Aim
Molecular Epidemiology teaches how molecular data (genetics, genomics, biomarkers, and pathogen sequences) supports epidemiology for tracking disease, identifying risk factors, and guiding public health decisions. Learn study design, lab-to-data workflows, analysis basics, and reporting through hands-on case studies.
Program Objectives
- Core Concepts: how molecular markers link exposure, susceptibility, and disease.
- Study Design: cohorts, case-control, outbreak studies, biobanks.
- Molecular Data: SNPs, GWAS concepts, RNA/protein biomarkers, microbiome (intro).
- Pathogen Genomics: sequencing for transmission and variant tracking (overview).
- Analysis: association, confounding, interaction, multiple testing (intro).
- Quality: bias sources, batch effects, missing data, reproducibility.
- Ethics: consent, privacy, data sharing, governance.
- Capstone: design and analyze a molecular epi study (case-based).
Program Structure
Module 1: Molecular Epidemiology Foundations
- What molecular epidemiology answers: risk, cause, transmission.
- Markers: germline genetics, somatic changes, biomarkers, pathogens.
- Exposure → biomarker → disease pathway thinking.
- Outcome measures and case definitions.
Module 2: Study Design and Sampling
- Case-control vs cohort vs nested case-control.
- Sampling, matching, power concepts (intro).
- Specimen collection: blood, saliva, tissue, swabs (overview).
- Biobanks and metadata: what must be recorded.
Module 3: Molecular Data Types (Human)
- Genetic variation: SNPs, CNVs, haplotypes (intro).
- GWAS basics: association signals and interpretation.
- Epigenetics concepts (intro): methylation and environment links.
- Biomarkers: proteins/metabolites and validation basics.
Module 4: Pathogen Molecular Epidemiology (Genomic Surveillance)
- Outbreak investigation using sequences (concepts).
- Variants, lineages, and transmission inference (overview).
- Phylogenetics basics and common plots (intro).
- Linking lab results with contact tracing and time.
Module 5: Data Quality, Bias, and Confounding
- Selection bias, information bias, population stratification.
- Batch effects and lab variability; QC checks.
- Missing data and sensitivity checks (intro).
- Replicability and external validation concepts.
Module 6: Statistical Analysis (Practical Intro)
- Association testing concepts: odds ratio, risk ratio, hazard ratio (overview).
- Regression basics for molecular epidemiology (intro).
- Gene-environment interaction concepts.
- Multiple testing and false discovery rate (FDR) concepts.
Module 7: Interpretation and Reporting
- Causal caution: correlation vs causation.
- Effect sizes, confidence intervals, and uncertainty.
- Reporting standards and clear visual summaries.
- Communicating findings for public health decisions.
Module 8: Ethics, Privacy, and Data Governance
- Informed consent and participant rights.
- Genetic privacy, de-identification, and data access controls.
- Responsible sharing: repositories and governance models (overview).
- Risk communication and avoiding misuse.
Final Project
- Choose a case study: outbreak tracking, risk factor study, biomarker validation.
- Deliverables: study design + analysis plan + results summary + limitations.
- Optional: short slide-style report for public health stakeholders.
Participant Eligibility
- Students and professionals in public health, microbiology, biotechnology, genetics
- Epidemiology basics helpful; no advanced coding required
- Researchers working with clinical, biomarker, or sequencing datasets
Program Outcomes
- Design molecular epidemiology studies with clear sampling and endpoints.
- Understand human and pathogen molecular data types used in epidemiology.
- Interpret association outputs and common bias sources.
- Write a clear, ethics-aware molecular epidemiology report.
Program Deliverables
- e-LMS Access: lessons, case studies, templates.
- Toolkit: study design template, QC checklist, reporting outline.
- Capstone Support: feedback on project design and results.
- Assessment: certification after project submission.
- e-Certification and e-Marksheet: digital credentials on completion.
Future Career Prospects
- Molecular Epidemiology Research Assistant
- Public Health Genomics Analyst (Entry-level)
- Genomic Surveillance Associate
- Biomarker Research Assistant
Job Opportunities
- Public Health Organizations: surveillance, outbreak analytics, program evaluation.
- Hospitals/Research Centers: clinical research and biomarker studies.
- Genomics Labs/CROs: sequencing data interpretation and reporting support.
- Universities/NGOs: population health research and field studies.
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