418},

year  = {2024},

date = {2024-08-01},

journal = {medRxiv},

abstract = {Aircraft wastewater surveillance has been proposed as a novel approach to monitor the global spread of pathogens. Here we develop a computational framework to provide actionable information for designing and estimating the effectiveness of global aircraft-based wastewater surveillance networks (WWSNs). We study respiratory diseases of varying transmission potentials and find that networks of 10 to 20 strategically placed wastewater sentinel sites can provide timely situational awareness and function effectively as an early warning system. The model identifies potential blind spots and suggests optimization strategies to increase WWSNs effectiveness while minimizing resource use. Our findings highlight that increasing the number of sentinel sites beyond a critical threshold does not proportionately improve WWSNs capabilities, stressing the importance of resource optimization. We show through retrospective analyses that WWSNs can significantly shorten the detection time for emerging pathogens. The presented approach offers a realistic analytic framework for the analysis of WWSNs at airports.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid38796449,

title = {Duality between predictability and reconstructability in complex systems},

author = {Charles Murphy and Vincent Thibeault and Antoine Allard and Patrick Desrosiers},

doi = {10.1038/s41467-024-48020-x},

issn = {2041-1723},

year  = {2024},

date = {2024-05-01},

journal = {Nat Commun},

volume = {15},

number = {1},

pages = {4478},

abstract = {Predicting the evolution of a large system of units using its structure of interaction is a fundamental problem in complex system theory. And so is the problem of reconstructing the structure of interaction from temporal observations. Here, we find an intricate relationship between predictability and reconstructability using an information-theoretical point of view. We use the mutual information between a random graph and a stochastic process evolving on this random graph to quantify their codependence. Then, we show how the uncertainty coefficients, which are intimately related to that mutual information, quantify our ability to reconstruct a graph from an observed time series, and our ability to predict the evolution of a process from the structure of its interactions. We provide analytical calculations of the uncertainty coefficients for many different systems, including continuous deterministic systems, and describe a numerical procedure when exact calculations are intractable. Interestingly, we find that predictability and reconstructability, even though closely connected by the mutual information, can behave differently, even in a dual manner. We prove how such duality universally emerges when changing the number of steps in the process. Finally, we provide evidence that predictability-reconstruction dualities may exist in dynamical processes on real networks close to criticality.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid38366192,

title = {Escherichia coli CRISPR arrays from early life fecal samples preferentially target prophages},

author = {Moïra B Dion and Shiraz A Shah and Ling Deng and Jonathan Thorsen and Jakob Stokholm and Karen A Krogfelt and Susanne Schjørring and Philippe Horvath and Antoine Allard and Dennis S Nielsen and Marie-Agnès Petit and Sylvain Moineau},

doi = {10.1093/ismejo/wrae005},

issn = {1751-7370},

year  = {2024},

date = {2024-01-01},

journal = {ISME J},

volume = {18},

number = {1},

abstract = {CRISPR-Cas systems are defense mechanisms against phages and other nucleic acids that invade bacteria and archaea. In Escherichia coli, it is generally accepted that CRISPR-Cas systems are inactive in laboratory conditions due to a transcriptional repressor. In natural isolates, it has been shown that CRISPR arrays remain stable over the years and that most spacer targets (protospacers) remain unknown. Here, we re-examine CRISPR arrays in natural E. coli isolates and investigate viral and bacterial genomes for spacer targets using a bioinformatics approach coupled to a unique biological dataset. We first sequenced the CRISPR1 array of 1769 E. coli isolates from the fecal samples of 639 children obtained during their first year of life. We built a network with edges between isolates that reflect the number of shared spacers. The isolates grouped into 34 modules. A search for matching spacers in bacterial genomes showed that E. coli spacers almost exclusively target prophages. While we found instances of self-targeting spacers, those involving a prophage and a spacer within the same bacterial genome were rare. The extensive search for matching spacers also expanded the library of known E. coli protospacers to 60%. Altogether, these results favor the concept that E. coli's CRISPR-Cas is an antiprophage system and highlight the importance of reconsidering the criteria use to deem CRISPR-Cas systems active.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid38154065,

title = {Nonlinear bias toward complex contagion in uncertain transmission settings},

