RaDAR: Risk and Disease burden of Antimicrobial Resistance

Start: 1 January 2018
Duration: 2 Years
Domain: Antimicrobial Resistance 
Keywords: Antimicrobial resistance, risk assessment, transmission, disease burden, evidence synthesis
Contact: Eelco FRANZ (RIVM)

The Project #RADAR

The RaDAR project aims to generate consensus estimates for sources attribution, risks of exposure and disease burden of AMR by integrating available data from various sources.

Resistance mechanisms emerge and spread globally. Circulation of antibiotic resistant bacteria in food and the environment and the resulting exposure of human beings to these bacteria may be significant. In general, information on the overall exposure to AMR from food and the environment is scarce. Therefore efforts are needed to fill data gaps and systematically integrate data into consensus estimates for sources attribution, risks of exposure and disease burden.

The RaDAR project aims at filling these gaps with a multidisciplinary and cross-member-state approach by:

  1. Addressing the relative and absolute contribution of animal and environmental sources to the public health burden of AMR,
  2. Linking data on antimicrobial consumption and the effects of different kinds of antimicrobial use on AMR in animal husbandry,
  3. Modeling the spread of resistance determinants in microbial communities, the environment and along the food chain,
  4. Quantifying human exposure and disease burden.

The project will develop generic risk and transmission models that may be adapted to various bacterial species and resistance determinants. It will generate consensus estimates for sources attribution, risks of exposure and disease burden of AMR by integrating available data from various sources and will lead to a consolidation of the international cooperation with respect to the assessment of risks related to the complex AMR problem.


Douarre, P E, Mallet, L, Radomski, N, Felten, A, Mistou, M. (2020). Analysis of COMPASS, a New Comprehensive Plasmid Database Revealed Prevalence of Multireplicon and Extensive Diversity of IncF Plasmids. Frontiers in Microbiology, 11, pp. 1-15. DOI: https://doi.org/10.3389/fmicb.2020.00483. 

Mughini-Gras, L, Dorado-García, A, van Duijkeren, E, van den Bunt, G, Dierikx, CM, Bonten, MJM, Bootsma, MCJ, Schmitt, H,  Hald, T, Evers, EG, de Koeijer, A, van Pelt, W, Franz, E, Mevius, DJ, Heederik, DJJ. (2019). Attributable Sources of Community-Acquired Carriage of Escherichia coli Containing β-Lactam Antibiotic Resistance Genes: A Population-Based Modelling Study. The Lancet Planetary Health, 3, pp. 357- 369. DOI: https://doi.org/10.1016/S2542-5196(19)30130-5

OpenAIRE Publications

Project Events

More information coming soon

Project Deliverables

RADAR Data Management Plan

D1.1 – Establishment of a database of synthetic, reference and genomic data

D1.2 – Establishment of a database of field (meta-)genomic data

D1.3- Automated assembly pipeline integrating de novo plasmid reconstruction

D1.4- Test and parameterization of the assembly pipeline for metagenomics data

D1.5- Biological annotations of plasmid identified in the pipeline

D1.6-1.7 GWAS-based method for genomic data analysis / Development of regression model for genomic data analysis

D2.1- Pharmacodynamics and transmission models

D2.2- A risk assessment to predict the sustainability of ESBL-producing E.Coli carriage within commercial pig farms

D2.3- The relevance of transmission routes of antibiotic resistant bacteria calculated using different methodologies and the relevance of routes

D2.4- Report on intervention strategies

D3.1- Inventory of available exposure assessment models and related data and transfer to FSK Standard

D3.2- Scientific report on a generic model for the chicken production chain

D3.3- Scientific Report on an adapted model fort he pork production chain

D3.4- Scientific report on a model of AMR exposure through shellfish consumption

D3.5- Scientific report on a generic comparative exposure assessment model

D3.6- Comparative Exposure Assessment of ESBL-Producing Escherichia coli Through Seafood Consumption

D4.1- Machine learning methods for quantification of risk and health effects

D5.1- Methodology to estimate the disease burden attributable to antimicrobial resistance

D5.2- A proposal for a new paradigm for AMR surveillance

D5.3- Attributable sources of community-acquired carriage of Escherichia coli containing β-lactam antibiotic resistance genes

D6.1- Integration of information by Bayesian evidence synthesis



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