5B012B - Digital Epidemiology and Health Analytics 01 Sep 2027 - 31 Aug 2033 | Version 0
Associated Module Information
| Module Code: | 5B012B | ||
|---|---|---|---|
| Module Title: | Digital Epidemiology and Health Analytics | ||
| Faculty: | Faculty of Computing, Engineering and Science | ||
| Faculty Group: | Applied Science | ||
| Faculty Sub Group: | Biology and Chemistry | ||
| Module Leader: | Martin Powell | ||
| Module Team: | Niamh Breslin | ||
| First Intended Intake: | SEP 2027 | Final Year of Intake: | 2032 |
| Date Closed: | |||
| Credit Value: | 30 | Credit Level: | 5 |
| Language: | English | ||
| Percentage of Module Taught in Welsh: | 33 | ||
| Equivalent Module: | |||
| HECOS codes: | 100265 - biomedical sciences | ||
| HECOS Code Weighting: | 100 | ||
Document Version Information
| Version | 0 |
|---|---|
| Valid From | 01 Sep 2027 |
| Valid To | 31 Aug 2033 |
Module Aims
Develop analytical competence in digital epidemiology by equipping students with the skills to manage, analyse, and interpret diverse public health datasets—including epidemiological, spatial, and genomic data—to identify patterns and determinants of disease.
Build critical evaluation skills for public health decision-making by enabling students to assess epidemiological evidence and modelling outputs to determine the effectiveness and implications of public health interventions.
Foster responsible and ethical use of health data by developing understanding of data governance, security, and ethical considerations in the application of digital and bioinformatic approaches to population health.
Content Summary
The Digital Epidemiology & Health Analytics module equips students with the skills to analyse and interpret complex public health data in a rapidly evolving digital landscape. Through a series of real-world challenges, students will work with epidemiological, spatial, and genomic datasets to explore how diseases spread, identify risk factors, and evaluate public health interventions.
The module introduces key analytical approaches, including statistical modelling, geographic information systems (GIS), and basic bioinformatics, while emphasising the importance of data quality, security, and ethical decision-making. Students will gain hands-on experience using tools such as R programming language and ArcGIS to generate insights and inform policy-relevant conclusions.
By integrating technical analysis with critical evaluation, this module supports the broader course aims of developing evidence-based practitioners who can apply data-driven approaches to improve population health outcomes and respond effectively to contemporary public health challenges.
Learning and Teaching Methods
| Activity Type | Hours |
|---|---|
| Practical classes and workshops | 56 |
| Digital Guided Learning | 10.5 |
| Independent Study? | 113.5 |
| Formative Assessment | 50 |
| Summative Assessment | 60 |
| Total Hours Selected | 290 |
Learning Outcomes
| # | Learning Outcome |
|---|---|
| LO1 | Analyse and interpret epidemiological, biomedical informatics and public health datasets using appropriate quantitative methods to identify patterns, associations, and potential determinants of disease in populations. |
| LO2 | Evaluate epidemiological evidence to assess the effectiveness of public health interventions and ensure equality, diversity and inclusion are embedded in healthcare policy. |
Module Requisites
N/A
Assessment Criteria
| Assessment Category | Assessment Type | Description | Duration | Word Count | Weight (%) | Best of? | Pass Mark |
|---|---|---|---|---|---|---|---|
| Asynchronous Assessment | Case study | Students will use generative AI to produce a patient-orientated infographic on a public health challenge or a diagnostic/therapeutic advance. Students will then evaluate the design of the infographic and how accurately it provides information. They will also design their own infographic outline the improvements they have made | 0 | 3000 | 50 | No | 40 |
| Asynchronous Assessment | Student Choice | Students will be assessed on their ability to communicate information to a scientific audience on a health issue related to the module content on a specific topic of their choice. They will need to introduce the scientific basis of the topic and why the topic is important (what need does it address, why is it innovative, what impact will it have on patient health and well-being). Students will also be expected to be innovative in their style of delivery dependent on their choice of digital output, with a focus being on clear communication. | 23 | N/A | 50 | No | 40 |
Assessment Matrix
| Assessment Type | Learning Outcomes | ||
|---|---|---|---|
| LO1 | LO2 | ||
| Case study | ✔ | ✔ | |
| Student Choice | ✔ | ✔ | |