GIS Analytics for Public Health Training Course

Participants can structure surveillance, facility and population datasets for reliable spatial analysis.

• Point, polygon, raster and network data models for health questions
• Relational tables linked through stable geographic and facility identifiers
• Administrative boundaries matched to reporting hierarchies and census geographies
• Population denominators aligned with case periods and geographic units
• Metadata records capturing provenance, restrictions, transformations and quality concerns
• Unique identifiers used to prevent duplicate case and facility records

Participants can clean and validate health records before mapping or statistical analysis.

• Completeness checks for onset dates, addresses, outcomes and demographic fields
• Range, format and logic tests applied to surveillance line lists
• Duplicate detection using deterministic matching and probabilistic record linkage
• Missing-location patterns assessed before excluding or imputing records
• Outlier review separates data-entry errors from plausible epidemiological events
• Audit trails record corrections, exclusions and unresolved data-quality issues

Participants can geocode health records and document positional accuracy for intended map use.

• Address standardisation improves match rates before batch geocoding runs
• Geocoding match scores reviewed against locally defined acceptance thresholds
• Manual location correction uses gazetteers, imagery and administrative references
• Coordinate reference systems selected for distance, area and local mapping
• Positional accuracy categories stored alongside geocoded case records
• Residence, exposure and diagnosis locations distinguished within outbreak datasets

Participants can design clear map products matched to operational and analytical audiences.

• Visual hierarchy directs attention to cases, boundaries and intervention sites
• Choropleth maps use rates rather than raw case counts
• Proportional symbols communicate event magnitude without obscuring nearby locations
• Sequential and diverging colour schemes match data measurement scales
• Classification methods compared using equal interval, quantile and Jenks breaks
• Map layouts include source notes, dates, denominators and uncertainty statements

Participants can calculate and map disease frequency measures using appropriate denominators.

• Incidence, prevalence, attack rate and mortality rate calculated correctly
• Person-time denominators used when populations change during observation periods
• Crude rates compared only after checking population composition differences
• Small-area rates stabilised through suppression rules and minimum-count thresholds
• Rate multipliers selected for interpretable reporting of uncommon conditions
• Denominator source dates aligned with surveillance reporting periods

Participants can produce comparable geographic rates using age standardisation and rate ratios.

• Age-specific rates calculated from stratified case and population data
• Direct standardisation applies a shared standard population distribution
• Indirect standardisation produces standardised mortality and morbidity ratios
• Confidence intervals distinguish random variation from meaningful geographic differences
• Funnel plots identify areas requiring further epidemiological investigation
• Rate ratios communicate relative burden against selected reference areas

Participants can examine disease patterns across time and geography for outbreak interpretation.

• Epidemic curves constructed using onset date and reporting date
• Weekly aggregation balances signal detection against unstable small counts
• Moving averages smooth reporting artefacts in routine surveillance series
• Time-enabled maps reveal spread, persistence and reporting delays
• Seasonal baselines distinguish expected variation from unusual disease activity
• Nowcasting assumptions documented when recent reports remain incomplete

Participants can test spatial patterning and interpret cluster results within epidemiological context.

• Global Moran's I tests overall spatial autocorrelation among area rates
• Local Moran's I identifies high-high and low-low spatial associations
• Getis-Ord Gi* identifies statistically significant hotspot and coldspot areas
• Spatial weights matrices defined by contiguity, distance or nearest neighbours
• Multiple-testing concerns considered when interpreting local cluster statistics
• Cluster outputs checked against data quality, denominators and reporting practices

Participants can analyse case locations in relation to potential exposures and population distribution.

• Kernel density estimation summarises concentrated point-event patterns
• Buffer analysis identifies cases near suspected exposure locations
• Spatial joins count cases within facilities, catchments and administrative areas
• Case-control location comparisons test plausible environmental exposure hypotheses
• Population-at-risk surfaces prevent misleading interpretation of case density
• Confidential point locations protected through aggregation and spatial masking

Participants can assess geographic access to services and identify underserved populations.

