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Students in Differential Equations and Epidemiology Model a Campus Outbreak of pH1N1

    Authors: Meredith L. Greer1,*, Karen A. Palin2
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    Affiliations: 1: Departments of Mathematics and; 2: Biology, Bates College, Lewiston, ME 04240
    AUTHOR AND ARTICLE INFORMATION AUTHOR AND ARTICLE INFORMATION
    • Published 03 December 2012
    • *Corresponding author. Mailing address: Department of Mathematics, 2 Andrews Road, Bates College, Lewiston, ME 04240. Phone: 207-786-6283. Fax: 207-753-6949. E-mail: mgreer@bates.edu.
    • Copyright © 2012 American Society for Microbiology
    Source: J. Microbiol. Biol. Educ. December 2012 vol. 13 no. 2 183-185. doi:10.1128/jmbe.v13.i2.429
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    Abstract:

    We describe a semester-long collaboration between a mathematics class and a biology class. Students worked together to understand and model the trajectory of the pandemic H1N1, pH1N1, outbreak across campus in fall 2009. Each course had about 30 students and was an upper-level elective for majors. Some mathematics students had taken no college-level biology, and some biology students had taken no college-level mathematics. All students had taken at least three quantitative courses, so they had some experience working with data. Our goals were to allow students to work with and model a real data set that affected them personally, to explore how the outbreak spread within our small campus, and for students to share their areas of expertise. This project created opportunities for synthesis and evaluation.

Key Concept Ranking

Influenza A virus
0.6666667
Infection
0.5017167
Influenza
0.47712126
0.6666667

References & Citations

1. Anderson LW, Krathwohl DR, Airasian PW, Cruikshank KA, Mayer RE, Pintrich PR, et al 2000 A taxonomy for learning, teaching, and assessing: a revision of Bloom’s taxonomy of educational objectives, abridged ed Allyn & Bacon Boston, MA
2. Brauer F, Castillo-Chávez C 2000 Mathematical models in population biology and epidemiology 281 287 Springer New York, NY
3. Kermack WO, McKendrick AG 1927 A contribution to the mathematical theory of epidemics Proc R Soc Lond 115 700 721 10.1098/rspa.1927.0118 http://dx.doi.org/10.1098/rspa.1927.0118
4. Palin K, Greer ML 2012 The effect of mixing events on the dynamics of pH1N1 outbreaks at small residential colleges J Am Coll Health 60 485 489 [Online.] Available from: http://www.tandfonline.com/doi/abs/10.1080/07448481.2012.696294. 10.1080/07448481.2012.696294 22857142 http://dx.doi.org/10.1080/07448481.2012.696294
5. Riegelman RK, Albertine S, Persily NA 2007 The educated citizen and public health: a consensus report on public health and undergraduate education Council of Colleges of Arts & Sciences Williamsburg, VA
6. Steen LA 2005 Math & Bio 2010: linking undergraduate disciplines The Mathematical Association of America Washington, DC
7. Stroup DF, Thacker SB 2007 Epidemiology and education: using public health for teaching mathematics and science Public Health Rep 122 283 291 17518299
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2012-12-03
2017-05-25

Abstract:

We describe a semester-long collaboration between a mathematics class and a biology class. Students worked together to understand and model the trajectory of the pandemic H1N1, pH1N1, outbreak across campus in fall 2009. Each course had about 30 students and was an upper-level elective for majors. Some mathematics students had taken no college-level biology, and some biology students had taken no college-level mathematics. All students had taken at least three quantitative courses, so they had some experience working with data. Our goals were to allow students to work with and model a real data set that affected them personally, to explore how the outbreak spread within our small campus, and for students to share their areas of expertise. This project created opportunities for synthesis and evaluation.

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FIGURE 1

A compartmental diagram for a standard SIR (Susceptible-Infected-Removed) model. Each individual in a population is in exactly one compartment (S, I, or R) at any given time. Arrows indicate movement is permitted between compartments. The parameter β governs likelihood of infection, and the parameter γ relates to length of time for an infectious individual to recover. This model can also be represented as a system of three differential equations, quantifying the changes over time in each of the three compartments S, I, and R.

Source: J. Microbiol. Biol. Educ. December 2012 vol. 13 no. 2 183-185. doi:10.1128/jmbe.v13.i2.429
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