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Arnoux, S.

Publications and source records attributed to Arnoux, S..

2 recordsLinked to original sources

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↗

Environmental heterogeneity drives tsetse fly population dynamics

A spatially and temporally heterogeneous environment may lead to unexpected population dynamics, and knowledge still is needed on which of the local environment properties favour population maintenance at larger scale. As regards pathogen vectors, such as tsetse flies transmitting human and animal African trypanosomosis, such a knowledge is crucial for proposing relevant management strategy. We developed an original mechanistic spatio-temporal model of tsetse fly population dynamics, accounting for combined effects of spatial complexity, density-dependence, and temperature on the age-structured population, and parametrized with field and laboratory data. We confirmed the strong impact of temperature and adult mortality on tsetse populations. We showed that patches with the lowest mean temperatures and lowest variations act as refuges when adult mortality is homogeneously increased. Our results highlighted the importance of baseline data collection to characterize the targeted ecosystem before any control measure is implemented to maximize its efficiency.

ecology↗