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  • 1
    Electronic Resource
    Electronic Resource
    LETTERS TO THE EDITOR (2000)
    Oxford, UK : Blackwell Publishing Ltd
    Pacing and clinical electrophysiology 23 (2000), S. 0 
    Details
    ISSN: 1540-8159
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Medicine
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/j.1540-8159.2000.tb00927.x
    Library Location Call Number Volume/Issue/Year Availability
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    Paper (German National Licenses)
    Fulltext
  • 2
    Electronic Resource
    Electronic Resource
    Stereotactic breast biopsy (1996)
    Springer
    Annals of surgical oncology 3 (1996), S. 106-106 
    Details
    ISSN: 1534-4681
    Source: Springer Online Journal Archives 1860-2000
    Topics: Medicine
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF02409060
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    Paper (German National Licenses)
    Fulltext
  • 3
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    Unknown
    Neural parameter calibration and uncertainty quantification for epidemic forecasting (2023)
    Details
    Publication Date: 2024-02-02
    Description: The recent COVID-19 pandemic has thrown the importance of accurately forecasting contagion dynamics and learning infection parameters into sharp focus. At the same time, effective policy-making requires knowledge of the uncertainty on such predictions, in order, for instance, to be able to ready hospitals and intensive care units for a worst-case scenario without needlessly wasting resources. In this work, we apply a novel and powerful computational method to the problem of learning probability densities on contagion parameters and providing uncertainty quantification for pandemic projections. Using a neural network, we calibrate an ODE model to data of the spread of COVID-19 in Berlin in 2020, achieving both a significantly more accurate calibration and prediction than Markov-Chain Monte Carlo (MCMC)-based sampling schemes. The uncertainties on our predictions provide meaningful confidence intervals e.g. on infection figures and hospitalisation rates, while training and running the neural scheme takes minutes where MCMC takes hours. We show convergence of our method to the true posterior on a simplified SIR model of epidemics, and also demonstrate our method's learning capabilities on a reduced dataset, where a complex model is learned from a small number of compartments for which data is available.
    Language: English
    Type: article , doc-type:article
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