Looking beyond borders: Disentangling international transmission dynamics towards ending the national HIV/AIDS epidemic in Switzerland

In the past decades, tremendous success has been made towards controlling the human immunodeficiency virus (HIV) epidemic, including diagnosis, treatment, and prevention measures. With the final goal of eliminating the epidemic, Switzerland successfully implemented early treatment and viral load monitoring for people living with HIV, and started a large-scale programme for HIV pre-exposure prophylaxis (PrEP). Nevertheless, in 2022, on average one new HIV diagnosis per day was reported in Switzerland, questioning whether the goal of reaching elimination is feasible. Different driving factors influencing the epidemic are at play, including migration, travelling, medical advances, sexual behaviour, other emerging infections, and humanitarian crises. The aim of the proposed project is to quantify the drivers of the ongoing HIV epidemic in Switzerland and beyond, and simulate different public health scenarios towards identifying optimal strategies for infectious disease elimination. I will parameterise mathematical and phylogenetic models by integrating a variety of different national and international data sources:In work package 1, I will develop phylogenetic methods to estimate the time and place of HIV infection for people living with HIV, and quantify cross-border HIV transmission events. I will combine detailed demographic and clinical information with international HIV sequence databases, and apply maximum-likelihood and Bayesian approaches. This will in particular lead to an estimation of pre- and post-migration HIV acquisition among migrants living with HIV, comprising around half of all people diagnosed with HIV in Switzerland in the last 10 years. Further, I will quantify the impact of cross-border transmission events on the Swiss HIV epidemic, stratified by nationality and HIV transmission group (men who have sex with men, heterosexuals, intravenous drug users). In work package 2, I will quantify the drivers of domestic HIV transmission in Switzerland: the impact of the large-scale roll-out of PrEP, retention in care, changes in sexual behavior, and the lockdown in 2020. I will implement a comprehensive mathematical model simulating the HIV epidemic in Switzerland from 2012 onwards, by combining national surveillance data with in-depth longitudinal information on people living with HIV, HIV-uninfected people interested in taking PrEP, and people visiting HIV counselling and testing centres. By simulating counterfactual scenarios and integrating results from work package 1, this will lead to a quantification of the main drivers of the ongoing HIV epidemic in Switzerland, and identification of the most effective scenarios towards curbing the epidemic.In work package 3, I will develop a European metapopulation model describing the spread of HIV and other sexually transmitted infections across Europe. Using international data sources, I will simulate and quantify the impact of public health scenarios on the HIV epidemic in Europe, including the roll-out of PrEP across Europe, different testing strategies, and the sudden crisis-related disruption of public health services in a country, for example due to the current war in Ukraine. Further, I will expand the metapopulation model towards sexually transmitted infections with transmission pathways similar to HIV, and simulate public health scenarios for newly emerging infections, such as vaccine allocation in the case of monkeypox.To summarize, I will develop complex mathematical and phylogenetic models addressing top-priority public health topics of infectious disease prevention and control. I will determine optimal prevention strategies towards curbing the HIV epidemic in Switzerland and Europe. I will intensify scientific collaborations and exchange of international HIV sequence data towards understanding cross-border transmission dynamics in Europe. From the methodological perspective, I will develop tailored, robust mathematical models to integrate different data sources, leading to a better understanding of real-world data application and integration of molecular and surveillance data.