Modeling and Simulation of SARS-CoV-2 Infections, Hospitalizations and Outcome in Germany
Christiane Dings, Dominik Selzer, Thorsten Lehr
Saarland University, Saarbrücken, Germany
The COVID-19 pandemic challenged many national health care systems with hospitals reaching capacity limits of intensive care units (ICU). Hence, estimating the acute local burden of ICUs is of vital importance for an adequate management of health care resources.
In this work, we applied non-linear mixed effects modeling techniques to develop a compartmental infection and outcome model for Germany, the 16 federal states, and 412 counties that describes COVID-19 related inpatients, ICU patients with and without mechanical ventilation, recoveries, and fatalities based on confirmed SARS-CoV-2 infections. Furthermore, important covariates influencing the relation between infections and outcomes were explored.
Non-pharmaceutical interventions imposed by the governments were found to have a major impact on the spread of SARS-CoV-2. Age and sex of the patients, variants of concern, vaccination and the testing strategy (number of tests performed weekly, test positive rate) influenced the correlation between confirmed cases and severity as well as outcome. The base model is implemented in a Shiny application and publicly available under https://covid-simulator.com/ which allows the simulation of future pandemic scenarios considering change in infectiousness, varying vaccination rates and the impact of seasonality.