Memory-based meso-scale modeling of Covid-19

Memory-based meso-scale modeling of Covid-19

Publication: Memory-based meso-scale modeling of Covid-19

Authors: Andreas Kergaßner, Christian Burkhardt, Dorothee Lippold, Matthias Kergaßner, Lukas Pflug, Dominik Budday, Paul Steinmann, Silvia Budday

Link to publication: Computational mechanics

Abstract: 
The COVID-19 pandemic has led to an unprecedented world-wide effort to gather data, model, and understand the viral spread. Entire societies and economies are desperate to recover and get back to normality. However, to this end accurate models are of essence that capture both the viral spread and the courses of disease in space and time at reasonable resolution. Here, we combine a spatially resolved county-level infection model for Germany with a memory-based integro-differential approach capable of directly including medical data on the course of disease, which is not possible when using traditional SIR-type models. We calibrate our model with data on cumulative detected infections and deaths from the Robert-Koch Institute and demonstrate how the model can be used to obtain county- or even city-level estimates on the number of new infections, hospitality rates and demands on intensive care units. We believe that the present work may help guide decision makers to locally fine-tune their expedient response to potential new outbreaks in the near future.