Outline

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

Academic unit	Public Health
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	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

Assessment

The census date for this unit availability is 1 September 2025

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Data analysis	Analytic Exercise 3 Problem set computation and interpretation	30%	Formal exam period Due date: 23 Nov 2025 at 23:59 Closing date: 30 Nov 2025	~4 pages	AI allowed
Outcomes assessed:
Contribution	Class participation Class participation	5%	Ongoing	12 weeks	AI allowed
Outcomes assessed:
Out-of-class quiz	Midterm test Multiple choice small test	5%	Week 09 Due date: 10 Oct 2025 at 12:00 Closing date: 10 Oct 2025	1.5 hours	AI allowed
Outcomes assessed:
Data analysis	Analytic Exercise 1 Problem set computation and interpretation	30%	Week 11 Due date: 26 Oct 2025 at 23:59 Closing date: 02 Nov 2025	~4 pages	AI allowed
Outcomes assessed:
Data analysis	Analytic Exercise 2 Problem set computation and interpretation	30%	Week 13 Due date: 09 Nov 2025 at 23:59 Closing date: 16 Nov 2025	~4 pages	AI allowed
Outcomes assessed:

Assessment summary

The midterm test is a multiple choice test designed to assess key topics underlying the applied analytic exercise assessments. It is worth 5% of the total mark.
Analytic Exercises 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.
Although generative artificial intelligence (AI) is allowed for your assessments, it is imperative that you keep the following items in mind. The concepts addressed and evaluated in the assessments are drawn directly from the material presented and discussed in lectures and tutorials. Some of the problems we cover in this class may not necessarily have definitive solutions. There may be topics that are as yet unresolved in the field. Moreover, although exciting and engaging, many of the concepts will be new to students. As such, this unit presents what the unit coordinator has determined represents the best evidence base as it is currently understood, as well as the methods that employ best practice as that is currently understood and accepted. There are many sources an AI system can draw from to answer a question or generate a statement (many of which can be very wrong!) that would be contrary to what we cover in class. As such, the unit coordinator will spend a good deal of time in class throughout the semester to provide specific guidance with respect to individual components of assessments as we cover specific topics and relate the topics to those assessments. With respect to data analysis in this unit, there are often many paths from point A to point B for any given research question and its attedant analysis in R. For those of you who are experienced users, you are free to travel along the analysis path from A to B however you wish as long as you provide the correct result, the correct interpretation of that result, and the R code by which you came to the result. For those of you who are not experienced R users, not to worry! The unit coordinator will provide a clear path for you with R scripts that are clearly annotated and these will be worked through in the tutorials so that you understand the analytic process. When it comes time for the assessment, you will then similarly have a clear path to your analyses if you follow the format we have outlined in the tutorials. However, if you are not experienced with R and you attempt to derive answers to analysis problems using feedback from an AI system, you will not have the nuanced understanding to evaluate what the code is doing behind the scenes and it can be difficult to determine if you have the correct answer. Sometimes, the AI will suggest analyses using packages that will have conflicts with the packages you already have installed, or packages that may be deprecated and thus are no longer available or are incompatible with other packages. In general, and as a corollary to the above points, it is strongly recommended that generative AI, if it is used, only be used to aid with grammar and spelling rather than as a tool for developing core concepts or doing data analyses. Finally, please also be aware that using generative AI comes with substantial environmental, ecological, and human costs. The carbon footprint of generative AI is massive and does not represent sustainable technological development in its current forms.

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.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

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 Analytic Exercises (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 University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

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.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

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

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

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

On this page

Useful links

PUBH5300: Infectious Disease Epidemiology

Semester 2, 2025 [Online] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

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

On this page

Useful links