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