School of Public Health and Community Medicine

Predictive Modelling in Public Health (PHCM9785)

image - Predictive Modelling in Public Health

5-day workshop, 9:00 am - 5:00 pm, 14-18 November 2016


Predictive modelling is a rapidly developing area in public health. Already widely applied in predictive studies of interventions such as vaccination, modelling is a key input to policy and planning decisions in public health. Understanding how trends in disease will unfold in future years helps policy makers evaluate and prepare for future priorities. The focus in this course is on building predictive models of disease trends in excel and on assessing the value of modelling results for policy. The course is run over 5 days in a workshop format, with additional activities and demonstrations posted online to reinforce concepts.

Credit points

This course is an elective course of the Master of Public Health, Master of International Public Health, Master of Health Management and Master of Infectious Diseases Intelligence programs, comprising 6 units of credit towards the total required for completion of the study program. It is also a designated methods course as part of the MPH Specialisation in Infectious Diseases Epidemiology and Control.


This course has no required pre-requisites but does rely on reasonable quantitative skills and familiarity with Microsoft Excel. If you have any concerns in relation to your suitability for the course, please contact the course convenor (A/Prof Wood) by email or phone.

Mode of study

This course is offered in workshop mode of 5 days duration during the Summer Semester.

Course aim

This course aims to provide students with skills in applying predictive models to disease risks in public health and in developing their understanding of the benefits and limitations of predictive modelling in this context.

Course Outcomes
  • Discuss and explain the value of modelling approaches in policy formulation and planning for disease prevention and control
  • Assess the suitability of a modelling approach to address policy questions in relation to disease prevention and control
  • Understand, design and construct single-cohort models for demographic and disease risk projections in Excel
  • Extend single-cohort models to whole of population models for projecting disease incidence through time
  • Implement disease interventions in projective models applied to case-studies from both communicable and non-communicable diseases
Learning and teaching rationale

This course focuses on developing your understanding, practical skills and critical thinking in relationship to predictive modelling in the context of public health. While lectures and course notes are an important component of the course, there is also an emphasis on skill development in terms of building and analysing models through structured tutorials and assessment. Finally, critical thinking in terms of the role of modelling in informing policy is an important component and one that the course aims to develop through discussions of published papers. From a professional perspective, the course will help students strengthen their quantitative skills and gain confidence in assessing the role of modelling in public health interventions. This is relevant both to policy roles in which models might inform decision making, and in more analytical roles where skills form the course could be applied in developing models.

Teaching strategies

PHCM9785 Predictive Modelling in Public Health is taught in short course form with additional materials provided online following the intensive workshop. Students must attend the workshop – PowerPoint slides and tutorial materials will also be provided via Moodle for reference. The course consists of lectures, practical tutorial sessions, paper discussions and guest lectures featuring applications of predictive modelling in public health. Assessment tasks are to be submitted online through Turnitin (via Moodle). Additional materials, including model-building demonstrations, will be provided online following the course.


Assessment Task 1 - Group Presentation
Weighting: 30%

Assessment Task 2 - Technical Report
Weighting: 30%

Assessment Task 3 - Report
Weighting: 40%

Readings and resources

Learning resources for this course consist of the following:

  1. Course notes and readings
  2. Lectures slides (posted in Moodle)
  3. Lecture recordings (available in Moodle)
  4. Excel-based tutorials (available in Moodle)
  5. Supplementary resources (video demonstrations available in Moodle)