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Quantitative and Predictive Infectious Disease Epidemiology

We use mathematical models to describe complex epidemiological and evolutionary processes, in order to make better predictions about the future of the epidemic. This to inform the hospital in particular and healthcare policy makers in general about the potential effectiveness of intervention measures and other policy choices. Our research focusses on antimicrobial resistance as well as respiratory infections such as COVID-19.


Projects

Our group studies the spread of antimicrobial resistance largely at the level of hospital networks. Such networks are formed by the patients exchanged and transferred between hospitals, and their structure influences the chances of AMR introduction in each hospital. We run the NEWiS project to gather information on hospital networks in multiple European countries. Furthermore, we collaborate closely with the CNAM (Paris, France), EHESP (Rennes, France), and Monash University (Victoria, Australia) to develop software solution (in the form of R packages) to facilitate network reconstruction. We further collaborate closely with the University Hospital Münster (Germany), University of Oxford (UK), UK Health and Safety Agency (UK), University Medical Center Groningen (NL) on this topic.


In spring 2024 we started a new project: ARCANE. ARCANE stands for Antimicrobial resistance control through adaptive networks, and we'll be looking at the effect of changes in hospital networks over time on the spread of antimicrobial resistance.

Analysis and Prediction Tool for COVID-19 Cases

During the COVID-19 pandemic, our group developed bed-demand forecasting models essential to the hospital’s pandemic planning. These models were generalised to fit any German hospital, based on where they think most of their admitted COVID-19 patients live. We then made this publicly available as a free online tool. The tool is continuously updated with the latest available data on regional incidence and bed occupancy. This project was the result of close collaboration with the university hospitals in Tübingen, Ulm, Heidelberg, and Mannheim.

Analysis and Prediction Tool for COVID-19 Cases


Team

Name 0761-270
Fabian Bürkin, mathematician, medical statistician 82380
Dr. rer. nat. Tjibbe Donker, Group Leader, biolinformatics, medical statistician 82550
Giovanna Donvito, guest scientist 82780
Marie-Rachel Garal, project coordinator E-mail
Daniel Goseberg, mathematician 82610
Raisa Kociurzynski, bioinformatics 82370
Alexis Papathanassopoulos, mathematician 82780

Publications

2024 

Donker, T., Papathanassopoulos, A., Ghosh, H., Kociurzynski, R., Felder, M., Grundmann, H., & Reuter, S. (2024). Estimation of SARS-CoV-2 fitness gains from genomic surveillance data without prior lineage classification. In Proceedings of the National Academy of Sciences (Vol. 121, Issue 25). https://doi.org/10.1073/pnas.2314262121

2023

Kociurzynski, R., D’Ambrosio, A., Papathanassopoulos, A. et al. Forecasting local hospital bed demand for COVID-19 using on-request simulations. Sci Rep 13, 21321 (2023).https://doi.org/10.1038/s41598-023-48601-8

Donker T.: The dangers of using large language models for peer review. Lancet Infect Dis. 2023 Jul;23(7):781.doi: 10.1016/S1473-3099(23)00290-6. Epub 2023 May 10

Scheithauer S,  Dilthey A, Bludau A, Ciesek S, Corman V, Donker T, Eckmanns T, Egelkamp R, Grundmann H, Häcker G, Kaase M, Lange B, Mellmann A, Mielke M, Pletz M, Salzberger B, Thürmer A, Widmer A, Wieler LH, Wolff T, Gatermann S, Semmler T:[Establishment of genomic pathogen surveillance to strengthen pandemic preparedness and infection prevention in Germany]. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz. 2023 Apr;66(4):443-449.doi: 10.1007/s00103-023-03680-w. Online

Salzberger B, Mellmann A, Bludau A, Ciesek S, Corman V, Dilthey A, Donker T, Eckmanns T, Egelkamp R, Gatermann SG, Grundmann H, Häcker G, Kaase M, Lange B, Mielke M, Pletz MW, Semmler T, Thürmer A, Wieler LH, Wolff T, Widmer AF, Scheithauer S.:An appeal for strengthening genomic pathogen surveillance to improve pandemic preparedness and infection prevention: the German perspective. Infection. 2023 Aug;51(4):805-811.doi: 10.1007/s15010-023-02040-9. Epub 2023 May 2. Online

Siebler L, Rathje T, Calandri M, Stergiaropoulos K, Donker T, Richter B, Spahn C, Nusseck M.:A coupled experimental and statistical approach for an assessment of SARS-CoV-2 infection risk at indoor event locations. BMC Public Health. 2023 Jul 20;23(1):1394.doi: 10.1186/s12889-023-16154-0.

2022

Budgell EP, Davies TJ, Donker T, Hopkins S, Wyllie DH, Peto TEA, Gill MJ, Llewelyn MJ, Walker AS.:Impact of antibiotic use on patient-level risk of death in 36 million hospital admissions in England.J Infect. 2022 Mar;84(3):311-320.doi: 10.1016/j.jinf.2021.12.029. Epub 2021 Dec 25. Online

2021

Donker T, Bürkin FM, Wolkewitz M, Haverkamp C, Christoffel D, Kappert O, Hammer T, Busch HJ, Biever P, Kalbhenn J, Bürkle H, Kern WV, Wenz F, Grundmann H.:Navigating hospitals safely through the COVID-19 epidemic tide: Predicting case load for adjusting bed capacity. Infect Control Hosp Epidemiol. 2021 Jun;42(6):653-658.doi: 10.1017/ice.2020.464. Epub 2020 Sep 15. Online

2020

Donker T.: Modelling how antimicrobial resistance spreads between wards. Elife. 2020 Nov 26;9:e64228.doi: 10.7554/eLife.64228. Online

Grundmann H, Donker T, Hengel H, Bürkle H, Hammer T, Wenz F, Kern W: Universelles Aufnahmescreening: Eintragungsrisiko beurteilen. Dtsch Arztebl 2020; 117(35-36): A-1610 / B-1378. Online

Group Leader

Dr. rer. nat. Tjibbe Donker

Biolinformatics, medical statistician

Tel.: +49 (0) 761 270 82550  

tjibbe.donker@uniklinik-freiburg.de