Public Health Research

Our main interest is in developing mathematical & computational methods that support data-driven public health decision making. We are mainly focussing on two application areas:

HIV-1 prevention with pre-exposure prophylaxis (PrEP)

The human immunodeficiency virus (HIV) constitutes a major burden in risk groups, such as men-who-have-sex-with men in most Western countries, as well as young women in many sub-Saharan African countries. To date, HIV infection cannot be cured, requiring life-long therapy to prevent disease and death. Hence, a major public health priority is to prevent infection in the first place. Unfortunately, an effective vaccine is not available for this purpose. However, pre-exposure prophylaxis (PrEP) has become an effective tool to prevent HIV infection. During PrEP, antiviral medication is given at a sub-therapeutic regimen to prevent the establishment of an infection, in the case of (e.g. sexual) HIV exposure. In 2013, the first compounds were approved for PrEP, and nowadays several options are available at low cost and covered by statutory health insurances in many settings.

In our research, we analyse (i) drug-dosing and adherence demands to achieve protective PrEP concentrations, (ii) factors associated with incomplete protection, as well as (iii) uptake and use of PrEP in real-life scenarios. Methodologically, we integrate heterogeneous data sets using hypothesis-driven mechanistic, as well as data-driven multi-scale stochastic modelling.

Key references

Zhang L, Iannuzzi S, et al. Synthesis of protective PrEP adherence levels in cisgender women using convergent clinical and bottom-up modelling. submitted, 2023. preprint

Zhang L, et al. Numerical approaches for the rapid analysis of prophylactic efficacy against HIV with arbitrary drug-dosing schemesPLoS Comput Biol. 2021;17(12):e1009295

S. Duwal, et al. Multi-scale Systems-Pharmacology pipeline to assess the prophylactic efficacy of NRTIs against HIV-1CPT: Pharmacometrics & Systems Pharmacology, 5 (2016), 377

SARS-2 prevention, surveillance & evolution

In 2020, SARS-CoV-2 started spreading globally and became one of the most severe pandemics to date. Rapid response to this newly emerging virus, in light of a dynamically evolving scientific knowledge became paramount in fighting the epidemic.

In our research, we develop methods to support data-driven decision making and surveillance of SARS-CoV-2. In particular, we built into-host models of viral kinetics that were used to inform quarantine, isolation and testing strategies to prevent the spread of SARS-CoV-2. Moreover, we built methods for genomic surveillance of SARS-CoV-2 and to infer SARS-CoV-2 incidence and under detection from genomic surveillance data. More recently, we are interested in (i) the mechanisms that lead to the emergence , selection and spread of novel SARS-CoV-2 variants, as well as in (ii) developing methods for the post-pandemic surveillance of the virus.

Key references

van der Toorn W, et al. An intra-host SARS-CoV-2 dynamics model to assess testing and quarantine strategies for incoming travelers, contact management, and de-isolationPatterns (N Y). 2021;2(6):100262

Smith MR, et al.. Rapid incidence estimation from SARS-CoV-2 genomes reveals decreased case detection in Europe during summer 2020Nat Commun. 2021 Oct 14;12(1):6009

Oh DY, et al. Advancing Precision Vaccinology by Molecular and Genomic Surveillance of Severe Acute Respiratory Syndrome Coronavirus 2 in Germany, 2021Clin Infect Dis. 2022;75:S110-S120

This work is currently supported through different funding agencies, including the BMBF, EU, the DFG-research center MATH+, as well as the Robert-Koch-Institute.