In this course, you will develop a working understanding of the core concepts and methods that drive epidemiological research. From measuring how often diseases occur to evaluating whether an exposure truly causes harm, each lesson builds your ability to think critically about population health data, design and appraise studies, and communicate findings in clear, quantitative terms. By the end of the course you will be equipped to describe the distribution and determinants of health issues and to assess the strengths and limitations of the evidence behind them.
Select a lesson below to begin. Each module includes interactive content, knowledge checks, reflections, and a final assessment.
1
Introduction & Causal Concepts
Foundations of epidemiological thinking, causal inference, and the counterfactual framework.
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2
Sampling
Sampling strategies, bias, and how sample design affects the validity of epidemiological findings.
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3
Questionnaire Design
Principles of measurement, question construction, and minimizing information bias in surveys.
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4
Measures of Disease Frequency
Prevalence, incidence, and mortality rates as tools for quantifying disease occurrence in populations.
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5
Screening & Diagnostic Tests
Sensitivity, specificity, predictive values, and evaluating the accuracy of screening programs.
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6
Measures of Association
Risk ratios, odds ratios, and rate ratios for quantifying exposure–outcome relationships.
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7
Concepts of Infectious Disease Epidemiology
SIR/SEIR models, R₀, transmission dynamics, herd immunity thresholds, and disease control strategies.
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8
Review of Study Design Concepts
A refresher on observational study designs, cohort and case-control studies, ecological studies, and evidence synthesis.
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9
Hybrid Study Designs
Nested case-control, case-cohort, and two-stage designs that combine features of multiple approaches.
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10
Controlled Studies
Experimental designs including randomized controlled trials, crossover studies, and field trials.
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11
Validity in Observational Studies
Internal and external validity, selection bias, information bias, and threats to causal inference.
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12
Confounding: Detection and Control
Identifying confounders and controlling for them through design and analytical strategies.
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