• Network analysis estimates travel distance and travel time to facilities
• Service areas model population coverage within defined travel-time thresholds
• Facility catchments assigned through nearest-provider and capacity assumptions
• Barriers such as rivers, checkpoints and seasonal roads incorporated
• Two-step floating catchment area methods compare supply and demand
• Access maps distinguish physical proximity from actual service availability

Participants can identify geographic priorities for vaccination, testing and outreach operations.

• Coverage rates mapped using eligible populations and verified doses
• Dropout analysis compares first-dose and final-dose geographic patterns
• Priority scoring combines disease burden, access barriers and vulnerability
• Location-allocation methods identify candidate sites for temporary services
• Microplanning maps assign outreach teams, target populations and service points
• Intervention boundaries documented to support post-campaign evaluation

Participants can integrate equity indicators into geographic public-health priority setting.

• Social determinants selected through a transparent conceptual framework
• Composite indices constructed with documented indicators, weights and limitations
• Deprivation, disability and language data interpreted without stigmatising communities
• Equity stratifiers compare access and outcomes across population groups
• Bivariate maps reveal overlap between disease burden and social disadvantage
• Community knowledge incorporated alongside administrative and survey data

Participants can use environmental spatial data to support disease-risk and exposure assessments.

• Land surface temperature derived from satellite imagery for heat-risk mapping
• Rainfall and vegetation indices examined for vector-borne disease surveillance
• Air-quality surfaces linked with respiratory health indicators and population exposure
• Raster resolution assessed against the scale of health decisions
• Zonal statistics summarise environmental conditions within reporting areas
• Environmental proxies interpreted cautiously alongside field and clinical evidence

Participants can apply governance and privacy safeguards to public-health GIS workflows.

• Purpose limitation defines permitted uses of identifiable health data
• Role-based access controls restrict sensitive datasets and map layers
• Minimum necessary data principles guide field and analytical extracts
• Small-number suppression prevents indirect identification in published maps
• Spatial masking methods reduce re-identification risk for point locations
• Data-sharing agreements specify retention, security, ownership and destruction requirements

Participants can identify and communicate limitations that affect geographic health conclusions.

• Underreporting, diagnostic access and testing policy distort observed disease patterns
• Modifiable areal unit problems affect aggregation and geographic comparisons
• Ecological fallacy avoided when interpreting area-level associations
• Geocoding error assessed for its effect on proximity analyses
• Sensitivity analysis tests alternative thresholds, boundaries and assumptions
• Uncertainty statements distinguish observed findings from causal claims

Participants can specify GIS reporting products for routine surveillance and incident coordination.

• Key performance indicators selected for surveillance, access and intervention monitoring
• Dashboard wireframes separate executive, operational and analytical user needs
• Refresh schedules match reporting cadence and data-quality assurance capacity
• Situation report maps communicate changes, priorities and confidence limitations
• Alert thresholds link surveillance indicators to predefined response actions
• Version control preserves traceability across rapidly updated incident products

Participants can design repeatable GIS workflows that reduce manual processing errors.

• Workflow diagrams identify inputs, transformations, outputs and quality checks
• ModelBuilder and QGIS processing models automate repeatable geoprocessing tasks
• Parameterised scripts separate reusable logic from incident-specific settings
• Scheduled data updates include validation gates before map publication
• File naming conventions support reproducibility, review and archival retrieval
• Peer review checklists catch analytical and cartographic errors before release

Participants can present GIS findings as focused operational recommendations for decision-makers.

• Decision briefings open with the operational question and key finding
• Annotated maps connect geographic evidence with recommended response actions
• Absolute burden and relative risk presented without misleading comparisons
• Counterfactual explanations test whether patterns support proposed interventions
• Technical methods documented separately from executive decision summaries
• Stakeholder questions anticipated on data quality, equity and feasibility

Participants can build, review and present a complete outbreak GIS analysis with prioritised response recommendations.

• Outbreak scenario requirements translated into an analysis and reporting plan
• Surveillance, population, facility and vulnerability datasets prepared for integration
• Rates, clusters and service-access findings produced using documented methods
• Priority intervention areas selected through transparent epidemiological criteria
• Privacy, uncertainty and equity checks completed before product review
• Peer review tests maps, assumptions, recommendations and presentation clarity
• Final briefing presents findings, limitations and accountable response actions