GIS and Remote Sensing for Public Health Training Course

Duration 10 days 20 modules · 65 contact hours
Level Intermediate Certificate on completion
Who attends Epidemiologists, Public Health Officers and others
Fee from USD 2,200 KSh 190,000 for Kenyan organisations
Where 52 cities + online See dates by city
Next start 9 Nov 2026 Course code SD-PH-027

Before you join: Participants should understand basic public health concepts such as incidence, prevalence, surveillance, population denominators and health service delivery.

Dates & prices

Two start dates a month in every city, and live online on weekdays or weekends. Fees are per participant; classroom fees include tuition, materials, lunch and refreshments, and the certificate.

Suggest a date or city

Showing the next six months. Later dates, other cities and private deliveries for your team are available on request.

Course overview

Public health teams must make decisions about disease control, service coverage, environmental hazards and emergency response with data that has a location, time and population attached. Routine surveillance tables can show rising cases, but they rarely show where transmission clusters, which communities lack nearby services, how rainfall or land cover may affect risk, or whether reporting gaps distort the picture. GIS and remote sensing provide practical ways to combine health, demographic, facility, environmental and earth-observation data into evidence that programme managers can act on.

This course teaches participants to prepare geographic health data, build maps, analyse spatial patterns and interpret satellite-derived environmental indicators for public health and epidemiology. Participants use geocoding, coordinate reference systems, thematic mapping, spatial joins, buffer analysis, travel-time modelling, cluster detection and disease mapping methods. They learn to assess data quality, select suitable satellite products, derive indicators such as vegetation, rainfall, land surface temperature and water presence, and relate these measures to surveillance data without overstating causal claims.

Instruction combines short technical briefings with guided software work, public health case material and structured group analysis. Cases reflect routine disease surveillance, vector-borne disease control, health facility access, outbreak investigation and humanitarian health planning. From the first week, participants develop a practical spatial health analysis using an approved workplace dataset or a realistic supplied scenario. They leave with a documented analysis workflow, map products, an indicator specification and a decision briefing suitable for adaptation within their own programme.

The course is suited to public health professionals who already work with surveillance, programme, facility, population or environmental data and need stronger spatial analysis capability. It is equally relevant to GIS specialists supporting health agencies who need to understand epidemiological questions, data limitations and responsible communication of health maps.

What you will be able to do

  • Prepare health, population, facility and environmental datasets for spatial analysis
  • Construct thematic maps using appropriate classification, symbology and cartographic design
  • Apply geocoding, spatial joins, buffers and network analysis to public health questions
  • Calculate disease rates, standardised measures and spatially referenced denominators
  • Detect and interpret spatial clusters using exploratory spatial data analysis methods
  • Derive public health exposure indicators from satellite imagery and earth-observation products
  • Assess spatial data quality, privacy risks and uncertainty in health mapping outputs
  • Produce a documented GIS and remote sensing analysis with a decision briefing

Benefits of attending

For you

  • Produce defensible disease, service access and environmental risk maps
  • Interpret satellite indicators for vector-borne and climate-sensitive disease programmes
  • Identify spatial clusters and reporting gaps with documented analytical methods
  • Translate location-based findings into concise intervention recommendations
  • Build a portfolio-ready public health GIS analysis and decision briefing

For your organisation

  • Improve geographic targeting of surveillance, prevention and outreach resources
  • Reduce avoidable mapping errors through documented data and quality controls
  • Link routine health information with environmental and population evidence
  • Strengthen outbreak situational awareness through repeatable spatial investigation workflows
  • Provide decision-makers with clearer evidence on coverage, access and disease risk

Course modules

20 modules over 10 days

Participants can frame public health questions using location, time, population and exposure concepts.

  • Spatial thinking: place, scale, exposure, access and population denominators
  • Public health questions suited to mapping, proximity and area-based analysis
  • Point, line, polygon, raster and network data structures compared
  • Administrative boundaries, catchments and service areas distinguished carefully
  • Ecological fallacy and modifiable areal unit problem explained
  • Problem statements linking intervention decisions to measurable spatial evidence

Participants can select suitable spatial data models and coordinate reference systems for health analysis.

