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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書目名稱Computational Epidemiology影響因子(影響力)




書目名稱Computational Epidemiology影響因子(影響力)學(xué)科排名




書目名稱Computational Epidemiology網(wǎng)絡(luò)公開(kāi)度




書目名稱Computational Epidemiology網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書目名稱Computational Epidemiology被引頻次




書目名稱Computational Epidemiology被引頻次學(xué)科排名




書目名稱Computational Epidemiology年度引用




書目名稱Computational Epidemiology年度引用學(xué)科排名




書目名稱Computational Epidemiology讀者反饋




書目名稱Computational Epidemiology讀者反饋學(xué)科排名

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Wahlen in der Bundesrepublik Deutschland,ures of these two approaches, we derive and compare explicit and implicit network diffusion and finite element methods for the SIS model. The learning objectives of this chapter on network epidemiology are to

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grate data and physics-based modeling.This innovative textbook brings together modern concepts in mathematical epidemiology, computational modeling, physics-based simulation, data science, and machine learning to understand one of the most significant problems of our current time, the outbreak dynam

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BILIO

Textbook 2021, and machine learning to understand one of the most significant problems of our current time, the outbreak dynamics and outbreak control of COVID-19. It teaches the relevant tools to model and simulate nonlinear dynamic systems in view of a global pandemic that is acutely relevant to human health..