Titlebook: Computational Epidemiology; Data-Driven Modeling Ellen Kuhl Textbook 2021 The Editor(s) (if applicable) and The Author(s), under exclusive

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ilistic programming. This book is a personal reflection on the role of data-driven modeling during the COVID-19 pandemic, motivated by the curiosity to understand it..978-3-030-82892-9978-3-030-82890-5

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Wichard Woyke Dr. phil.,Udo Steffens of the COVID-19 pandemic. We compare the strengths and weaknesses of purely statistical and mechanistic models and illustrate how we can integrate the large volume of COVID-19 data into mechanistic compartment models to infer model parameters, learn correlations, and identify causation. The learnin

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,M?glichkeiten und Grenzen von Wahlen, data for the SIS model, infer the posterior distribution of its parameter values, and illustrate the result using means and credible intervals. The learning objectives of this chapter on data-driven modeling are to

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,M?glichkeiten und Grenzen von Wahlen,sit mobility for all 27 countries of the European Union, infer the dynamic reproduction number for each country, and correlate mobility and reproduction. The learning objectives of this chapter on data-driven modeling are to

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Textbook 2021ata science, epidemiology, health sciences, machine learning, mathematical biology, numerical methods, and probabilistic programming. This book is a personal reflection on the role of data-driven modeling during the COVID-19 pandemic, motivated by the curiosity to understand it..

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Introduction to data-driven epidemiology data for the SIS model, infer the posterior distribution of its parameter values, and illustrate the result using means and credible intervals. The learning objectives of this chapter on data-driven modeling are to

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