PhD defence by Maja Lykke Brinch

Principal supervisor:

• Professor Tine Hald, DTU Food

Co-supervisors:

• Senior Researcher Ana Sofia Ribeiro Duarte, DTU Food
• Assistant Professor Ofosuhene Okofrobour Apenteng, University of Copenhagen

Examiners:

• Senior Researcher Alessandro Foddai, DTU Food
• Senior Scientist Hanne-Dorthe Emborg, Statens Serum Institut
• Head of Science Department Katharina Stärk, Federal Food Safety and Veterinary Office, Bern, Switzerland

Chairperson at defence:

• Senior Researcher Lea Sletting Jakobsen

Resume
Antimicrobial resistance (AMR) is one of the biggest health challenges of our time. As resistance grows, infections become harder to treat, hospital stays get longer, and the risk of complications and death increases. This problem is made worse by the overuse and misuse of antibiotics in both people and animals. Without action, the consequences for global health and the economy could be severe.

To tackle this issue, governments and organisations around the world are working on action plans to use antibiotics more wisely and prevent infections, e.g., through vaccination. But how do we know which solutions will work best? This is where mathematical models come in. Much like weather forecasts, these models help decision-makers predict how infections and resistance spread—and how different strategies might stop them.

The overall aim of this PhD thesis was to strengthen the use of these models to better understand and address antimicrobial resistance. It focused on three key areas: identifying persistent gaps in current AMR models, modelling the impact of intervention on antibiotic use in viral-bacterial co-infections, and comparing ways to model the spread of resistant bacteria between animals, food, and people.

Manuscript I presents a review of existing AMR transmission models and highlights ongoing gaps. Although research in this area is expanding, many models still lack coverage of certain regions, drug-pathogen combinations, and interactions between viral and bacterial infections. In addition, poor documentation of model development makes it difficult for others to reuse or build upon existing work. The review calls for better data sharing and clearer reporting to improve collaboration and progress.

Manuscript II introduces a model to examine how respiratory syncytial virus (RSV) spreads in Denmark and its link to bacterial co-infections. It evaluates the impact of different interventions, like vaccines and monoclonal antibodies, on health outcomes and antibiotic use. The findings show that targeted interventions can significantly reduce severe outcomes and antibiotic use.

Manuscript III explore how resistant Escherichia coli (E. coli) spreads in Denmark, using two different modelling methods. A compartmental model examines transmission in groups such as farmers, pet owners, and the general public, while a source attribution model estimates the contributions of different sources to human infections—based on genetic data. The findings show that human-to-human transmission plays a major role, but food and animal sources also contribute. These insights support a “One Health” perspective, considering that human, animal, and environmental factors are deeply connected.

Together, these studies demonstrate how mathematical models can support efforts to understand and combat antimicrobial resistance. At the same time, they highlight the need for better data, transparency, and cross-sector collaboration. AMR is a complex problem with no single solution—but through collective action and informed strategies, important steps can be taken to protect public health worldwide.

A copy of the PhD thesis is available for reading at the department