Modeling the Impact of Public Traffic on COVID-19 Spread Rates in Germany

Forschungspraxis project on traffic network reconstruction in the wake of COVID-19. This repository holds all information related to the Forschungspraxis project offered by LSR.

Abstract

This report builds on the idea of the networked SEIR model to recover pandemic spread parameters and apply the identified model for simulation/prediction of pandemic activity based on COVID-19 case data, public transport schedule information and estimated mobility behavior of a population. A data-driven methodology to the problems of transient network structure recovery and time-varying strength of adjacency estimation is presented. A case study on German data is implemented and the resulting models’ performances are evaluated numerically and graphically.

Index Terms

Epidemic modeling, System identification, Networked control systems, Data engineering

Meta

People

• Supervisor: Yuhong Chen [[email protected]]
• Student: Tobias Krug [[email protected]]

Chair

Chair of Automatic Control Engineering - TUM School of Computation, Information and Technology - Technical University of Munich

Repository structure

• doc: overall documentation of the Forschungspraxis and related material, e.g. URL collections
• prj: software projects, documentation and related material, e.g. data sets