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Computational epidemiology = data-dr...

Kuhl, Ellen.

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Computational epidemiology = data-driven modeling of COVID-19 /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Computational epidemiology/ by Ellen Kuhl.
Reminder of title:	data-driven modeling of COVID-19 /
Author:	Kuhl, Ellen.
Published:	Cham :Springer International Publishing : : 2021.,
Description:	xvi, 312 p. :ill., digital ;24 cm.
[NT 15003449]:	table of contents -- introduction -- infectious diseases -- a brief history of infectious diseases -- II. mathematical epidemiology -- introduction to compartment modeling -- compartment modeling of epidemiology -- concepts of endemic disease modeling -- data-driven modeling in epidemiology. - compartment modeling of COVID19 -- early outbreak dynamics of COVID-19 -- asymptomatic transmission of COVID-19 -- inferring outbreak dynamics of COVID-19 -- modeling outbreak control -- managing infectious diseases -- change-point modeling of COVID-19 -- dynamic compartment modeling of COVID-19 -- network modeling of epidemiology -- network modeling of epidemic processes -- network modeling of COVID-19 -- dynamic network modeling of COVID-19 -- informing political decision making through modeling -- exit strategies from lockdown -- vaccination strategies -- the second wave -- lessons learned.
Contained By:	Springer Nature eBook
Subject:	COVID-19 (Disease) - Epidemiology -
Online resource:	https://doi.org/10.1007/978-3-030-82890-5
ISBN:	9783030828905

Computational epidemiology = data-driven modeling of COVID-19 /
Kuhl, Ellen.

Computational epidemiologydata-driven modeling of COVID-19 /[electronic resource] :by Ellen Kuhl. - Cham :Springer International Publishing :2021. - xvi, 312 p. :ill., digital ;24 cm.

table of contents -- introduction -- infectious diseases -- a brief history of infectious diseases -- II. mathematical epidemiology -- introduction to compartment modeling -- compartment modeling of epidemiology -- concepts of endemic disease modeling -- data-driven modeling in epidemiology. - compartment modeling of COVID19 -- early outbreak dynamics of COVID-19 -- asymptomatic transmission of COVID-19 -- inferring outbreak dynamics of COVID-19 -- modeling outbreak control -- managing infectious diseases -- change-point modeling of COVID-19 -- dynamic compartment modeling of COVID-19 -- network modeling of epidemiology -- network modeling of epidemic processes -- network modeling of COVID-19 -- dynamic network modeling of COVID-19 -- informing political decision making through modeling -- exit strategies from lockdown -- vaccination strategies -- the second wave -- lessons learned.

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 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. If you are a student, educator, basic scientist, or medical researcher in the natural or social sciences, or someone passionate about big data and human health: This book is for you! It serves as a textbook for undergraduates and graduate students, and a monograph for researchers and scientists. It can be used in the mathematical life sciences suitable for courses in applied mathematics, biomedical engineering, biostatistics, computer science, data 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.

ISBN: 9783030828905

Standard No.: 10.1007/978-3-030-82890-5doiSubjects--Topical Terms:

3492877
COVID-19 (Disease)
--Epidemiology

LC Class. No.: RA644.C67 / K85 2021

Dewey Class. No.: 614.592414

Computational epidemiology = data-driven modeling of COVID-19 /
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505 0 $a table of contents -- introduction -- infectious diseases -- a brief history of infectious diseases -- II. mathematical epidemiology -- introduction to compartment modeling -- compartment modeling of epidemiology -- concepts of endemic disease modeling -- data-driven modeling in epidemiology. - compartment modeling of COVID19 -- early outbreak dynamics of COVID-19 -- asymptomatic transmission of COVID-19 -- inferring outbreak dynamics of COVID-19 -- modeling outbreak control -- managing infectious diseases -- change-point modeling of COVID-19 -- dynamic compartment modeling of COVID-19 -- network modeling of epidemiology -- network modeling of epidemic processes -- network modeling of COVID-19 -- dynamic network modeling of COVID-19 -- informing political decision making through modeling -- exit strategies from lockdown -- vaccination strategies -- the second wave -- lessons learned.
520 $a 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 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. If you are a student, educator, basic scientist, or medical researcher in the natural or social sciences, or someone passionate about big data and human health: This book is for you! It serves as a textbook for undergraduates and graduate students, and a monograph for researchers and scientists. It can be used in the mathematical life sciences suitable for courses in applied mathematics, biomedical engineering, biostatistics, computer science, data 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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