rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Computational Epidemiology
    sub title: Data-Driven Modeling of COVID-19
  name:
    name part: Kuhl, Ellen.
    role:
      role term: creator
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  genre: Electronic books.
  origin info:
    place:
      place term: sz
    date issued: 2021
    edition: 1st ed. 2021.
    issuance: monographic
  language:
    language term: eng
  physical description:
    reformatting quality: access
    form: remote
    extent: 1 online resource (XVI, 312 pages)
  abstract: 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.
  table of contents: 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.
  note: by Ellen Kuhl.
  subject:
    topic: Biomedical engineering
  subject:
    topic: Bioinformatics
  subject:
    topic: Epidemiology
  subject:
    topic: Mathematical models
  subject:
    topic: Biomedical Engineering and Bioengineering
  subject:
    topic: Bioinformatics
  subject:
    topic: Epidemiology
  subject:
    topic: Mathematical Modeling and Industrial Mathematics
  classification: R856-857
  classification: HC79.E5
  classification: GE220
  classification: 610.28
  related item:
    title info:
      title: Springer Nature eBook
  identifier: 9783030828905
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record creation date: 210922
    record change date: 20230330185355.3
    record identifier: in00004393537