Assessment 1

Study Design

In this assessment, you will produce a 1,500‑word study design proposal that outlines a rigorous epidemiological approach to investigating a specified public health issue. You are expected to synthesise advanced epidemiological principles and critically select an appropriate study design (e.g., cross‑sectional, cohort, case–control, experimental) that aligns with the research question and population context. Your proposal should demonstrate a clear rationale for methodological decisions, integrating biostatistical considerations such as sampling strategies, measurement error, potential confounding, and risk estimation.

You will be required to articulate how your chosen design would be operationalised in practice, including identification of suitable data sources, approaches to ensuring data quality, and strategies for assessing determinants of health within the selected system. The assessment should also outline the analytical techniques required to generate robust, evidence‑based insights that can inform subsequent public health interventions. Clear justification using high‑quality literature, epidemiological theory, and relevant policy or surveillance frameworks is essential.

Rationale

This assessment is designed to cultivate advanced application and problem‑solving skills by requiring you to translate epidemiological concepts into a feasible research plan. It mirrors real‑world responsibilities of public health professionals who must design methodologically sound investigations to address complex and emerging challenges.

Assessment 2

Biostatistical Analysis

For this assessment, you will conduct a biostatistical analysis using appropriate digital tools and statistical software to interrogate a dataset relevant to a contemporary public health issue. You will produce a 1,000‑word analytical report that demonstrates your ability to acquire, manage, analyse, and interpret epidemiological data with precision and critical awareness.

Your report should include a clear explanation of the analytical techniques employed, such as descriptive statistics, measures of association, hypothesis testing, or regression modelling with justification for their suitability. You must critically interrogate the dataset to detect issues such as missing data, measurement bias, data quality limitations, and the implications these have for interpretation, including how this influences epidemiological decision‑making.

The final report should present findings using appropriate tables, visualisations, and narrative interpretation, with an emphasis on producing evidence‑informed insights that could support public health action.

Rationale

This assessment develops advanced digital literacy, analytical reasoning, and problem‑solving skills which are core competencies for epidemiologists and public health practitioners. It reflects professional expectations in data‑driven decision‑making, where practitioners must interpret complex datasets, evaluate their reliability, and communicate meaningful conclusions that inform policy, service planning, and health protection activity.

Epidemiological concepts, measures and evidences
Study designs: descriptive, analytical, experimental
Disease surveillance systems
Outbreak investigation and response
Measures of association and risk
Understanding Data
Sampling, cofounders and bias in epidemiology
Biostatistical methods and estimation
Investigating determinants of health and diseases
Health Outcomes and Measures – life expectancy, quality of life reports, PROM, quality of care
Data quality and management
Use of statistical software

1. Critically evaluate epidemiological principles, study designs, and biostatistical methods, demonstrating a systematic and advanced understanding of their application and implications within public health research and practice.

Programme Learning Outcome: Research Skills, Knowledge and Understanding

2. Apply epidemiological and statistical techniques to generate evidence-based solutions for particular public health problems.

Programme Learning Outcome: Application & Problem Solving

3. Critically apply digital tools and statistical software to acquire, manage, analyse, and interpret epidemiological data, demonstrating digital literacy and proficiency in interrogating public health research and generating evidence‑informed insights.

Programme Learning Outcome: Digital Literacy, Research Skills

4. Apply and critically evaluate data sources, surveillance systems and analytical outputs to identify accuracy issues, interpret limitations, and generate problem‑solving insights for epidemiological decision‑making.

Programme Learning Outcome: Application & Problem Solving

Lectures on key concepts will be delivered in-person on campus for both the full time and part-time cohorts while the distance learning cohorts will be delivered online. This will be complemented by online group discussions among the students to explore key issues and their relevance in practice. Interactive sessions and critical appraisal of data would be employed. You will also engage in independent, self-directed learning activities and drop-in support sessions where needed will be provided.

Webb, P., Bain, C. and Page, A., (2024). Essential Epidemiology. 5th ed. Cambridge: Cambridge University Press.

Miller, E., Begg, S. and Lee, P., (2025). Introduction to Epidemiology for the Health Sciences. 1st ed. Cambridge: Cambridge University Press.

Normand, S.-L.T. and Shahian, D.M., (2026). Healthcare Quality Measurement: History and Practice. Cambridge: Cambridge University Press.

Sullivan, L. M. (2023). Essentials of Biostatistics in Public Health (4th ed.). Jones & Bartlett Learning.

John, M., (2025). Biostatistics for Epidemiology and Public Health. London: MedPlus.

How do we transform data into decisions that save lives? In the Epidemiology & Biostatistics module, you will develop the analytical and practical skills essential for evidence-based public health. You’ll build a strong foundation in epidemiological principles, study designs (descriptive, analytical, experimental), disease surveillance, outbreak investigation, and biostatistical methods. Beyond theory, you will learn to manage data quality, sampling, confounding, and bias, while applying statistical techniques using to real-world datasets. This module equips you to critically evaluate data sources and surveillance systems, communicate findings clearly to policymakers and communities, and design strategies that address pressing health challenges. By mastering these tools, you will become a practitioner capable of turning complex data into actionable insights—shaping health outcomes locally and globally.