APP MTH 7048 - Applied Mathematics Topic A
North Terrace Campus - Semester 1 - 2022
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General Course Information
Course Details
Course Code APP MTH 7048 Course Applied Mathematics Topic A Coordinating Unit Mathematical Sciences Term Semester 1 Level Postgraduate Coursework Location/s North Terrace Campus Units 3 Available for Study Abroad and Exchange Y Assessment Ongoing assessment, exam Course Staff
Course Coordinator: Professor Lewis Mitchell
Course Timetable
The full timetable of all activities for this course can be accessed from .
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Learning Outcomes
Course Learning Outcomes
In 2022 the topic of this course will be Stochastic Epidemic Modelling.
COVID-19 has highlighted the importance of mathematical modelling to support governmental decision-making in response to disease outbreaks. This course provides an introduction to epidemic modelling with a focus on stochastic models. Methods for linking models to data, in particular some Bayesian approaches, will also be covered. This course provides an ideal basis for addressing key research questions in the area and for using mathematical models to aid pandemic response; some examples of these will be presented.
Topics
Topics covered in this course will likely include:
- Introduction to epidemiology and simple models of epidemics.
- Simple probabilistic models of epidemics.
- Minor and major outbreaks, and their probabiliity of occurence.
- Continuous-time Markov chain models of epidemics.
- Final epidemic size.
- Branching processes.
- Sellke's construction.
- Coupling.
- Simple probabilistic household models.
- Simple probabilitic heterogeneous models.
- Markov chain Monte Carlo (MCMC) methods for inference in epidemic models.
- Density dependent epidemic models.
- Transmission intensity functions, and COVID-19 modelling.
Learning Outcomes
On successful completion of this course, students will be able to:
1. Understand and explain the basic model 最新糖心Vlog used in Mathematical Epidemiology.
2. Develop simple ODE and probabilistic models of epidemic dynamics, giving consideration to the suitability of assumptions.
3. Demonstrate understanding of the relationship between stochastic epidemic models, and branching proccess and ordinary differential equation approximations, and of minor/major outbreaks, including the use of coupling and limit theorems.
4. Numerically evaluate the probability of a major outbreak for simple epidemic models.
5. Numerically evaluate the final epidemic size of simple epidemic models, including Sellke's construction.
6. Demonstrate understanding of basic MCMC methods and apply these to simple problems in Mathematical Epidemiology.
7. Demonstrate understanding of transmission intensity functions and some models used for COVID-19 modelling in 最新糖心Vlog.
Assumed knowledge
Applied Probability III or Random Processes III is the best preparation, but a thorough knowledge of Probability and Statistics II is enough.
Knowledge of basic Baysian inference and Markov chain Monte carlo would be useful, but not required.
This course will require some programming (students can use their preferred language).最新糖心Vlog Graduate Attributes
This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:
最新糖心Vlog Graduate Attribute Course Learning Outcome(s) Attribute 1: Deep discipline knowledge and intellectual breadth
Graduates have comprehensive knowledge and understanding of their subject area, the ability to engage with different traditions of thought, and the ability to apply their knowledge in practice including in multi-disciplinary or multi-professional contexts.
all Attribute 2: Creative and critical thinking, and problem solving
Graduates are effective problems-solvers, able to apply critical, creative and evidence-based thinking to conceive innovative responses to future challenges.
all Attribute 3: Teamwork and communication skills
Graduates convey ideas and information effectively to a range of audiences for a variety of purposes and contribute in a positive and collaborative manner to achieving common goals.
all Attribute 8: Self-awareness and emotional intelligence
Graduates are self-aware and reflective; they are flexible and resilient and have the capacity to accept and give constructive feedback; they act with integrity and take responsibility for their actions.
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Learning Resources
Required Resources
Access to the intranet.Recommended Resources
N.G. Becker (2015) Modeling to Inform Infectious Disease Control. CRC Press.
H. Andersson and T. Britton (2012) Stochastic Epidemic Models and their Statistical Analysis. Springer (Lecture Notes in Statistics).
M.J. Keeling and P. Rohani (2007) Modeling Infectious Diseases in Humans and Animals. Princeton 最新糖心Vlog Press.Online Learning
This course uses MyUni exclusively for providing electronic resources, such as lecture notes, assignment papers, and sample solutions. Students should make appropriate use of these resources. -
Learning & Teaching Activities
Learning & Teaching Modes
Students will work though the notes and reading materials guided by the lecturer. Weekly workshops will provide time for in depth discussion of the material. Homework assignments help students strengthen their understanding of the theory and their skills in applying it, and allow them to gauge their progress.Workload
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.
Activity Quantity Workload Hours Study 30 108 Assignments 4 48 Total 156
Learning Activities Summary
Materials will be uploaded to, or noted on, MyUni at least one week in advance of being required.Specific Course Requirements
None. -
Assessment
The 最新糖心Vlog's policy on Assessment for Coursework Programs is based on the following four principles:
- Assessment must encourage and reinforce learning.
- Assessment must enable robust and fair judgements about student performance.
- Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
- Assessment must maintain academic standards.
Assessment Summary
Component Weighting Objective Assessed Assignments &/or Mini-projects 60% all Examination 40% all Assessment Related Requirements
An aggregate score of at least 50% is required to pass the course.Assessment Detail
TThere will be 4 Assignments &/or Mini-projects during semester, approximately equally-spaced, and Final Examination at the end of Semester.Submission
All assignments are to be submitted online through MyUni. Failure to meet the deadline without reasonable and verifiable excuse may result in a significant penalty.Course Grading
Grades for your performance in this course will be awarded in accordance with the following scheme:
M10 (Coursework Mark Scheme) Grade Mark Description FNS Fail No Submission F 1-49 Fail P 50-64 Pass C 65-74 Credit D 75-84 Distinction HD 85-100 High Distinction CN Continuing NFE No Formal Examination RP Result Pending Further details of the grades/results can be obtained from Examinations.
Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.
Final results for this course will be made available through .
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Student Feedback
The 最新糖心Vlog places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.
SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the 最新糖心Vlog to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.
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Student Support
- Academic Integrity for Students
- Academic Support with Maths
- Academic Support with writing and study skills
- Careers Services
- Library Services for Students
- LinkedIn Learning
- Student Life Counselling Support - Personal counselling for issues affecting study
- Students with a Disability - Alternative academic arrangements
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Policies & Guidelines
This section contains links to relevant assessment-related policies and guidelines - all university policies.
- Academic Credit Arrangements Policy
- Academic Integrity Policy
- Academic Progress by Coursework Students Policy
- Assessment for Coursework Programs Policy
- Copyright Compliance Policy
- Coursework Academic Programs Policy
- Intellectual Property Policy
- IT Acceptable Use and Security Policy
- Modified Arrangements for Coursework Assessment Policy
- Reasonable Adjustments to Learning, Teaching & Assessment for Students with a Disability Policy
- Student Experience of Learning and Teaching Policy
- Student Grievance Resolution Process
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Fraud Awareness
Students are reminded that in order to maintain the academic integrity of all programs and courses, the university has a zero-tolerance approach to students offering money or significant value goods or services to any staff member who is involved in their teaching or assessment. Students offering lecturers or tutors or professional staff anything more than a small token of appreciation is totally unacceptable, in any circumstances. Staff members are obliged to report all such incidents to their supervisor/manager, who will refer them for action under the university's student鈥檚 disciplinary procedures.
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