標題: Titlebook: Computational Epidemiology; Data-Driven Modeling Ellen Kuhl Textbook 2021 The Editor(s) (if applicable) and The Author(s), under exclusive [打印本頁] 作者: 生手 時間: 2025-3-21 16:55
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)學科排名
書目名稱Computational Epidemiology網(wǎng)絡公開度
書目名稱Computational Epidemiology網(wǎng)絡公開度學科排名
書目名稱Computational Epidemiology被引頻次
書目名稱Computational Epidemiology被引頻次學科排名
書目名稱Computational Epidemiology年度引用
書目名稱Computational Epidemiology年度引用學科排名
書目名稱Computational Epidemiology讀者反饋
書目名稱Computational Epidemiology讀者反饋學科排名
作者: Insubordinate 時間: 2025-3-21 21:02 作者: ANTIC 時間: 2025-3-22 02:52
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作者: relieve 時間: 2025-3-22 06:03 作者: figurine 時間: 2025-3-22 09:26
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作者: 很是迷惑 時間: 2025-3-22 13:35 作者: 很是迷惑 時間: 2025-3-22 21:02 作者: 枯燥 時間: 2025-3-22 22:42 作者: LAVE 時間: 2025-3-23 02:13 作者: BILIO 時間: 2025-3-23 09:24
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..作者: CREST 時間: 2025-3-23 13:10 作者: canonical 時間: 2025-3-23 16:32
The computational SIR modelo the analytical solution of the SIS model and show its sensitivity to the infectious period, reproduction number, and initial conditions. To illustrate the features of the SIR model, we simulate the early COVID-19 outbreak in Austria using reported case data. The learning objectives of this chapter on computational SIR modeling are to作者: Budget 時間: 2025-3-23 20:45
Introduction to network epidemiologyures 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作者: Phagocytes 時間: 2025-3-24 00:07
The network SEIR modelxplicit and implicit time integration schemes to solve it. To illustrate the features of the network SEIR model, we simulate the early COVID-19 outbreak in the United States and the European Union using reported case data and air travel statistics. The learning objectives of this chapter on SEIR network modeling are to作者: POWER 時間: 2025-3-24 03:20 作者: 巨頭 時間: 2025-3-24 08:17 作者: companion 時間: 2025-3-24 11:00
,Die Parteien — Tr?ger der Wahl,oint methods for the SIS model. We calculate their errors compared to the analytical solution and discuss concepts of convergence and accuracy. The learning objectives of this chapter on computational epidemiology are to作者: nettle 時間: 2025-3-24 17:34
Wahlen in der Bundesrepublik Deutschland,e data. We compare two strategies to model outbreak control, the potentially susceptible population approach and the dynamic SEIR model. The learning objectives of this chapter on computational SEIR modeling are to作者: 寬宏大量 時間: 2025-3-24 19:21 作者: 言外之意 時間: 2025-3-25 00:51
The computational SEIR modele data. We compare two strategies to model outbreak control, the potentially susceptible population approach and the dynamic SEIR model. The learning objectives of this chapter on computational SEIR modeling are to作者: Wordlist 時間: 2025-3-25 04:32
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作者: craving 時間: 2025-3-25 09:44
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作者: staging 時間: 2025-3-25 12:33
,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作者: menopause 時間: 2025-3-25 16:05
,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作者: IST 時間: 2025-3-25 22:14 作者: 滑動 時間: 2025-3-26 03:14 作者: Pulmonary-Veins 時間: 2025-3-26 07:06
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..作者: 清楚 時間: 2025-3-26 09:41 作者: institute 時間: 2025-3-26 15:37
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作者: 定點 時間: 2025-3-26 19:35 作者: ornithology 時間: 2025-3-26 21:16
Data-driven dynamic SEIIR modeleling can illustrate the potential effects of asymptomatic transmission and visualize the dynamics of the asymptomatic population for various different scenarios. Knowing the exact dimension of the asymptomatic transmission is critical to estimate the true severity of the outbreak, its hospitalizati作者: 流浪者 時間: 2025-3-27 03:09 作者: BULLY 時間: 2025-3-27 05:35 作者: Phagocytes 時間: 2025-3-27 11:30
978-3-030-82892-9The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: 顧客 時間: 2025-3-27 14:34 作者: 側面左右 時間: 2025-3-27 20:02
https://doi.org/10.1007/978-3-322-87741-3diseases like the common cold or influenza that do not provide immunity upon infection. While the SIS model is too simplistic to explain the outbreak dynamics of complex infectious diseases, it is the only compartment model with an explicit analytical solution for the time course of its populations.作者: abreast 時間: 2025-3-27 22:45
Wichard Woyke Dr. phil.,Udo Steffensrizes infectious diseases that provide immunity upon infection. While the SIR model does not have an analytical solution for the time course of its populations, it has explicit analytical solutions for its maximum infectious population and for the final sizes of its susceptible and recovered populat作者: 取消 時間: 2025-3-28 04:20
Wichard Woyke Dr. phil.,Udo Steffensracterizes infectious diseases with a significant incubation period during which individuals have been infected, but are not yet infectious themselves. While the SEIR model does not have an analytical solution for the time course of its populations, it has explicit analytical solutions for the maxim作者: arthroplasty 時間: 2025-3-28 08:56