  • Vector geometry, raster cells and attribute tables examined
  • Geographic and projected coordinate reference systems differentiated accurately
  • Selecting projections for distance, area and national health mapping
  • Datum transformations and coordinate mismatches diagnosed before analysis
  • Metadata fields recording source, date, resolution and positional accuracy
  • Layer naming conventions supporting reproducible public health workflows

Participants can prepare, geocode and document health datasets for mapping and analysis.

  • Cleaning facility, case and community records using data dictionaries
  • Geocoding addresses, settlements and health facilities with match review
  • Handling duplicate locations, missing coordinates and invalid geometries
  • Unique identifiers linking surveillance records to geographic reference data
  • Date parsing, episode definitions and temporal aggregation decisions
  • Geocoding quality logs documenting match methods and unresolved records

Participants can construct geographically appropriate denominators and boundary datasets for health indicators.

  • Census, WorldPop and gridded population datasets compared for use
  • Administrative hierarchy validation against official boundary source records
  • Areal interpolation methods for aligning population and health boundaries
  • Age-sex population structures incorporated into health rate calculations
  • Facility catchment assumptions tested against referral and travel patterns
  • Denominator uncertainty documented when population estimates are outdated

Participants can design maps that communicate public health evidence clearly and responsibly.

  • Choropleth, proportional symbol and dot-density maps selected appropriately
  • Natural breaks, quantiles and equal intervals compared for classification
  • ColourBrewer palettes selected for sequential, diverging and categorical data
  • Titles, legends, scale bars and source notes designed clearly
  • Rates and counts mapped separately to prevent misleading interpretation
  • Map critique using audience, decision, uncertainty and accessibility criteria

Participants can inspect spatial distributions and identify patterns requiring further epidemiological investigation.

  • Mapping case locations, event dates and reporting completeness indicators
  • Kernel density estimation used to visualise event concentration
  • Nearest-neighbour analysis assesses point-pattern clustering versus dispersion
  • Moran's I measures global spatial autocorrelation in area data
  • Local Indicators of Spatial Association identify local spatial patterns
  • Outliers, boundary effects and small-number instability investigated systematically

Participants can calculate and map epidemiological measures with appropriate geographic interpretation.

  • Incidence, prevalence and mortality rates calculated using correct denominators
  • Crude and age-standardised rates distinguished for geographic comparison
  • Standardised mortality ratios mapped with observed and expected events
  • Empirical Bayes smoothing stabilises rates in small-area analyses
  • Standardised incidence ratios interpreted with confidence interval awareness
  • Rate suppression rules protect confidentiality and reduce unstable comparisons

Participants can combine health and contextual datasets using repeatable GIS query and overlay methods.

  • Attribute queries identify priority facilities, districts and population groups
  • Spatial joins connect cases with districts, catchments and exposure zones
  • Intersect, union and clip operations create analysis-ready geographic layers
  • Zonal statistics summarise raster exposure values within health areas
  • Raster reclassification converts continuous surfaces into programme categories
  • Query expressions documented for audit, reruns and peer review

Participants can analyse physical access to health services and interpret service coverage gaps.

  • Euclidean distance and travel-time measures compared for facility access
  • Buffer analysis estimates populations within defined service thresholds
  • Network analysis models routes, impedance and referral travel times
  • Friction surfaces combine roads, slope, land cover and barriers
  • Two-step floating catchment area method estimates provider accessibility
  • Coverage gaps prioritised using population, vulnerability and travel-time evidence

Participants can create spatial products that support timely outbreak investigation and response.

  • Line lists structured for person, place and time analysis
  • Epidemic curves linked with mapped onset dates and locations
  • Case definitions and laboratory status represented without disclosure risks
  • Spot maps distinguish probable, confirmed and discarded cases
  • Ring mapping supports vaccination, contact tracing and active case finding
  • Incident map updates governed by version control and approval rules

Participants can explain how earth-observation data supports public health exposure and risk analysis.

