Early detection for early action: integrating multiple data sources for monitoring the SARS-CoV-2 epidemic in near real-time

Lead Applicant: PD Dr. Christian Althaus (Cluster: Epidemiology)

Institute of Social and Preventive Medicine (ISPM)

Co-Lead Applicant: Prof. Dr. med. Guido Beldi (Cluster: Patient-Focused Research)

University Clinic of Visceral Surgery and Medicine

Co-Applicant: Dr. med. Julien Riou (Cluster: Epidemiology)

Recipient of MCID Career Development Grant

Institute of Social and Preventive Medicine (ISPM)

Co-Applicant: Prof. Dr. Raphael Sznitman (Cluster: Patient-Focused Research)

ARTORG Centet for Biomedical Engineering

Co-Applicant: PD Dr. Alban Ramette (Cluster: Microbiology)

Institute for Infectious Diseases (IFIK)

Co-Applicant: Prof. Dr. Kevin Heng (Cluster: Epidemiology)

Center for Space and Habitability (CSH)

Co-Applicant: Prof. Dr. Alexander Leichtle (Cluster: Patient-Focused Research)

University Institute of Clinical Chemistry

The SARS-CoV-2 pandemic has highlighted the importance of reliable surveillance systems to monitor epidemic trends for decision-making. We will develop a novel tool to track the spread of SARS-CoV-2 in the canton of Bern in near real-time, harnessing different data streams from electronic health records to social contact surveys, mobility patterns, and wastewater surveillance. Using methods from natural language processing and machine learning, we will determine early warning signals that can predict the epidemic dynamics. The tool will assist decision-makers to make informed decisions during the public health response against SARS-CoV-2 and in the face of future infectious disease threats.