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24, chemin de Borde Rouge –Auzeville – CS52627
31326 Castanet Tolosan CEDEX - France

Dernière mise à jour : Mai 2018

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DYNAMO: Modelling in population dynamics and animal epidemiology

Understanding, anticipating and controlling the spread of large-scale animal diseases through mechanistic modelling

DYNAMO aims to better understand and predict the spread and persistence of pathogens in animal populations, and to identify efficient and targeted control strategies. Such complex biological systems are studied at the within-host, between-host, and metapopulation scales, also using in the latter case trade network data and graph theory. We mainly focus on infectious diseases of cattle and swine, as well as on vector population dynamics.

Expertise & skills

  • Predictive modelling approaches combining mechanistic models and data-driven simulations
  • Model reproducibility, robustness, and clarity: a generic multiscale simulation framework to ease knowledge and data integration, and to capitalise developed models
  • Development of innovative inference methods to promote realistic models and quantify uncertain processes
  • Decision support tools for health advisers to guide on-farm management of livestock infectious risks and associated public health issues

Major projects

  • ATOM: Automatisation of the software process from epidemiological models to decision support tools
  • CaDeNCE: Spread of epidemic processes on dynamical networks of animal movements with application to cattle in France (funded by ANR)
  • DECIDE: Data-driven control and prioritisation of non-EU-regulated contagious animal diseases
  • FORESEE: Virus-host-environment interplay and drivers behind pathogen emergence, spread and persistence: Rift Valley fever (RVF) as a case study (funded by INRA, metaprogramme GISA)
  • PPApred: Predict the spread of african swine fever (ASF) at the wildlife / pig herd interface (funded by AHD, INRA)
  • RobustInfer: Combine and estimate: towards a reduction in data complexity for a better calibration of large-scale dynamic epidemiological models