Foundations & applied surveillance
Lessons 01 — 02L · 01
Introduction & Causal ConceptsFoundations of epidemiological thinking, causal inference, and the counterfactual framework.
Open module
L · 02
Surveillance & Outbreak InvestigationRoutine surveillance systems, outbreak detection, and field-epidemiology response — case definitions, epi curves, and contact tracing.
Open module
Population & measurement
Lessons 03 — 04L · 03
SamplingSampling strategies, bias, and how sample design affects the validity of epidemiological findings.
Open module
L · 04
Questionnaire DesignPrinciples of measurement, question construction, and minimizing information bias in surveys.
Open module
Measures & tests
Lessons 05 — 07L · 05
Measures of Disease FrequencyPrevalence, incidence, and mortality rates as tools for quantifying disease occurrence in populations.
Open module
L · 06
Screening & Diagnostic TestsSensitivity, specificity, predictive values, and evaluating the accuracy of screening programs.
Open module
L · 07
Measures of AssociationRisk ratios, odds ratios, and rate ratios for quantifying exposure–outcome relationships.
Open module
Study designs
Lessons 08 — 10L · 08
Review of Study Design ConceptsA refresher on observational study designs, cohort and case-control studies, ecological studies, and evidence synthesis.
Open module
L · 09
Hybrid Study DesignsNested case-control, case-cohort, and case-crossover designs that combine elements of cohort and case-control studies.
Open module
L · 10
Controlled StudiesRandomized controlled trials, quasi-experimental designs, allocation, blinding, and the role of experimental control in epidemiology.
Open module
Validity & causal inference
Lessons 11 — 12L · 11
Validity in Observational StudiesInternal and external validity, selection bias, information bias, and confounding as the three dominant threats to validity in observational research, with strategies to anticipate and mitigate each.
Open module
L · 12
Confounding & Causal InferencePre-analysis design choices, detection through stratification and DAGs, multivariable adjustment, and the full causal-inference framework that closes the bias triad.
Open module