Project Details
PanVadere: Modeling and simulation of local infection spread in moving crowds
Applicant
Professorin Dr. Gerta Köster
Subject Area
Image and Language Processing, Computer Graphics and Visualisation, Human Computer Interaction, Ubiquitous and Wearable Computing
Data Management, Data-Intensive Systems, Computer Science Methods in Business Informatics
Epidemiology and Medical Biometry/Statistics
Statistical Physics, Nonlinear Dynamics, Complex Systems, Soft and Fluid Matter, Biological Physics
Traffic and Transport Systems, Intelligent and Automated Traffic
Data Management, Data-Intensive Systems, Computer Science Methods in Business Informatics
Epidemiology and Medical Biometry/Statistics
Statistical Physics, Nonlinear Dynamics, Complex Systems, Soft and Fluid Matter, Biological Physics
Traffic and Transport Systems, Intelligent and Automated Traffic
Term
since 2023
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 515675334
PanVadere wants to develop a model for local infection spread and implement it in a simulation tool. The model shall combine local movement, transmission via aerosols and a dose-response model. Findings on viral aerosols and the kinetics of breathing, coughing and sneezing will be taken into account. The software intends to enable computer-aided, quantitative experiments so that researchers can create simulation data for scenarios in which measurements are not available at all or incomplete. The software will be free and open-source. It will meet quality standards of software engineering. We will identify influential and uninfluential parameters in the model and order them according to importance. Methodologically, we will use variance-based indices. For a number of everyday scenarios, we will quantify the observation variables “degree of exposure” of virtual persons and the “number of infected persons”. For this purpose, we will propagate forward distributions of the influential input parameters through the model and, thus, obtain statistical distributions of the observation variables. The results will be visualized in a generally understandable way. We will compare the model to publicly available COVID-19 data to validate it. In particular, PanVadere will recreate at least to known super-spreading events and check whether the actual observations are within the scope of the probable results determined by forward propagation.
DFG Programme
Research Grants
International Connection
United Kingdom
Cooperation Partner
Anne Templeton, Ph.D.