PUBH5300: Semester 2, 2023

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Units PUBH5300 Semester 2 2023 [Online]

Unit of study_

PUBH5300: Infectious Disease Epidemiology

Semester 2, 2023 [Online] - Camperdown/Darlington, Sydney

Overview

The suite of epidemiological practices and methods unique to infectious diseases comprises a critical toolkit that is urgently needed by epidemiologists in our current pandemic era. This unit will provide students with a firm understanding of infectious disease processes, modes of transmission, and transmission dynamics in populations of diverse demographic characteristics. Students will learn a standardised framework of infectious disease epidemiology to understand how pathogens move through populations and from which we can derive key parameters such as the basic reproduction number, epidemic growth, epidemic thresholds, and herd immunity thresholds. We will also incorporate aspects of networks and ecology to understand the ways in which contacts, and other forms of interaction, between individuals or between individuals and vectors influence transmission dynamics. Finally, we will explore the ways in which various public health interventions can be used to arrest infection transmission within populations and how to monitor the effects of such interventions.

Unit details and rules

Unit code	PUBH5300
Academic unit	Public Health
Credit points	6
Prohibitions ?	None
Prerequisites ?	None
Corequisites ?	None
Assumed knowledge ?	A basic understanding of introductory statistics and generalised linear regression (as would be attained through a unit such as PUBH5217 or equivalent, or through equivalent experience). No previous coding experience is required or assumed
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Michael Walsh, michael.walsh1@sydney.edu.au
Lecturer(s)	Michael Walsh, michael.walsh1@sydney.edu.au

Type	Description	Weight	Due	Length
Assignment	Analytic Exercise 3 Problem set computation and interpretation	30%	Formal exam period Due date: 13 Nov 2023 at 23:59	~4 pages
Outcomes assessed:
Participation	Class participation Class participation	5%	Ongoing	12 weeks
Outcomes assessed:
Small test	Midterm test Multiple choice small test	5%	Week 08 Due date: 21 Sep 2023 at 23:59	1.5 hours
Outcomes assessed:
Assignment	Analytic Exercise 1 Problem set computation and interpretation	30%	Week 10 Due date: 13 Oct 2023 at 23:59	~4 pages
Outcomes assessed:
Assignment	Analytic Exercise 2 Problem set computation and interpretation	30%	Week 12 Due date: 27 Oct 2023 at 23:59	~4 pages
Outcomes assessed:

Assessment summary

The midterm test is a multiple choice test designed to assess key topics underlying the applied problem set assessments. It is worth 5% of the total mark.
Problem sets 1-3 are each worth 30% of the total mark, and each invloves computation, interpretation, and evaluation of infectioud disease epidemiology data.
Classes invlove extenisve discussion, review, and practise of specific epidemiology methods and therefore 5% of the total mark will be based on student participation in class.

Assessment criteria

Result name	Mark range	Description
High distinction	85 – 100	Demonstrated the learning outcomes for the unit at an exceptional standard
Distinction	75 – 84	Demonstrated the learning outcomes for the unit at a very high standard
Credit	65 – 74	Demonstrated the learning outcomes for the unit at a good standard
Pass	50 – 64	Demonstrated the learning outcomes for the unit at an acceptable standard
Fail	0 – 49	The learning outcomes of the unit were not met to a satisfactory standard.

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

Students are required to be present for the midterm test (Assessment 1) on the scheduled date. Failure to do so will result in a 0 for this test. Failure to complete the problem sets (Assessments 2-4) on their respective due dates will result in a loss of 5% per day, unless the student has received prior written permission through the Simple Extension or Special Consideration system for a late submission.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Weekly schedule

WK	Topic	Learning activity
Week 01	Introduction to infectious disease epidemiology	Lecture and tutorial (3 hr)
Week 02	Exploration of infectious organisms	Lecture and tutorial (3 hr)
Week 03	Application of pathogen surveillance mechanisms and technologies	Lecture and tutorial (3 hr)
Week 04	Applied outbreak investigation	Lecture and tutorial (3 hr)
Week 05	Measuring spatiotemporal dependencies and disease clusters	Lecture and tutorial (3 hr)
Week 06	Epidemiological models of infection dynamics, part 1	Lecture and tutorial (3 hr)
Week 07	Epidemiological models of infection dynamics, part 2	Lecture and tutorial (3 hr)
Week 08	Network analysis in epidemiology	Lecture and tutorial (3 hr)
Week 09	Niche modelling: combing epidemiology and ecology to understand zoonoses and vector-borne diseases	Lecture and tutorial (3 hr)
Week 10	Vaccines and non-pharmaceutical interventions as foundational interventions for infectious disease processes	Lecture and tutorial (3 hr)
Week 11	Pandemic case study	Lecture and tutorial (3 hr)

Attendance and class requirements

Although, this unit is online delivery with all content is recorded, we will have six "live" sessions via Zoom. These sessions are all focused on data analyses so it is strongly recommended that these be attended so you can work through the analyses with the unit coordinator live and ask questions in context.

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. Describe and evaluate infectious disease outbreak investigations, modes of transmission, and transmission dynamics in diverse populations
LO2. Apply standard protocols for outbreak investigation in clinical and public health settings
LO3. Analyse and identify spatial and temporal patterns of transmission
LO4. Use infectious disease models to predict future infections, recoveries, and other public health outcomes
LO5. Calculate and interpret measures of pathogen transmission derived from epidemiological models
LO6. Evaluate the effectiveness of public health interventions to reduce or prevent outbreaks
LO7. Analyse and map outbreak risk by way of the interactions between humans, animals, and their shared environments

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

No changes have been made since this unit was last offered.

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

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