Since the beginning of the COVID-19 pandemic, our institute was one of the three modelling teams tasked with providing weekly short-term forecasts for the numbers of confirmed cases in the upcoming weeks, and how they will translate into hospital bed occupancy. I present the forecast system that we designed and the role that it played in the Austrian response to the pandemic to date, in particular with regard to strengthening and easing non-pharmaceutical interventions (NPIs). To be able to better anticipate the impact of future NPIs on case numbers in these forecasts, we quantified their effectiveness by statistically analyzing the implementation of approximately 48,000 NPIs in 226 countries across three independent datasets (government response trackers) using four different computational techniques merging statistical, inference and artificial intelligence tools. We find that the effectiveness of NPIs depends on the local context such as timing of their adoption. Finally, we present a recently developed agent-based epidemiological modelling framework to design optimal prevention strategies for curbing the spread of SARS-CoV-2 in specific settings, namely schools and nursing homes. In both cases, the models were calibrated using extensive Austrian contact tracing data. In brief, we find that a suitable combination of measures is necessary to achieve control of the virus in these settings and that the usefulness of screening strategies depends crucially on turnover times of the test results.

Peter Klimek is Associate Professor at the Medical University of Vienna and faculty member of the Complexity Science Hub Vienna. Peter was awarded a PhD in physics in 2010 and a Venia Docendi (Habilitation) in computational science in 2018. Drawing from his expertise in complexity science, data science, statistics and physics, his research aims to improve our understanding and ability to predict complex socio-economic systems, ranging from human disease over healthcare systems to economic and financial systems.

Peter and his research team developed prediction and stress-test models for how people acquire more and more chronic disorders as they age, how healthcare systems cope with changes in their workforce, and how shocks disrupt economic and financial markets. He invented a novel statistical test to detect signs of electoral fraud and was the first to mathematically prove that governments are bound to become ineffective over time. He authored a textbook an the Theory of Complex Systems (together with S. Thurner and R. Hanel) and operated a model used by the Austrian government to forecast the COVID-19 epidemics in Austria.

Labs:

https://www.csh.ac.at

http://www.complex-systems.meduniwien.ac.at/

Links to Publicatons / preprints:

'Ranking the effectiveness of worldwide COVID-19 government interventions': https://www.nature.com/articles/s41562-020-01009-0

'Supporting Austria through the COVID-19 Epidemics with a Forecast-Based Early Warning System': https://www.medrxiv.org/content/10.1101/2020.10.18.20214767v2

'Agent-based simulations for optimized prevention of the spread of SARS-CoV-2 in nursing homes': https://osf.io/5xdqe/