  • Electromagnetic spectrum concepts relevant to optical and thermal imagery
  • Spatial, temporal, spectral and radiometric resolution compared
  • Landsat, Sentinel and MODIS products matched to health questions
  • Passive optical and active radar sensors distinguished by capability
  • Cloud cover, atmospheric effects and seasonal timing considered
  • Satellite data limitations separated from public health inference claims

Participants can obtain and prepare satellite imagery for environmental health indicator extraction.

  • Search criteria define location, dates, cloud threshold and sensor
  • Level-1 and surface reflectance products selected appropriately
  • Cloud masks, shadows and no-data pixels removed before analysis
  • Band stacking and clipping produce analysis-ready image subsets
  • Resampling methods selected for categorical and continuous raster data
  • Processing records capture product identifiers, parameters and acquisition dates

Participants can derive and interpret environmental indicators relevant to disease transmission and exposure.

  • Normalized Difference Vegetation Index calculated from red and near-infrared bands
  • Normalized Difference Water Index identifies surface water patterns
  • Land surface temperature derived and interpreted with caution
  • Rainfall estimates extracted from CHIRPS gridded precipitation products
  • Land-cover classes summarised around settlements and health facilities
  • Seasonal indicator time series aligned with surveillance reporting periods

Participants can map environmental features that may influence vector habitat and disease risk.

  • Supervised classification uses labelled training samples and validation data
  • Random forest classification distinguishes land-cover categories from imagery
  • Confusion matrices report overall, producer's and user's accuracy
  • Object-based image analysis compared with pixel-based classification approaches
  • Water-body persistence assessed through multi-date satellite observations
  • Vector habitat proxies documented separately from confirmed entomological evidence

Participants can select and interpret cluster detection and risk mapping methods for health surveillance.

  • Kulldorff spatial scan statistic identifies unusually high-rate clusters
  • Space-time scan statistics detect clusters with temporal concentration
  • Getis-Ord Gi* identifies statistically significant hot and cold spots
  • Kernel smoothing produces continuous disease intensity surfaces
  • Bayesian disease mapping concepts distinguish signal from random variation
  • Multiple testing, surveillance bias and cluster interpretation limitations discussed

Participants can develop transparent spatial risk models using epidemiological and environmental variables.

  • Conceptual causal diagrams distinguish confounders, mediators and exposure proxies
  • Regression model inputs prepared at consistent spatial and temporal scales
  • Logistic regression estimates associations between exposures and health outcomes
  • Poisson regression models counts using population offsets appropriately
  • Multicollinearity checked using correlation matrices and variance inflation factors
  • Risk surfaces validated against withheld observations and expert knowledge

Participants can manage spatial health data according to quality, confidentiality and ethical requirements.

  • Positional accuracy, completeness, timeliness and consistency assessed systematically
  • Small-area disclosure risks identified before publishing health maps
  • Aggregation, masking and jittering methods protect sensitive case locations
  • Data minimisation principles applied to personal health information
  • Access controls and data-sharing agreements recorded for project datasets
  • Ethical review questions address harm, stigma and inequitable targeting

Participants can document, review and maintain a reproducible public health spatial analysis workflow.

  • Folder structures separate raw, processed, output and documentation files
  • Analysis logs record assumptions, transformations, tools and parameter values
  • Metadata templates capture lineage, ownership, update frequency and limitations
  • Peer review checks coordinate systems, denominators and map claims
  • Version control principles manage revisions to datasets and outputs
  • Quality assurance checklists support release approval and future updates

Participants can present spatial findings with clear recommendations, limitations and action implications.

  • Decision briefings state the question, finding, implication and recommended action
  • Map annotations direct attention to priority areas and uncertainty
  • Scenario comparisons show consequences of alternative service deployment choices
  • Confidence intervals and data limitations communicated in plain language
  • Dashboard indicators selected for outbreak operations and programme reviews
  • Stakeholder questions anticipated through evidence, assumptions and response options

Participants can build, review and present a complete GIS and remote sensing public health analysis for a defined decision.

