1. Introduction to spatial analysis for epidemiology
Concepts and methods
NTD Asia January 2016
Marc SOURIS
Remote Sensing and GIS Field of Study

2. Spatial analysis in epidemiology
Introduction

3. Diseases are complex systems
Different groups of agents acting and interacting, in relation with their characteristics and environment:
Pathogens (if exist, like virus, bacteria, parasites…)
Hosts (human or animal) potentially susceptible to be exposed to the pathogen and infected
Vectors (if exist: mosquito, flea, rodent…)
Reservoirs (civet, bat, rodent…)

4. Diseases are complex systems
Many actors with relationships and complex mechanisms at different geographical scales
Pathogens (virus, bacteria, parasite, fungus, prion)
Reservoirs
(civet, bats…)
Vectors (mosquito, rodent, bats, snail…)
Host (human or animal)

5. Diseases are complex systems
Hosts, vectors, reservoirs, even pathogens are influenced by:
individual factors: genetic, demographic (age, sex…), socio-economic, biologic (as immune status, immune response, etc.)…
relationship between agents (based on space and time): contagion, spreading, linked by contact or proximity
environment (natural or social): organization of space, spatial structures, land use, proximity to infrastructures, natural hazards,
Health system and structure

6. Diseases are complex systems
Research and studies involve many fields:
Biology and virology to study pathogens
Biology and medicine to study disease in individuals
Epidemiology to study risk factors from populations
Entomology, biology, ecology to study vectors and reservoirs
Ecology and geography to study environment
Social sciences (geography, anthropology, sociology…) to study and characterize systems, society, and their mechanisms
Mathematics, statistics, and computer sciences to characterize and model phenomena (descriptive data, space, time)

7. Diseases are complex systems
Risk factors
Some characteristics cannot be determined individuals by individuals, and should be considered as probabilities
These probabilities are determined in populations and groups, through the use of statistics
Most risk factors are located in time and space, with many levels and effects of context
Variability can be high because the probabilities of occurrence may be low. Low probability events need to be taken into account, and give the whole system a high temporal and spatial variability.
Some risk factors are mobile, others are immobile (geographic characteristics). We need to be careful with mobile factors and risk maps!

8. Spatial analysis in epidemiology
Why spatial analysis?

9. Why spatial analysis in epidemiology ?
Spatial analysis in epidemiology is used mainly:
to highlight the spatial and temporal differentiation in the distribution of events
to test hypotheses about the factors involved in the cause of these events, using their location in time and space
It allows viewing, synthesize and analyze position and spatial relationships between events (continuity, consolidation, attraction-repulsion, pattern, centrality, movement, diffusion processes ...).
It allows to analyze the spatial relationship between attribute values and environmental characteristics (environmental correlations)

10. Why spatial analysis in epidemiology?
Events or ratios may present specific spatial distribution or patterns, due to individual risk factors (which may present also spatial distribution), interaction with environment, interaction between hosts (spreading, contagion, contacts).
Why spatial analysis?
To highlight and analyze spatial distribution, hot spots, clusters, disease pattern, diffusion pattern, residual distribution
How?
Set of methods and tools to manage; display; analyze; simulate; spatial situations: GIS, mapping, statistics, geostatistics, statistical modeling, spatial statistics, spatial modeling

11. Tools for spatial analysis in epidemiology
Spatial analysis includes many tools and techniques :
Tools for description and visualization, allowing analysis and visual synthesis of observed situations
Tools for geostatistics and statistical analysis, to characterize the observed situations (cluster, shape, distribution), and to study the spatial relationships between events
Tools for modeling and simulation, to evaluate the different possible situations restricted to the assumptions developed during the analysis
GIS are essential tools to manipulate data, to manage spatial locations, to perform aggregations and environmental correlations

12. GIS and spatial epidemiology
Methods and tools: why GIS is useful
Many relations between actors are based on proximity and spatial relationships, especially with infectious diseases
Many data must be evaluated on populations, using surveys and statistics
GIS help managing data, mapping, and performing spatial analysis (statistics, geostatistics, scale transfers, integration, etc).
GIS need to be able to perform statistical modeling in order to separate the three kinds of spatial influence (individual factors; environmental factors; spatial relationship factor).

