Zeitschriftenartikel:
N. Popper, M. Zechmeister, D. Brunmeir, C. Rippinger, N. Weibrecht, C. Urach, M. Bicher, G. Schneckenreither, A. Rauber:
"Synthetic Reproduction and Augmentation of COVID-19 Case Reporting Data by Agent-Based Simulation";
Data science journal,
20
(2021),
1;
16 S.
Kurzfassung englisch:
We generate synthetic data documenting COVID-19 cases in Austria by the means of an agent-based simulation model. The model simulates the transmission of the SARS-CoV-2 virus in a statistical replica of the population and reproduces typical patient pathways on an individual basis while simultaneously integrating historical data on the implementation and expiration of population-wide countermeasures. The resulting data semantically and statistically aligns with an official epidemiological case reporting data set and provides an easily accessible, consistent and augmented alternative. Our synthetic data set provides additional insight into the spread of the epidemic by synthesizing information that cannot be recorded in reality.
Schlagworte:
COVID-19, synthetic data, agent-based simulation, data augmentation
"Offizielle" elektronische Version der Publikation (entsprechend ihrem Digital Object Identifier - DOI)
http://dx.doi.org/10.5334/dsj-2021-016
Erstellt aus der Publikationsdatenbank der Technischen Universität Wien.