GIS for Disease Surveillance Training Course

Participants can calculate and interpret epidemiological measures for mapped disease patterns.

• Incidence, prevalence, attack rate, and case-fatality rate calculation
• Age-specific and risk-specific rates using appropriate population denominators
• Standardised mortality ratios and direct age standardisation for area comparison
• Constructing epidemic curves by onset date, specimen date, and report date
• Interpreting denominator uncertainty, under-ascertainment, and reporting-delay bias
• Line-list fields supporting person, place, time, exposure, and outcome analysis

Participants can structure surveillance datasets for traceable spatial analysis and reporting.

• Relational data models linking cases, facilities, laboratories, and administrative areas
• Unique identifiers, data dictionaries, and controlled vocabularies for surveillance records
• Designing case line lists with mandatory, conditional, and derived fields
• Code lists for diagnosis, outcome, specimen, and reporting status
• Metadata documenting source, update frequency, ownership, and known limitations
• Data lineage from notification receipt through analysis and published outputs

Participants can configure GIS projects using appropriate spatial data structures and layer conventions.

• Vector and raster models selected for surveillance questions
• Points, lines, polygons, and grids representing health events
• Attribute tables, joins, relates, and spatial keys for analysis
• Layer order, symbology, labels, and scale dependencies in map design
• Coordinate reference systems and transformation concepts for health datasets
• QGIS and ArcGIS Pro workspace conventions for reproducible projects

Participants can select and document coordinate systems suitable for surveillance mapping and analysis.

• Geographic coordinates, projected coordinates, and datum selection for surveillance mapping
• Choosing equal-area projections for rate maps and area statistics
• Projected coordinate systems suited to distance, travel-time, and buffering
• Checking coordinate ranges, units, axis order, and transformation warnings
• Reprojecting administrative boundaries without introducing geometry or alignment errors
• Documenting coordinate reference system decisions in project metadata

Participants can prepare surveillance and contextual data for reliable GIS analysis.

• Routine surveillance feeds from facilities, laboratories, community reports, and registries
• Importing CSV, Excel, GeoJSON, shapefile, and geodatabase source files
• Schema checks for dates, codes, missingness, duplicates, and invalid values
• Facility master lists reconciled with reporting units and catchment areas
• Data quality dimensions: completeness, timeliness, validity, consistency, and uniqueness
• Creating refresh logs and exception reports for incoming surveillance data

Participants can locate surveillance events while controlling positional uncertainty and privacy risks.

• Address standardisation before geocoding case residences and exposure locations
• Geocoding match scores, candidate review, and unresolved-address workflows
• Administrative-area assignment through point-in-polygon spatial joins
• Positional accuracy classes recorded for household and facility coordinates
• Suppressing small counts and masking locations to protect confidentiality
• Applying minimum-necessary access controls to identifiable health geodata

Participants can produce clear disease maps that support valid interpretation and action.

• Choropleth classification using quantiles, natural breaks, and equal intervals
• Rates, ratios, and counts selected according to decision purpose
• Bayesian smoothing principles for unstable rates in small populations
• Dot density, proportional symbols, and heatmaps used with stated limits
• Colour schemes, legends, and visual hierarchy for accessible outbreak maps
• Map critique using audience, message, denominator, uncertainty, and action

Participants can examine the progression of outbreaks using linked spatial and temporal methods.

• Outbreak case definitions translated into spatial and temporal analytic fields
• Creating epidemic curves linked to mapped onset-date distributions
• Time-enabled layers and animation for incident progression assessment
• Space-time cubes aggregating cases by location and reporting period
• Kernel density estimation for exploratory event concentration mapping
• Temporal aggregation choices and their effect on apparent outbreak signals

Participants can test and interpret disease clustering using established spatial statistical methods.

