Tags: sensitivity

Resources (1-7 of 7)

  1. 2013-Fathalla_Rihan-Delay Differential Equations in Biosciences - Parameter estimation and sensitivity analysis

    07 Apr 2023 | Teaching Materials | Contributor(s):

    By Fathalla Rihan

    NA

    This is a review article to show that delay differential models have a richer mathematical framework (compared with models without memory or after-effects) and a better consistency with biological...

    https://qubeshub.org/publications/4097/?v=1

  2. 2011-Nakul-Chitnis-Introduction to Mathematical Epidemiology - Deterministic Compartmental Model

    07 Apr 2023 | Teaching Materials | Contributor(s):

    By Nakul Chitnis

    NA

    Deterministic compartmental models form the simplest models in the mathematical study of infectious disease dynamics. They assume that a population is homogenous (all people are the same) and the...

    https://qubeshub.org/publications/4063/?v=1

  3. 1977-Michael_Mackey-Leon_Glass-Oscillation and Chaos in Physiological Control Systems

    23 Mar 2023 | Teaching Materials | Contributor(s):

    By Michael Mackey1, Leon Glass1

    NA

    First-order nonlinear differential-delay equations describing physiological control systems are studied. The equations display a broad diversity of dynamical behavior including limit cycle...

    https://qubeshub.org/publications/3835/?v=1

  4. 2018-Weber_Theers_Surmann_Ligges_Weihs-Sensitivity Analysis_of_Ordinary_Differential_Equation_Models

    09 Mar 2023 | Teaching Materials | Contributor(s):

    By Frank Weber

    NA

    This report will focus on the sensitivity analysis of ordinary differential equation (ODE) models since they can be used to model so-called Low Frequency Oscillations (LFOs).

    https://qubeshub.org/publications/3750/?v=1

  5. 6-018-ExploringSIRModel-ModelingScenario

    17 May 2022 | Teaching Materials | Contributor(s):

    By Stanley Florkowski, Ryan Miller1

    United States Military Academy, West Point NY USA

    Students will transform, solve, and interpret Susceptible Infected Recovered (SIR) models using systems of differential equation models. The project is progressively divided into three parts to...

    https://qubeshub.org/publications/3097/?v=1

  6. Data from: Advancing population ecology with integral projection models: a practical guide

    23 Oct 2018 | Teaching Materials | Contributor(s):

    By Cory Merow, Johan Dahlgren, Jessica Metcalf, Dylan Childs, Margaret Evans, Eelke Jongejans, Sydne Record, Mark Rees, Roberto Salguero-Gómez, Sean McMahon

    Review important resources for building IPMs and provide a comprehensive guide, with extensive R code, for their construction.

    https://qubeshub.org/publications/853/?v=1

  7. Advancing population ecology with integral projection models: a practical guide

    20 Oct 2018 | Teaching Materials | Contributor(s):

    By Cory Merow1, Johan Dahlgren2, Jessica Metcalf3, Dylan Childs4, Margaret Evans5, Eelke Jongejans6, Sydne Record7, Mark Rees4, Roberto Salguero-Gómez8, Sean McMahon9

    1. Ecology and Evolutionary Biology, University of Connecticut, Storrs, CT, USA 2. Department of Ecology, Environment and Plant Sciences, Stockholm University, Stockholm, Sweden 3. Department of Zoology, Oxford University, Oxford, UK 4. Department of Animal and Plant Sciences, University of Sheffield, Sheffield, UK 5. Laboratory of Tree-Ring Research and Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, USA 6. Department of Animal Ecology and Ecophysiology, Institute for Water and Wetland Research, Radboud University Nijmegen, Nijmegen, The Netherlands 7. Harvard University, Harvard Forest, Petersham, MA, USA 8. Centre for Biodiversity and Conservation Science, University of Queensland, St Lucia, Qld, Australia 9. Smithsonian Environmental Research Center, Edgewater, MD, Edgewater, MD, USA

    Integral projection models (IPMs) use information on how an individual's state influences its vital rates – survival, growth and reproduction – to make population projections using regression...

    https://qubeshub.org/publications/824/?v=1