Comparison of screening strategies to detect patients infected with hepatitis C virus in Switzerland: mathematical model Maryam Sadeghimehr1, Barbara Bertisch 1, Francesco Negro2, Olivia Keiser1, Janne Estill1 1Institute of Global Health, University of Geneva, Geneva, Switzerland, 2University Hospital of Geneva, Geneva, Switzerland Background Hepatitis C virus (HCV) is a major cause of liver disease and liver-related mortality About 40,000 people were estimated to be chronically infected with the HCV in Switzerland in 2016 A considerable proportion of people living with HCV may be unaware of their infection until the onset of severe symptoms, which often appear only several years later Figure 2: A comparison of the model’s outputs and observed data: Number of new cases of DC and HCC projected by the model (top left), Number of new HCC cases (model vs Swiss cancer registry data; top right); Number of annual deaths attributable to HCV (model vs Swiss death registry data; bottom left); Number of annual diagnoses (model vs notification data to the Swiss Federal Office of Public Health) Progress Fig. 2 shows a comparison of the model’s outputs with observed data: differences in early years likely due to differing definitions and uncertainty around the data The preliminary results suggest no considerable differences between most screening strategies (Fig. 3) Aims Our project aims to develop a mathematical model to: Analyze the progression of Hepatitis C disease in Switzerland (Fig. 1) Evaluate the effect of different HCV screening strategies based on birth year or current or former intravenous drug use Figure 3: Model run output of the effect of the different screening strategy on the number of diagnosed patients. Current scenario in Switzerland is that after 2018 all the people can be treated by DAA, but before 2018 just the people who had liver disease of stage F2 or more where eligible to be treated. Discussion & Steps Forward High uncertainty and ambiguous definitions in data on the HCV epidemic make the calibration of the model challenging We will conduct sensitivity analyses on key parameters such as disease progression rates and past diagnosis rates We will model additional screening strategies: origin-based, universal Acknowledgements Maryam Sadeghimehr is being supported by a grant from the Federal office of Public health. We thank Christian Schaetti, Jean-Luc Richard, Daniel Koch, Mark Witschi, Gilles Wandeler and Claude Scheidegger for helpful comments. Figure 1: Structure of the model