The University of Georgia
Approved Course


  1. COURSE ID: EPID(ECOL)(IDIS) 8515

  2. TITLES

  3. Course Title: Modeling Infectious Diseases
    Course Computer Title: MODEL INF DISEASES
  4. COURSE DESCRIPTION (must be 50 words or less)

  5. Computational and mathematical methods for building and analyzing models of infectious diseases. Population-level processes for a range of infectious diseases of humans, wildlife, and livestock will be studied incorporating a variety of transmission mechanisms. Within-host processes will also be addressed.
  6. GRADING SYSTEM

  7. A-F (Traditional)
  8. CREDIT HOURS AND LECTURE/LAB/DISCUSSION HOURS

  9. FIXED VARIABLE
    Credit Hours 4
    Lecture Hours 4
  10. NON-TRADITIONAL FORMAT(if lecture/lab hours or lecture/discussion hours are fewer than credit hours, please justify)


  11. REPEAT POLICY

  12. Course cannot be repeated for credit
  13. DUPLICATE CREDIT STATEMENT(do not list quarter course IDs)

  14. The course will not be open to students who have credit in the following courses:
  15. REQUIRED PREREQUISITES

  16. EPID 7010 or ECOL 4000/6000 or permission of department
  17. PREREQUISITE OR COREQUISITE COURSES

  18. COREQUISITE COURSES

  19. PRIMARY DELIVERY MECHANISM (select only one):

  20. Lecture
  21. COURSE WILL BE OFFERED

  22. Every Year - Scheduling unknown
  23. EFFECTIVE SEMESTER AND YEAR OF CURRENT VERSION OF COURSE

  24. Spring 2014
  25. ADDITIONAL INFORMATION REQUIRED FOR THE SYLLABUS

  26. COURSE OBJECTIVES OR EXPECTED LEARNING OUTCOMES

    Knowledge
    Students will be exposed to the major processes involved in a 
    variety of infectious diseases. They will learn a broad range 
    of basic mathematical and computational methodologies for 
    modeling infectious diseases. They will be able to recognize 
    key parameters in a variety of models and give their meaning. 
    They will be able to derive some basic important results in the 
    theory of infectious disease transmission. 
    
    Comprehension
    Students will understand how key processes such as 
    transmission, recovery and waning immunity operate. They will 
    be able to articulate the steps required to formulate a 
    mathematical model. They will interpret biological problems as 
    simple mathematical and computational models. 
    
    Application
    Students will develop ideas and concepts introduced in lectures 
    to practical examples through instructional computer lab 
    sessions. They will be able to modify basic models to add more 
    realism or to apply it to a different biological problem.
    
    Analysis
    Students will learn to break down large-scale infectious 
    disease observations into component processes and will 
    understand how important concepts in infectious disease 
    modeling are manifested in real situations. They will be able 
    to correct basic computer programs so that they work as 
    intended. They will be able to distinguish between infectious 
    disease theory and inference from data. They will be able to 
    interpret the results of models in a biological context. 
    
    Synthesis
    Students will appreciate the progression of ideas that has led 
    to the modern field of infectious disease modeling. They will 
    be able to combine component processes, relevant to infectious 
    diseases, into single models that can explain infectious 
    disease progression in populations. Students will learn the 
    value of considering several infectious diseases together to 
    ask broader questions about why they affect populations 
    differently. 
    
    Evaluation
    Students will learn the usefulness and assumptions of different 
    types of mathematical and computational models in infectious 
    disease studies. They will also be able to critically read and 
    evaluate mathematical modeling studies of infectious diseases 
    that are published in primary research journals. Students will 
    be able to discriminate between good and bad models and will be 
    able to discern what type of data sets can best be used with 
    models to answer research questions.

    TOPICAL OUTLINE

    1.	Introduction to the topic 
    2.	Introduction to R 
    3.	Mathematical refresher
    4.	The basics: Simple compartmental models
    5.	Different forms of transmission (frequency-dependent & 
    density-dependent)
    6.	The latent period; acquired & waning immunity
    7.	Stochastic models
    8.	Within-host models 
    9.	Herd immunity & vaccination strategies
    10.	Parasite evolution  I: transmission-virulence trade-
    offs; increase of virulence due to vaccines
    11.	Parasite evolution II: emergence of drug resistance and 
    vaccine escape mutants
    12.	Critical community sizes & disease 'fade-outs'
    13.	Spatial models (the rescue effect, synchrony)
    14.	Network models applied to STDs
    15.	Regulation of wildlife by infectious diseases
    16.	Macro-parasite models
    17.	Vector-borne diseases
    18.	Fitting models to (and estimating parameters from) data 
    19.	Uncertainty and sensitivity analysis
    20.	Multi-pathogen/multi-host systems

    UNIVERSITY HONOR CODE AND ACADEMIC HONESTY POLICY

               UGA Student Honor Code: "I will be academically honest in all of my academic work and will not tolerate academic dishonesty of others." A Culture of Honesty, the University's policy and procedures for handling cases of suspected dishonesty, can be found at www.uga.edu/ovpi. Every course syllabus should include the instructor's expectations related to academic integrity.