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    Potential Scenario
    202

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    2009-Peter-Howard-Modeling With ODE
    This is a set of class notes rich in examples and ideas for modeling. There is some MatLab code in support of some of the activities.
    Potential Scenario
    148

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    51

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    2006-Loyd-Wodarz-Drug Resistance in Acute Viral Infections-Rhinovirus as a Case Study
    We develop an epidemiological model that can be used to address the spread of resistance at the population level, and a virus dynamics model that can be used to study the dynamics of virus over the time course of an individual’s infection.
    Potential Scenario
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    2019-Fred_Brauer-The_Final_Size_of_a_Serious_Epidemic
    In an epidemic of a serious disease, there is likely to be behavioral response that decreases the epidemic size considerably.
    Potential Scenario
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    2015-Just-Callender-Differential equation models of disease transmission
    The work give full analyses of standard epidemic model and then shows how to do it with differential equations and agent-based modeling. Then it compares the results.
    General Resource
    203

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    48

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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,
    Potential Scenario
    152

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    39

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    1992-Emelie_Kenney-Differential equations and the AIDS epidemic
    This paper describes a lecture for Calculus II students in which the Acquired Immune Deficiency Syndrome was used to motivate study of first-order linear differential equations and the derivative as a rate of change.
    Potential Scenario
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    2018-Akman-EtAl-Parameter Estimation in Ordinary Differential Equations Modeling via Particle Swarm Optimization
    We demonstrate Particle Swarm Optimization efficacy by showing that it outstrips evolutionary computing methods previously used to analyze an epidemic model.
    Potential Scenario
    199

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    64

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    2011-Brian_Winkel-Parameter Estimates in Differential Equation Models for Population Growth
    We estimate the parameters present in several differential equation models of population growth, specifically logistic growth models and multiple species competition models.
    Potential Scenario
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    57

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    2011-Radouane_Yafia-A Study of Differential Equations Modeling Malignant Tumor Cells in Competition with Immune System
    In this paper, we present a competition model of malignant tumor growth that includes the immune system response. The model considers two populations: immune system (effector cells) and population of tumor (tumor cells).
    Potential Scenario
    138

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    56

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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.
    Potential Scenario
    140

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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.
    Potential Scenario
    124

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    68

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    2001-Noymer-The transmission and Persistence of Urban Legends-Sociological Application of Age-Structures Epidemic Models
    This paper describes two related epidemic models of rumor transmission in an age-structured population. Rumors share with communicable disease certain basic aspects, which means that formal models of epidemics may be applied to the transmission of...
    Modeling Scenario
    549

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    279

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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.
    Potential Scenario
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    2012-Arvind_Kumar_Misra-A simple mathematical model for the spread of two political parties
    In this paper, a non-linear mathematical model for the spread of two political parties has been proposed and analyzed by using epidemiological approach.
    Potential Scenario
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    40

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    2008-Yang-EtAl-Differential Equation Model of HIV Infection of CD T-Cells with Delay 
    Abstract: An epidemic model of HIV infection of CD4+ T-cells with cure rate and delay is studied. We include a baseline ODE version of the model, and a differential-delay model with a discrete time delay.
    Potential Scenario
    170

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    48

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    2019-Lorelei_Koss-SIR_Models_Differential_Equations_that_Support_The_Common_Good
    This article surveys how SIR models have been extended beyond investigations of biologically infectious diseases to other topics that contribute to social inequality and environmental concerns.
    Potential Scenario
    280

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    2009-Munz-EtAl-When Zombies Attack-Mathematical modelling of an Outbreak of Zombie Infection
    We introduce a basic model for zombie infection, determine equilibria and their stability, and illustrate the outcome with numerical solutions.
    Article or Presentation
    184

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    2011-Brian_Winkel-Parameter_Estimates_in_Differential_Equation_Models_for_Population_Growth
    We estimate the parameters present in several differential equation models of population growth, specifically single species exponential and logistic growth, and multiple species competition and predation models.
    Potential Scenario
    220

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    60

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    2014-Bozkurt-Peker-Mathematical modelling of HIV epidemic and stability analysis
    A nonlinear mathematical model of differential equations with piecewise constant arguments is proposed. This model is analyzed by using the theory of both differential and difference equations to show the spread of HIV in a homogeneous population.
    Free Online Textbook
    166

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    2014-Andre_De Ross-Modeling Population Dynamics
    This course is intended as an introduction to the formulation, analysis and application of mathematical models that describe the dynamics of biological populations.