Advanced Public Health Epidemiology and Outbreak Analytics Training Course

Course overview

Public health teams must make defensible decisions while case counts are incomplete, reporting delays distort trends, and political or operational pressure demands answers quickly. This course addresses the analytical work behind outbreak detection, investigation and control: distinguishing signal from noise, estimating transmission and severity, identifying affected populations, and communicating uncertainty without delaying action. Participants work with the types of line-list, laboratory, surveillance and denominator data that routinely challenge outbreak response teams.

The course develops advanced applied epidemiology skills for respiratory, foodborne, vector-borne and healthcare-associated outbreaks. Participants build and clean outbreak datasets; construct epidemic curves and spot maps; calculate attack rates, risk ratios and odds ratios; select and interpret regression models; assess surveillance-system performance; estimate time-varying reproduction numbers; and design analytical studies to test exposure hypotheses. Sessions also cover case definitions, bias, confounding, missing data, laboratory-epidemiology integration, genomic data interpretation and incident-level risk communication.

Teaching combines expert-led technical briefings with guided analysis in R/RStudio, Epi Info and ArcGIS Pro. Teams work through a multi-source outbreak scenario, progressing from an initial surveillance signal to a targeted control recommendation. Each participant leaves with an outbreak analytics portfolio: a documented analysis plan, cleaned line-list specification, reproducible R workflow, epidemiological briefing pack, map and epidemic curve, and a 30-day plan for applying improved surveillance and investigation practices in their organisation.

The programme is designed for experienced public health, infection prevention and surveillance professionals who already work with routine health data and need stronger analytical judgement for outbreak response, assurance and leadership.

Course objectives

By the end of this course, participants will be able to:

• Construct an outbreak case definition and line-list data dictionary that supports consistent case classification
• Calculate and interpret attack rates, incidence rate ratios, risk ratios and odds ratios for exposure hypotheses
• Produce epidemic curves, stratified tables and spot maps to identify outbreak timing, place and population patterns
• Build and interpret multivariable logistic and Poisson regression models to address confounding and effect modification
• Estimate time-varying reproduction numbers and explain the assumptions and uncertainty behind transmission estimates
• Evaluate surveillance sensitivity, timeliness, representativeness and positive predictive value using WHO-aligned indicators
• Develop a reproducible R analysis script and quality-assurance trail for an outbreak line-list
• Write an evidence-based outbreak situation report with actionable control recommendations and quantified uncertainty

Benefits of attending

Target competencies

Who should attend

• Field Epidemiologists — who lead or support analytical investigations of suspected outbreaks
• Public Health Intelligence Analysts — who turn surveillance, laboratory and population data into response decisions
• Communicable Disease Control Specialists — who need to assess transmission patterns and target interventions
• Infection Prevention and Control Leads — who investigate healthcare-associated infection clusters and escalation risks
• Health Protection Consultants — who must assure outbreak evidence and brief senior incident-management teams
• Surveillance Programme Managers — who need to improve data quality, signal detection and reporting performance

Requirements and prerequisites

Participants should have practical experience of public health surveillance, outbreak investigation, infection prevention, health intelligence or a related role. They should already understand basic epidemiological measures, including incidence, prevalence, attack rate, risk ratio, odds ratio and confidence intervals, and be comfortable working with tabular data in Excel or similar software. Familiarity with study designs and routine line lists is expected. No prior programming or GIS expertise is required: guided R/RStudio and ArcGIS Pro exercises are provided. This is not a foundation course in epidemiology, microbiology or statistics.

Training methodology

Instructor-led sessions introduce the epidemiological rationale and decision rules behind each method, followed by guided analysis of realistic outbreak datasets in R/RStudio, Epi Info and ArcGIS Pro. Participants clean line lists, test hypotheses, build epidemic curves, model risk and critique surveillance performance in small response teams. Facilitated case discussions examine how laboratory results, field intelligence and uncertain estimates affect control decisions. Each day closes with an applied output, and the final session converts learning into a workplace outbreak analytics improvement plan.

Course outline

Day 1: Outbreak intelligence, case definitions and data quality

• Outbreak verification and signal triage thresholds
• Operational case definitions and case classification rules
• Line-list architecture, variable coding and data dictionaries
• Duplicate detection and record linkage principles
• Missing-data mechanisms and data-quality profiling
• Descriptive epidemiology by person, place and time
• Epidemic curve construction and interpretation

Workshop: Participants clean and document a flawed respiratory-outbreak line list, then produce a case definition, data dictionary and first epidemic curve.

Day 2: Analytical epidemiology and exposure assessment

• Attack rates, secondary attack rates and denominator selection
• Risk ratios, odds ratios and attributable fractions
• Cohort, case-control and case-case study selection
• Questionnaire design and exposure-window reconstruction
• Stratified analysis using Mantel-Haenszel methods
• Confounding, interaction and causal diagrams
• Bias assessment in outbreak investigations

Workshop: Teams design an analytical study for a foodborne outbreak and calculate stratified effect estimates to prioritise exposure hypotheses.

Day 3: Modelling transmission, severity and uncertainty

• Generation intervals and serial intervals
• Time-varying reproduction number estimation
• Delay distributions and nowcasting principles
• Logistic regression for severe-outcome risk
• Poisson and negative binomial models for count data
• Model diagnostics, residual checks and overdispersion
• Confidence intervals, sensitivity analysis and uncertainty statements

Workshop: Participants use R/RStudio to estimate transmission trends and fit a multivariable model identifying predictors of hospital admission.

Day 4: Spatial, laboratory and surveillance analytics

• Geocoding cases and denominator population linkage
• Spot maps, rate maps and spatial clustering
• ArcGIS Pro symbology and map-quality controls
• Laboratory turnaround time and test-positivity analysis
• Interpreting pathogen typing and genomic cluster information
• Surveillance sensitivity, timeliness and representativeness
• Signal detection using baselines and aberration thresholds

Workshop: Participants create an ArcGIS Pro outbreak map and complete a surveillance-performance scorecard for a simulated healthcare-associated cluster.

Day 5: Decision support, reporting and response improvement

• Triangulating epidemiological, laboratory and field evidence
• Incident action objectives and control-measure prioritisation
• Situation report structure and data visualisation choices
• Communicating uncertainty to incident-management teams
• Equity analysis and identification of underserved populations
• Reproducible analysis documentation and peer review
• Post-outbreak review and surveillance improvement planning

Workshop: Participants deliver an incident briefing from the full case study and produce a defensible situation report with control recommendations and a 30-day improvement plan.

Tools & standards covered

R, RStudio, Epi Info, ArcGIS Pro

A typical training day

Live online deliveries follow the same structure in the East Africa Time zone, with shorter screen blocks and longer breaks.

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions