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    Potential Scenario
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    2008-Abramson-Mathematical modeling of the spread of infectious diseases
    These are informal notes, mostly based on the bibliography listed at the end and on recent papers in the field. The practical use of these models is based on the fact that they can be kept realistic enough.
    Modeling Scenario
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    1-022-SpreadOfTechnologies-ModelingScenario
    We examine plots on the spread of technologies and ask students to estimate and extract data from the plots and then model several of these spread of technologies phenomena with a logistic differential equation model.
    Modeling Scenario
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    1-104A-T-InfectionRisk-ModelingScenario
    This project is designed to examine differences between the exponential and logistic growth models in biology and how to apply these models in solving epidemic questions and comparing to actual disease data sets.
    Modeling Scenario
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    6-004-VillageEpidemic-ModelingScenario
    Students are offered data from a plague epidemic that occurred in the middle of the seventeenth century in Eyam, a small English village. With only two assumptions offered to students they are to build a mathematical model.
    Potential Scenario
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    2014-Do-Lee-A Differential Equation Model for the Dynamics of Youth Gambling
    We examine the dynamics of gambling among young people aged 16–24 years, how prevalence rates of at-risk gambling and problem gambling change as adolescents enter young adulthood, and prevention and control strategies.
    Potential Scenario
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    2001-Smith-Moore-The SIR Model for Spread of Disease
    A multiple part tour of SIR Models freely available with Maple, Mathematics, and MatLab support files at MAA site.
    Modeling Scenario
    271

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    110

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    1-104-InfectionRisk-ModelingScenario
    This project is designed to examine differences between the exponential and logistic growth models in biology and how to apply these models in solving epidemic questions.
    Modeling Scenario
    286

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    408

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    6-016-PandemicModeling-ModelingScenario
    The recent coronavirus outbreak has infected millions of people worldwide and spread to over 200 countries. How can we use differential equations to study the spread of coronavirus?
    Modeling Scenario
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    6-018-ExploringSIRModel-ModelingScenario
    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 understand, to apply, and to develop SIR models.
    Potential Scenario
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    1975-DH_Griffel-Teaching the formation and solution of differential equations
    This paper raises many interesting questions about teaching the formation (and solution) of differential equations, i.e. modeling with differential equations.
    Modeling Scenario
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    6-019-EnablingEpidemicExploration-ModelingScenario
    We became aware of several interesting possibilities for a modeling opportunity with data and we invited you to explore the several routes to parameter estimation in a SIR model with respect to the data offered.
    Modeling Scenario
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    6-003-SchoolFluEpidemic-ModelingScenario
    We offer a model of the spread of flu in a school dormitory and are asked to find when the flu levels reach their peak and explain long term behavior of the spread of the flu.
    Modeling Scenario
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    1-038-Ebola-ModelingScenario
    Students will use data published by the World Health Organization to model the 2014 outbreak of the Ebola virus in West Africa. We begin with a simple exponential growth model and move through the modeling process to the logistic growth model.
    General Resource
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    2003-Knorrenschild-Gross-Text Books on Mathematical Modeling in Biology
    Text Books on Mathematical Modeling in Biology Compiled from the Internet by Michael Knorrenschild,