Quantitative Reasoning Foundation: Epidemics
Course Description
Course Description:
This is a foundation-level quantitative reasoning (QR) module. QR can be understood as a way of applying certain set of logic that allow us to work with numbers, using data to provide numerical evidence to answer questions and support arguments. The primary objective of this module is to allow students to develop necessary skills for the application of this logic, through the topic of epidemic. With the current COVID-19 pandemic, large amount of information and data becomes available and analyses of these data are crucial in helping policy makers in making the right decisions in a timely way.
Through discussions on various case studies, students will be exposed to the use of statistical analysis and mathematical modelling to supplement qualitative arguments. During the course, we will learn to appreciate the importance of analysis using both qualitative and quantitative approaches as a complement to each other, to help one develop a deeper and broader understanding of a problem.
Course Objectives:
Upon completion of this QRF module, students should be able to:
- Present data in different visualisation forms (tables, graphs, maps etc)
- Interpreting data using statistical analyses (e.g. descriptive statistics to study distribution.)
- Identify variables in a mathematical model leading to the construction of the model
- Build and analyse model using tools such as spreadsheets
Course Assessment
Course Assessments:
- Class participation: 10%
- Homework and tutorial: 20%
- Tests: 20%
- Group project: 30%
- Individual project: 20%
Course Outline
Course Outline (subject to change):
Week |
Content |
1 |
What is quantitative reasoning with data? |
2 |
How do we know that an Epidemic has occur? |
3 |
How are epidemiological studies carried out? |
4 |
How can we tell if two variables are associated? |
5 |
How can we tell the associations between two categorical variables? |
6 |
Case study: |
7 |
How can we predict what will happen in an epidemic? |
8 |
Extending SIR model to SEIR model. |
9 |
Introduction of Agent Based Modelling (ABM). |
10 |
How can additional parameters be included in ABM? |
11 |
Case study: |
12 |
Case study: |
13 |
Presentation of model |