The objective of this WP is to develop mathematical models for assessing the impact of various treatment and prevention strategies to reduce the burden of AMR. The models will be adaptable to specific infectious agents and intervention strategies and their usefulness will be illustrated through several case studies selected on the basis of the prioritised pathogens and the outcomes of WP1 (Burden of disease).

A systematic literature review and evaluation of existing AMR models will form the basis for the development of new models, including the theoretical definitions of model parameters. The outcomes of the models will include disease burden estimates of AMR in different human subpopulations expressed as incidence rates of relevant health outcomes and will estimate the effect on the disease burden of introducing vaccination and/or mAb.

The models will guide the data collection in WP3 (Data gathering) and will provide inputs for WP4 (Cost-effectiveness analysis).


  1. To conduct a systematic review and evaluation of existing AMR models.  

  2. To define the theoretical framework and assumptions for population-based AMR models.  

  3. To apply and adapt the population-based AMR models to selected case studies.  

  4. To identify data gaps for modelling purposes. 

Academic Lead:  Prof. Frank M. Aarestrup, DTU: Dr. Frank Aarestrup is a Professor and Head of the Genomic Epidemiology Unit at the Technical University of Denmark (DTU), and a member of the Danish Academy of the Technical Sciences. He has an extensive publication history focusing on surveillance, molecular typing, and antimicrobial resistance of veterinary and human pathogens, and in 2009 he received the Danish Elite-research prize. In collaboration he developed, evaluated, and implemented several methods and defined threshold values for susceptibility testing of several bacterial species. In 2011 he helped start the Global Microbial Identifier (GMI). 


Lead: Prof. Tine Hald, DTU: Dr. Tine Hald is a Professor in translational epidemiology. Her work is focused on the development of mathematical models for explaining the trends and sources, and assessing the burden of zoonoses, food-borne diseases and AMR. She is currently PI of two international projects on burden and source attribution modelling (FOCAL and DiSCoVeR ). She was a core member of the WHO Foodborne disease Epidemiology Reference Group (FERG) from 2007-2016, and member of the EFSA expert panel on biological hazards (BIOHAZ) from 2009-2015. 


Industry Lead:  Dr. Andrea Palladino (GSK): Dr. Andrea Palladino is a Senior Data Scientist who holds a PhD in Physics from the Gran Sasso Science Institute (L’Aquila, Italy). Andrea joined GSK after 3 years as Postdoctoral researcher at Deutsches Elektronen-Synchrotron and 2 years at Apheris AI, a start-up based in Berlin and focused on artificial intelligence. There he worked on several data science projects, including the development of ML models for medical image analysis based on Convolutional Neural Networks and the development of QSAR models for drug discovery applications. Throughout his career, Dr. Palladino published >20 manuscripts in high impact peer reviewed journals and he was in the organizing committee of 2 international conferences of Physics. In the last 2 years, he has developed epidemiological models for the analysis and predictions of the spread of Covid19 in Italy, and his work has been featured by Italian media and presented at SIS conference (Italian Statistical Society). Apart from work, Dr. Palladino has a piano diploma and he is a master of chess, a game that he has been practicing for over 15 years.