OutbreakFlow: Model-based Bayesian inference of disease outbreak dynamics with invertible neural networks and its application to the COVID-19 pandemics in Germany

OutbreakFlow: Model-based Bayesian inference of disease outbreak dynamics with invertible neural networks and its application to the COVID-19 pandemics in Germany

Mathematical models in epidemiology are an indispensable tool to determine the dynamics and important characteristics of infectious diseases. Apart from their scientific merit, these models are often used to inform political decisions and interventional measures during an ongoing outbreak. However,...

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Bibliographic Details
Main Authors: Bärnighausen, T. (Author), Chen, S. (Author), Eichel, V.M (Author), Graw, F. (Author), Köthe, U. (Author), Mutters, N.T (Author), Radev, S.T (Author)
Format: Article
Language:English
Published: Public Library of Science 2021
Subjects:
Article
artificial neural network
Bayes theorem
Bayes Theorem
biological model
coronavirus disease 2019
COVID-19
data processing
decision making
disease exacerbation
disease outbreak dynamics
disease transmission
epidemic
epidemiological monitoring
epidemiology
Germany
human
Humans
knowledge management
mathematical model
Models, Biological
Neural Networks, Computer
OutbreakFlow
pandemic
Pandemics
prediction
reliability
simulation
uncertainty
Uncertainty
Online Access:View Fulltext in Publisher

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