• Global Moran's I for measuring overall spatial autocorrelation
• Local Moran's I identifying high-high and low-low clusters
• Getis-Ord Gi* hotspot analysis with neighbourhood-distance specification
• Spatial scan statistics concepts, null hypotheses, and multiple testing
• Cluster outputs interpreted against population distribution and surveillance bias
• Documenting parameter choices, sensitivity checks, and analytical caveats

Participants can combine surveillance data with environmental factors to assess disease risk patterns.

• Environmental covariates including rainfall, temperature, elevation, and land cover
• Remote-sensing raster resolution matched to disease transmission mechanisms
• Zonal statistics summarising environmental exposures within administrative areas
• Buffer analysis around water points, markets, vector habitats, and roads
• Weighted overlay models combining epidemiological and environmental risk factors
• Confounding, ecological fallacy, and correlation limits in risk mapping

Participants can assess geographic access to surveillance, diagnostic and treatment services.

• Network analysis measuring travel distance and time to health facilities
• Service-area polygons based on road networks, speed assumptions, and barriers
• Two-step floating catchment area method for service accessibility
• Referral pathways mapped between community sites, facilities, and laboratories
• Mobility proxies interpreted cautiously during displacement and outbreak response
• Locating outreach sites using coverage gaps, population, and travel constraints

Participants can design mapped indicators, alert rules and follow-up processes for early warning.

• Threshold selection using baselines, percentiles, and expected-case counts
• Alert algorithms: CUSUM, EWMA, and moving-average detection rules
• DHIS2 aggregate data structures and geographic hierarchy for monitoring
• Dashboard indicators linking alert status, map views, and follow-up actions
• Escalation matrices defining verification, investigation, communication, and closure
• Monitoring alert sensitivity, positive predictive value, and timeliness

Participants can create controlled workflows for integrating and updating surveillance GIS datasets.

• ETL workflows extracting, transforming, validating, and loading surveillance data
• Joining laboratory results to case records using deterministic matching rules
• Automated spatial joins assigning cases to wards, districts, and catchments
• Versioned scripts, naming conventions, and folder structures for reproducibility
• Scheduled refresh procedures with audit trails and failure notifications
• Quality assurance checks before dashboard publication or situation reporting

Participants can prepare GIS-enabled field collection and supervision processes for investigations.

• Mobile data collection forms capturing coordinates, symptoms, exposures, and follow-up
• Offline basemaps and synchronisation procedures for low-connectivity deployments
• Field-team briefing packs with maps, contact lists, and safety protocols
• GPS accuracy assessment, waypoint naming, and duplicate-location prevention
• Supervisory review of incoming records, photographs, and coordinate anomalies
• Chain-of-custody principles for specimens and field investigation records

Participants can communicate surveillance findings, uncertainty and recommended actions to decision-makers.

• Situation maps tailored for incident commanders, clinicians, and community partners
• Decision thresholds connected to resource deployment and investigation priorities
• Uncertainty statements covering incomplete reporting, location error, and denominators
• Briefing notes presenting findings, limitations, recommended actions, and owners
• Risk communication maps avoiding stigma, blame, and identifiable household details
• Map production checklist covering title, source, date, scale, and disclaimer

Participants can define governance controls and operating procedures for sustained GIS surveillance.

• Data governance roles for stewardship, approval, access, retention, and disposal
• Surveillance SOPs defining collection, validation, analysis, escalation, and reporting
• Information-sharing agreements covering permitted use, recipients, and safeguards
• Indicator definitions aligned with reporting calendars and responsible owners
• After-action reviews assessing data flow, alerts, response, and lessons
• Performance scorecards tracking completeness, timeliness, map production, and action

Participants can build, review and present a complete GIS disease surveillance analysis with response recommendations.

• Scoping a priority disease, population, geography, decision, and surveillance question
• Preparing documented case, facility, boundary, and contextual datasets
• Building a reproducible GIS project with validated spatial analysis
• Producing decision maps, alert indicators, and an investigation workflow
• Peer review against privacy, data quality, interpretation, and communication criteria
• Presenting findings, limitations, actions, owners, and monitoring measures