  • Decision question, target population and intervention context defined precisely
  • Health, population, facility and environmental datasets prepared and documented
  • Spatial methods selected, justified and applied to the evidence
  • Maps, indicators and analytical outputs checked against quality criteria
  • Peer review tests interpretation, privacy safeguards and decision relevance
  • Executive briefing presents findings, limitations, recommendations and implementation actions

Exercises and examples are adapted to each cohort's sectors and roles.

What you take back to work

  1. Public health GIS analysis plan
  2. Spatial health data quality checklist
  3. Disease mapping and rate calculation template
  4. Facility accessibility modelling workflow
  5. Remote sensing indicator specification
  6. Capstone decision briefing and map portfolio

Who should attend

  • Epidemiologists — analyse disease patterns and communicate geographically targeted findings
  • Public Health Officers — plan interventions using service, risk and population maps
  • Health Information Managers — improve geographic quality and use of routine data
  • GIS Analysts — apply spatial methods to epidemiological and health service questions
  • Disease Surveillance Officers — investigate clusters, reporting gaps and outbreak spread
  • Humanitarian Health Coordinators — target mobile services and assess access during emergencies

Skills and tools covered

Health data mappingSpatial epidemiologyRemote sensing analysisService accessibility modellingOutbreak spatial investigationHealth map communication

Tools and standards

ArcGIS Pro, QGIS, Google Earth Engine, DHIS2

How the course runs

Training methodology

The course uses short instructor briefings followed by guided GIS and remote sensing tasks. Participants work with realistic surveillance, facility, population and satellite datasets, progressing from data preparation to interpretation and communication. Case work examines outbreak detection, malaria risk, immunisation access and emergency health planning. Group exercises test analytical choices, map critique and interpretation of uncertainty. Simulations mirror an incident briefing and programme planning meeting. Each participant develops an application plan and receives structured feedback on their final analysis and decision briefing.

Requirements and prerequisites

Participants should understand basic public health concepts such as incidence, prevalence, surveillance, population denominators and health service delivery. Experience using spreadsheets to inspect, clean and summarise tabular data is assumed. Participants should be comfortable working with files, folders and simple data tables. Prior GIS or remote sensing experience is helpful but not required; the course starts with essential spatial concepts before progressing to analysis. Programming, advanced statistics, satellite image processing expertise and prior use of ArcGIS Pro, QGIS or Google Earth Engine are not required.

A typical training day

08:30 – 10:30First session
10:30 – 10:45Refreshment break
10:45 – 12:30Second session
12:30 – 13:30Lunch and networking
13:30 – 15:00Third session
15:00 – 15:15Refreshment break
15:15 – 16:30Workshop and daily review

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

What the fee includes

  • Instruction by a practitioner facilitator
  • Full course workbook and materials
  • Exercise files, templates and case studies
  • Refreshments and lunch on classroom deliveries
  • Certificate of completion
  • Post-course application plan

Frequently asked questions

You should understand basic public health measures and be comfortable working with spreadsheet data. No prior GIS, remote sensing, programming or advanced statistical experience is required.

Bring a laptop if attending in person and, where permitted, an anonymised workplace question or dataset. Software access and course datasets are arranged for the training; trainers will explain any installation requirements before the course.

It is designed for epidemiologists, surveillance officers, public health programme staff, health information managers, GIS analysts and humanitarian health personnel. It is most valuable for people who need to turn location-based data into operational decisions.

The course uses health-specific questions, measures, datasets and ethical controls rather than generic mapping examples. It covers disease rates, outbreak mapping, facility access, environmental exposure indicators and confidential health data handling.

Participants can adapt the workflow to map surveillance patterns, assess facility access, identify priority areas and combine routine health data with rainfall, temperature or land-cover indicators. The final analysis structure is designed for reuse in programme reviews and incident briefings.

The first part builds core GIS, health data and mapping capability while participants begin their applied analysis. The second part develops remote sensing, spatial epidemiology, risk modelling, quality assurance and communication, ending with a reviewed capstone presentation.

Questions about this course?

Ask about dates, fees, group bookings or running it for your team. Our training team replies by email.

Ask on WhatsApp info@skillsetdevelopment.com

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