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    Modeling Scenario
    300

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    234

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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.
    data analysistechnologylogisticspreadinnovation
    Modeling Scenario
    192

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    145

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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.
    SIR modelsepidemicsusceptibleinfectiveremovedEyamEnglish village
    Modeling Scenario
    223

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    88

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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.
    infectionlogisticexponentialcovidcarrying capacity
    Modeling Scenario
    168

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    6-010-SocialCampaign-ModelingScenario
    The epidemic modeling problem is formulated as a system of three nonlinear, first order differential equations in which three compartments (S, I, and R) of the population are linked.
    diseasegrowth rateJudgment and Decision MakingSIR modelinfections diseasesocial campaignrecovery ratedelay timejoining processquitting processexponential distribution
    Modeling Scenario
    268

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    367

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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?
    population dynamicsinfectious diseasediseaseEbolaeSIR modelsCOVID19pandemicepidemicreproduction number
    Modeling Scenario
    202

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    155

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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.
    population dynamicssensitivitydiseaseSIR modelsepidemicrumors
    Modeling Scenario
    590

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    140

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    5-026-Evictions-ModelingScenario
    In this project, students develop two SIS models to study eviction trends in a population of non-homeowner households using an actual eviction rate. Students can calculate solutions, sketch the phase portrait, and determine long-term trends .
    carrying capacitycompartmentevictionhomeownerSIS modelhomelessness
    Modeling Scenario
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    229

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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.
    Akaike Information CriterionMichaelis-Mentenepidemicsaturationgradientleast sum of square errors
    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.
    infectionepidemicSIR modelflupeakschool children
    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.
    population dynamicsdata analysisexponential growthEbolalogisticepidemic