13. Spatial analysis for epidemiology
Type of studies conducted in spatial epidemiology (examples)
Visualization of health data, and health atlas. Mapping can represent what happens in each place, but it is mainly used in health to understand or illustrate a global or local spatial distribution.
Analysis of the use of the healthcare system, optimization of the healthcare system, geomarketing of health. Many parameters are involved in these analyzes: healthcare services supply, accessibility, demand characteristics, demographics, socioeconomic conditions, et.

14. Spatial analysis for epidemiology
Type of studies conducted in spatial epidemiology (examples)
Environmental correlation studies. The objective is to study the relationship between a health indicator and environmental exposure.
Local studies about a source point or along a network. These studies aim to highlight a health phenomenon (characteristics, morbidity, mortality) around sites of assumed risk (pollution, industrial risk).
Studies to detect locations which are responsible of a health event, or those which concentrate the consequences. Regardless of the research causal factors, real-time detection of these places by monitoring and alert systems can limit the spread of a phenomenon.

15. Spatial analysis in epidemiology: outline
1. Data preparation, GIS, and statistical analysis: major indices in epidemiology (incidence, prevalence, RR, OR); data integration in polygons; data standardization; Environmental data integration with GIS (buffering, integration, distance matrix, etc.); statistics; statistical modeling;
2. Spatial visualization: cases, prevalence, incidence, OR, regression residual, etc. Spatial variations, interpolations; Bayesian estimate;
3. Spatial dependence analysis: point pattern analysis (position, trend, structure; centrality; spatial model); global autocorrelation; global bivariate spatial correlation;
4. Cluster detection and local spatial dependence: local association; cluster detection; bivariate local cluster detection;
5. Space-time analysis: time analysis; visualization; moving averages; index cases; pathway reconstruction; space-time local cluster detection;
6. Modeling the spread: finding a diffusion model in space and time; finding characteristics of the spread (direction; speed; periodicity; etc.)

16. Spatial data in health sciences and epidemiology
Epidemiological data, at individual or aggregated level, with place and time of patient, from public or private administrations (health ministry, hospitals, health insurances, statistical administration, census, etc.)
Environmental data on possible risk factors:
Air , soil, water pollution, etc.
Climate
Economic activities, agriculture
Urban environment, land use, forest, water bodies, etc.
Field surveys (observation, inventory, survey, capture, samples…)
Interpretation of remote sensing data (satellite, aerial photography)

17. Spatial visualization: mapping
Mapping is used for visual analysis and interpretation
Graphic semiology and cartographic language need to be used to avoid graphical errors in interpretation
Discretization is needed for quantitative values

18. Spatial visualization: synthesis tools
Trend surface
by interpolation from quantitative data : Kernel, IDW, NN
Shows values even where no cases can occur

19. Spatial visualization: synthesis tools
Example of Standard Deviational Ellipses (SDE)
SDE doesn’t represent clusters, but central tendency and synthesis of absolute locations

20. Spatial visualization: mapping
Standardization and mapping
SMR : Observed vs Expected
Significativity : Breslow and Day

21. Spatial analysis: local dependence
Local indices of spatial association (LISA)
Getis-Ord local statistics to identify “hot spots”
Local Moran’s index - mapping statistically significant values or scatter plot (neighbors values vs observed values)

22. Spatial analysis: local clustering
Local cluster detection
Kulldorf’s spatial scan statistic
(most likely cluster detection)

23. Data preparation with GIS
Database preparation (epidemiological data)
Cases; ratios; by location; by surface; fixed locations; etc.
Data integration, scale transfer (environmental risk factor)
All GIS methods in scale transfer for layer to layer calculation: spatial joins, overlays, search in a radius, search in the neighborhood, etc.

24. Spatial analysis and GIS for epidemiology: sotfware
Full GIS software solution for research and scientific studies SavGIS
Free download at
Spatial Database management ● Spatial queries ● Spatial analysis ● Geostatistics ● Interpolations ● Remote sensing ● Cartography ● Network analysis and Operation Research ● 3D ● Georeferencing ● Digitalization ● and more…

25. Spatial analysis and GIS for epidemiology: SavGIS
Savateca : Administration database management
Savedit : Digitalization and quality control
Savamer : Georeferencing and mosaics
Savane : Queries, analysis, statistics, mapping, image processing, etc.

26. End
M. Souris, 2016