,Die Parteien — Tr?ger der Wahl,equations. Except for the SIS model, these equations have no analytical solution and we generally solve them numerically. Here we introduce the basic concepts of numerical methods for first order differential equations and illustrate explicit and implicit time integration schemes to solve them. To d作者: Firefly 時間: 2025-3-28 13:49
,Die Direktwahl des Europ?ischen Parlaments,ous diseases that provide immunity upon infection. Since the SIR model has no analytical solution for the time course of its populations, we discretize it in time using finite differences and adopt explicit and implicit time integration schemes to solve it. We compare the timeline of the SIR model t作者: 拖債 時間: 2025-3-28 14:38
Wahlen in der Bundesrepublik Deutschland, It characterizes infectious diseases that have a significant incubation period and provide immunity upon infection. Since the SEIR model has no analytical solution for the time course of its populations, we discretize it in time using finite differences and apply explicit and implicit time integrat作者: TSH582 時間: 2025-3-28 22:46
,Wahlforschung und W?hlerverhalten,S, E, Is , Ia and R. It characterizes infectious diseases with a significant group of individuals that remain asymptomatic upon infection, but can still infect others. Since the SEIIR model has no analytical solution for the time course of its populations, we discretize it in time using finite diffe作者: 斜谷 時間: 2025-3-29 02:34
Wahlen in der Bundesrepublik Deutschland,tial equations. These equations generally have no analytical solution and we have to solve them numerically. Here we introduce the basic concepts of numerical methods for partial differential equations and illustrate network diffusion and finite element methods to solve them. To demonstrate the feat作者: famine 時間: 2025-3-29 06:25
https://doi.org/10.1007/978-3-322-96040-5 I, and R, at each node of the network. It characterizes the spatio-temporal spreading of infectious diseases along the edges of the network proportional to human mobility. Since the network SEIR model has no analytical solution, we discretize it in space using a weighted Laplacian graph and apply e作者: FLIRT 時間: 2025-3-29 09:34
,M?glichkeiten und Grenzen von Wahlen,eality disease data are inherently stochastic; they are incomplete, include noise, and contain systemic uncertainty. Here we integrate data-driven modeling and computational epidemiology to explore disease data and compartment models using a probabilistic approach and quantify the uncertainties of o作者: Soliloquy 時間: 2025-3-29 11:24
,M?glichkeiten und Grenzen von Wahlen, is widely used by the public media, scientists, and political decision makers to explain and justify political strategies to control the COVID-19 pandemic. Here we explore the effectiveness of political interventions using the reproduction number of COVID-19 across Europe. We combine what we have l作者: 使增至最大 時間: 2025-3-29 17:21
Ingrid Gogolin,Harm Kuper,Jürgen Baumertequire the most urgent medical attention. In the more advanced stages, the interest shifts towards mildly symptomatic and asymptomatic individuals who–by definition–are difficult to trace and likely to retain normal social and travel patterns. In the case of COVID-19, early antibody seroprevalence s作者: helper-T-cells 時間: 2025-3-29 21:17
Wandel p?dagogischer Institutionenase, a critical question is to decide when and how to reopen the borders. Here we explore the impact of border reopening for the example of Newfoundland and Labrador, a Canadian province that has been under a strict travel ban and COVID-free for almost for almost two months. We combine what we have 作者: Morbid 時間: 2025-3-30 02:08
Ellen KuhlIncludes more than 400 examples, figures, and problems.Provides problem sets, both analytical and computational.Teaches students how to integrate data and physics-based modeling作者: eustachian-tube 時間: 2025-3-30 07:07
http://image.papertrans.cn/c/image/232277.jpg作者: agitate 時間: 2025-3-30 08:23 作者: Angiogenesis 時間: 2025-3-30 12:36 作者: 群島 時間: 2025-3-30 16:43
The classical SIR modelrizes infectious diseases that provide immunity upon infection. While the SIR model does not have an analytical solution for the time course of its populations, it has explicit analytical solutions for its maximum infectious population and for the final sizes of its susceptible and recovered populat作者: abnegate 時間: 2025-3-31 00:31
The classical SEIR modelracterizes infectious diseases with a significant incubation period during which individuals have been infected, but are not yet infectious themselves. While the SEIR model does not have an analytical solution for the time course of its populations, it has explicit analytical solutions for the maxim作者: palpitate 時間: 2025-3-31 02:03 作者: 團結 時間: 2025-3-31 09:07
The computational SIR modelous diseases that provide immunity upon infection. Since the SIR model has no analytical solution for the time course of its populations, we discretize it in time using finite differences and adopt explicit and implicit time integration schemes to solve it. We compare the timeline of the SIR model t作者: Nmda-Receptor 時間: 2025-3-31 10:26
The computational SEIR model It characterizes infectious diseases that have a significant incubation period and provide immunity upon infection. Since the SEIR model has no analytical solution for the time course of its populations, we discretize it in time using finite differences and apply explicit and implicit time integrat
