Biology subjects

Beaunee, G.

Publications and source records attributed to Beaunee, G..

3 recordsLinked to original sources

bioRxiv2026

A modelling challenge to improve preparedness for highly pathogenicavian influenza epidemics: objectives, design and synthetic datageneration

Highly pathogenic avian influenza (HPAI) is among the most significant disease threats to poultry worldwide. Modelling challenges, where independent teams address a common epidemiological scenario, can support the development of response capabilities and strengthen communication between modellers and risk managers. Despite the established benefits of these exercises, they remain uncommon in animal health, and none have previously addressed avian influenza. Here Jolly Island, an artificial landscape of 9160 poultry farms with five production types, was developed and seeded with stochastic HPAI epidemics that ran their course over a three-to-four month period. From January through April 2026, 45 registered teams across five continents investigated a selected synthetic epidemic, producing probabilistic forecasts, inferring transmission drivers, and evaluating counterfactual control policies. Here the objectives, design choices, and data generation of the HPAI Modelling Challenge are described in full, covering transmission processes, surveillance system operations, and control strategy implementation, along with the selected epidemic trajectory, the narrative situation reports supplied to the teams at each phase, and the complete phase-specific datasets released to them. As the opening paper of this special issue, here is provided the foundation upon which all subsequent contributions, where teams document their approaches and analyses, are built.

ecology↗

bioRxiv2024

Silent reservoirs are shaping disease emergence: the case of Usutu virus in the Netherlands

Disentangling contributions of different hosts to disease transmission is highly complex, but critical for improving predictions, surveillance, and response. This is particularly challenging in wildlife, with pathogens often infecting multiple species and data collection being difficult. Using the emergence of Usutu virus (USUV) in the Netherlands as a case study, we demonstrate the use of an Approximate Bayesian Computation framework on diverse data sources to uncover drivers of spatio-temporal wildlife disease emergence. We calibrated single- and multi-host mechanistic transmission models to five types of wildlife surveillance and research data, describing molecular and serological evidence of USUV in birds. Although Eurasian blackbirds, the primary target species for surveillance, were most severely affected, our models indicated that USUV could not persist in blackbirds alone. Our framework provided statistical support for additional, unobserved bird species to have contributed to transmission. This population of bird species is characterised by limited infection mortality, a longer lifespan, and likely further dispersal than blackbirds. Immunity in this population appears to have protected blackbirds from further USUV-related population decline. Our results underscore the importance of considering multiple host populations to understand outbreak dynamics. Neglecting the multi-host context of transmission can impact the reliability of predictions and projected impact of interventions.

ecology↗

bioRxiv2019

EMULSION: transparent and flexible multiscale stochastic models in epidemiology

Stochastic mechanistic models are essential to predictive epidemiology, to better explore biological assumptions and anticipate effects of control measures on pathogen spread. Their development is usually long and iterative, involving multi-disciplinary knowledge integration. However, knowledge often becomes lost in model code, hindering late changes in assumptions and often making models appearing as black boxes to end-users. We introduce here EMULSION, an artificial intelligence-based software intended to help modellers focus on model design rather than programming. EMULSION defines a domain-specific language to make all components of an epidemiological model (structure, processes, parameters...) explicit as a structured text file. This file is readable by scientists from other fields (epidemiologists, biologists, economists), who can contribute to validate or revise assumptions at any stage of model development. It is then automatically processed by EMULSION generic simulation engine, preventing any discrepancy between model description and implementation. The modelling language and simulation architecture both rely on the combination of advanced artificial intelligence methods (knowledge representation and multi-level agent-based simulation), allowing several modelling paradigms (from compartment- to individual-based models) at several scales (up to metapopulations). The flexibility of EMULSION and its capability to support iterative modelling are illustrated here through examples of progressive complexity, including late revisions of core model assumptions. EMULSION is also currently used to model the spread of several diseases in real pathosystems (zoonoses such as Q fever or the vector-borne Rift Valley fever; or bovine respiratory diseases, with a focus on detection and treatment protocols). EMULSION provides a command-line tool for checking models, producing model diagrams, running simulations, and plotting outputs. Implemented in Python 3, EMULSION runs on Linux, MacOS, and soon Windows. It is released under Apache-2.0 license. A comprehensive documentation with installation instructions, tutorial and examples is available from: https://sourcesup.renater.fr/emulsion-public.

epidemiology↗