Data-driven prediction of COVID-19 cases in Germany for decision making

Data-driven prediction of COVID-19 cases in Germany for decision making

Abstract Background The COVID-19 pandemic has led to a high interest in mathematical models describing and predicting the diverse aspects and implications of the virus outbreak. Model results represent an important part of the information base for the decision process on different administrative lev...

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Bibliographic Details
Main Authors: Lukas Refisch, Fabian Lorenz, Torsten Riedlinger, Hannes Taubenböck, Martina Fischer, Linus Grabenhenrich, Martin Wolkewitz, Harald Binder, Clemens Kreutz
Format: Article
Language:English
Published: BMC 2022-04-01
Series:BMC Medical Research Methodology
Subjects:
COVID-19
Infectious disease models
Input estimation
Ordinary differential equations
Parameter estimation
Nonlinear systems
Online Access:https://doi.org/10.1186/s12874-022-01579-9

Internet

https://doi.org/10.1186/s12874-022-01579-9

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