author = {Guillaume St-Onge and Laurent Hébert-Dufresne and Antoine Allard},

doi = {10.1073/pnas.2312202121},

issn = {1091-6490},

year  = {2024},

date = {2024-01-01},

journal = {Proc Natl Acad Sci U S A},

volume = {121},

number = {1},

pages = {e2312202121},

abstract = {Current epidemics in the biological and social domains are challenging the standard assumptions of mathematical contagion models. Chief among them are the complex patterns of transmission caused by heterogeneous group sizes and infection risk varying by orders of magnitude in different settings, like indoor versus outdoor gatherings in the COVID-19 pandemic or different moderation practices in social media communities. However, quantifying these heterogeneous levels of risk is difficult, and most models typically ignore them. Here, we include these features in an epidemic model on weighted hypergraphs to capture group-specific transmission rates. We study analytically the consequences of ignoring the heterogeneous transmissibility and find an induced superlinear infection rate during the emergence of a new outbreak, even though the underlying mechanism is a simple, linear contagion. The dynamics produced at the individual and group levels are therefore more similar to complex, nonlinear contagions, thus blurring the line between simple and complex contagions in realistic settings. We support this claim by introducing a Bayesian inference framework to quantify the nonlinearity of contagion processes. We show that simple contagions on real weighted hypergraphs are systematically biased toward the superlinear regime if the heterogeneity of the weights is ignored, greatly increasing the risk of erroneous classification as complex contagions. Our results provide an important cautionary tale for the challenging task of inferring transmission mechanisms from incidence data. Yet, it also paves the way for effective models that capture complex features of epidemics through nonlinear infection rates.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid37879475,

title = {The umbrella value of caribou management strategies for biodiversity conservation in boreal forests under global change},

author = {Guillemette Labadie and Ilhem Bouderbala and Yan Boulanger and Jean-Michel Béland and Christian Hébert and Antoine Allard and Mark Hebblewhite and Daniel Fortin},

doi = {10.1016/j.scitotenv.2023.168087},

issn = {1879-1026},

year  = {2024},

date = {2024-01-01},

journal = {Sci Total Environ},

volume = {907},

pages = {168087},

abstract = {Single-species conservation management is often proposed to preserve biodiversity in human-disturbed landscapes. How global change will impact the umbrella value of single-species management strategies remains an open question of critical conservation importance. We assessed the effectiveness of threatened boreal caribou as an umbrella for bird and beetle conservation under global change. We combined mechanistic, spatially explicit models of forest dynamics and predator-prey interactions to forecast the impact of management strategies on the survival of boreal caribou in boreal forest. We then used predictive models of species occupancy to characterize concurrent impacts on bird and beetle diversity. Landscapes were simulated based on three scenarios of climate change and four of forest management. We found that strategies that best mitigate human impact on boreal caribou were an effective umbrella for maintaining bird and beetle assemblages. While we detected a stronger effect of land-use change compared to climate change, the umbrella value of management strategies for caribou habitat conservation were still impacted by the severity of climate change. Our results showed an interplay among changes in forest attributes, boreal caribou mortality, as well as bird and beetle species assemblages. The conservation status of some species mandates the development of recovery strategies, highlighting the importance of our study which shows that single-species conservation can have important umbrella benefits despite global change.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid38562286,

title = {NBS-SNI, an extension of the network-based statistic: Abnormal functional connections between important structural actors},

author = {Francis Normand and Mehul Gajwani and Daniel C Côté and Antoine Allard},

doi = {10.1162/netn_a_00344},

issn = {2472-1751},

year  = {2024},

date = {2024-01-01},

journal = {Netw Neurosci},

volume = {8},

number = {1},

pages = {44--80},

abstract = {Elucidating the coupling between the structure and the function of the brain and its development across maturation has attracted a lot of interest in the field of network neuroscience in the last 15 years. Mounting evidence supports the hypothesis that the onset of certain brain disorders is linked with the interplay between the structural architecture of the brain and its functional processes, often accompanied with unusual connectivity features. This paper introduces a method called the network-based statistic-simultaneous node investigation (NBS-SNI) that integrates both representations into a single framework, and identifies connectivity abnormalities in case-control studies. With this method, significance is given to the properties of the nodes, as well as to their connections. This approach builds on the well-established network-based statistic (NBS) proposed in 2010. We uncover and identify the regimes in which NBS-SNI offers a gain in statistical resolution to identify a contrast of interest using synthetic data. We also apply our method on two real case-control studies, one consisting of individuals diagnosed with autism and the other consisting of individuals diagnosed with early psychosis. Using NBS-SNI and node properties such as the closeness centrality and local information dimension, we found hypo- and hyperconnected subnetworks and show that our method can offer a 9 percentage points gain in prediction power over the standard NBS.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid38049512,

title = {Hypergraph reconstruction from uncertain pairwise observations},

author = {Simon Lizotte and Jean-Gabriel Young and Antoine Allard},

doi = {10.1038/s41598-023-48081-w},

issn = {2045-2322},

year  = {2023},

date = {2023-12-01},

journal = {Sci Rep},

volume = {13},

number = {1},

pages = {21364},

abstract = {The network reconstruction task aims to estimate a complex system's structure from various data sources such as time series, snapshots, or interaction counts. Recent work has examined this problem in networks whose relationships involve precisely two entities-the pairwise case. Here, using Bayesian inference, we investigate the general problem of reconstructing a network in which higher-order interactions are also present. We study a minimal example of this problem, focusing on the case of hypergraphs with interactions between pairs and triplets of vertices, measured imperfectly and indirectly. We derive a Metropolis-Hastings-within-Gibbs algorithm for this model to highlight the unique challenges that come with estimating higher-order models. We show that this approach tends to reconstruct empirical and synthetic networks more accurately than an equivalent graph model without higher-order interactions.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}