001439889 000__ 03873cam\a2200505\i\4500
001439889 001__ 1439889
001439889 003__ OCoLC
001439889 005__ 20230309004528.0
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001439889 019__ $$a1272995903$$a1273974143$$a1284934104
001439889 020__ $$a9783030828905$$q(electronic bk.)
001439889 020__ $$a3030828905$$q(electronic bk.)
001439889 020__ $$z9783030828899
001439889 020__ $$z3030828891
001439889 0247_ $$a10.1007/978-3-030-82890-5$$2doi
001439889 035__ $$aSP(OCoLC)1269055540
001439889 040__ $$aYDX$$beng$$erda$$epn$$cYDX$$dGW5XE$$dEBLCP$$dN$T$$dUKAHL$$dOCLCO$$dOCLCQ$$dOCLCO$$dOCLCQ
001439889 049__ $$aISEA
001439889 050_4 $$aRA644.C67$$bK85 2021
001439889 08204 $$a614.5/92414$$223
001439889 1001_ $$aKuhl, Ellen,$$d1971-$$eauthor.
001439889 24510 $$aComputational epidemiology :$$bdata-driven modeling of COVID-19 /$$cEllen Kuhl.
001439889 264_1 $$aCham :$$bSpringer,$$c[2021]
001439889 264_4 $$c©2021
001439889 300__ $$a1 online resource
001439889 336__ $$atext$$btxt$$2rdacontent
001439889 337__ $$acomputer$$bc$$2rdamedia
001439889 338__ $$aonline resource$$bcr$$2rdacarrier
001439889 504__ $$aIncludes bibliographical references and index.
001439889 5050_ $$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.
001439889 506__ $$aAccess limited to authorized users.
001439889 520__ $$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.
001439889 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed October 11, 2021).
001439889 650_0 $$aCOVID-19 (Disease)$$xEpidemiology$$xMathematical models.
001439889 650_6 $$aCOVID-19$$xÉpidémiologie$$xModèles mathématiques.
001439889 655_0 $$aElectronic books.
001439889 77608 $$iPrint version:$$z3030828891$$z9783030828899$$w(OCoLC)1259050347
001439889 852__ $$bebk
001439889 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-82890-5$$zOnline Access$$91397441.1
001439889 909CO $$ooai:library.usi.edu:1439889$$pGLOBAL_SET
001439889 980__ $$aBIB
001439889 980__ $$aEBOOK
001439889 982__ $$aEbook
001439889 983__ $$aOnline
001439889 994__ $$a92$$bISE