Infectious Disease Modelling - Group Kretzschmar

Infectious Disease Modelling


We aim to understand the dynamics of infectious diseases and the impact of interventions on transmission dynamics. To achieve this aim, we work on the interface of data analysis and mathematical modelling. We use various types of data, such as surveillance data, pathogen genetic data, data from epidemiological studies, and serological data, to inform mathematical models. We conduct scenario analyses and assess effectiveness of interventions. We focus on the dynamics of HIV and other STI, on hospital infections and antimicrobial resistance, and on the relationship between social networks and infectious disease transmission.  

Research Program

Research interests

  • Modelling hospital infections and antibiotic resistance
  • HIV transmission dynamics and impact of interventions
  • Relationship between social networks, health behaviour and outbreak dynamics

Key publications

  1. Pham, T. M., et al. (2019). "Tracking Pseudomonas aeruginosa transmissions due to environmental contamination after discharge in ICUs using mathematical models." PLoS Comput Biol 15(8): e1006697. View
  2. Rozhnova, G., et al. (2019). "Impact of sexual trajectories of men who have sex with men on the reduction in HIV transmission by pre-exposure prophylaxis." Epidemics 28: 100337. View
  3. Stein, M. L., et al. (2018). "A stochastic simulation model to study respondent-driven recruitment." PLoS One 13(11): e0207507. View
  4. Cassini, A., et al. (2019). "Attributable deaths and disability-adjusted life-years caused by infections with antibiotic-resistant bacteria in the EU and the European Economic Area in 2015: a population-level modelling analysis." Lancet Infect Dis 19(1): 56-66. View
  5. Rozhnova, G., et al. (2018). "Elimination prospects of the Dutch HIV epidemic among men who have sex with men in the era of preexposure prophylaxis." Aids 32(17): 2615-2623. View



Gini in a bottle: Impact of PrEP on sexual behavior and sexually transmitted infections in the MSM population

Making it count 2: Risk underestimation, high-risk sexual behavior episodes and estimating the impact of tailored interventions, a novel approach to HIV elimination

Harnessing social networks for infectious disease control, Proposal TOP subsidie (2016)


Effectiveness of infection control strategies against intra- and inter-hospital transmission of MultidruG resistant Enterobacteriaceae – insights from a multi-level mathematical NeTwork model (Emerge-net) (2016)