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008			210922s2021 sz | o |||| 0|eng d
020			_a9783030828905
024	7		_a10.1007/978-3-030-82890-5 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aRA644.C67 _b2021 EB
100	1		_aKuhl, Ellen _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9681480
245	1	0	_aComputational Epidemiology : _bData-Driven Modeling of COVID-19 _cby Ellen Kuhl
250			_aFirst edition 2021
264		1	_aCham _bSpringer International Publishing _c2021
300			_a1 recurso en línea (XVI, 312 páginas)
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aArchivo de texto _bPDF
490	0		_aBiomedical and Life Sciences (SpringerNature-11642)
490	0		_aBiomedical and Life Sciences (R0) (SpringerNature-43708)
505	0		_atable 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	3		_aThis 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.
988			_aSpringer_BiomedLife_2021
650		7	_2embne _aCOVID-19 _9683668
776	0	8	_iPrinted edition: _z9783030828899
776	0	8	_iPrinted edition: _z9783030828912
776	0	8	_iPrinted edition: _z9783030828929
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-82890-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2022 _dz _eb _zSI