Abstract
Health-care systems aid in the diagnosis, treatment and prevention of diseases. Epidemiology deals with the demographic study on frequency, distribution and determinants of disease in order to provide better health-care. Today information technology has made data pervasive i.e. data is available anywhere and in abundance. GIS in epidemiology enables prompt services to mankind or people at risk. It brings out health-care services that are amicable for prevention and control of disease spread. This could be achieved when epidemiology data is modeled considering temporal and spatial factors and using data driven computation techniques over such models. This chapter discusses 1) the need for integrating GIS and epidemiology, 2) various case studies that indicates the need for spatial analysis being performed on epidemiologic data, 3) few techniques involved in the spatial analysis, 4) functionalities provided by some of the widely used GIS software packages and tools.
TopIntroduction
Health-care systems focus on prevention, diagnosis and treatment of diseases. With the increase in population, the amount of medical information that the health-care administrators should handle is exponentially increasing. So, in order to provide better health-care facilities to human beings it is necessary that health-care systems are to be integrated with technology. This chapter will clearly explain the need for innovative technologies in handling medical data.
Epidemiology data describes distribution of disease and risk of the disease spread among a given population. The space – time modeling of such data gives insights about spatial variation of disease and risk factors causing such diseases in various spatial locations through time. Epidemiological studies based on GPS technology is a research area which is gaining importance wherein the space time data histories are used for epidemiologic analyses (Meliker et al., 2011). Spatial and Temporal factors of epidemiology data have its significance in various applications like hotspot detection of Kalaazar disease in the Vaishali district (Bihar), India (Bhunia et al., 2013). Epidemiology data collection methods include investigation on symptoms of disease, epidemiologists survey records, death rate, etc. These data are employed in geo-epidemiology analysis for disease mapping. One such notable application is use of geo-statistical analysis methods for disease mapping (Giorgi et al., 2008). Hence, analysis of epidemiological data can contribute towards development of better health-care systems.
Geographic Information System is software system that stores and manipulates spatial and a-spatial data. In addition, spatial and temporal characteristics of epidemiology data can be modeled using GIS. Scientific methods can be applied to understand the distribution of epidemiologic data which can help in controlling the disease spread by forecasting the disease progression. One such application was proposed for controlling the mumps disease using Spatio-temporal analysis (Yu et al., 2018). Several scientific methods employed in epidemiology applications are geo-referencing, area estimation, migration of population, disease mapping, detection of area of interest of disease, and integration of spatial and temporal clusters (Zulu et al., 2014; Kirby et al., 2017).
Recent study on endemic disease (Srinivasa Roa et al., 2018) was made to explore the use of GIS tools and spatial autocorrelation methods using spatial statistics Getis-Ord Gi*. Data mining techniques were applied to classify endemicity level using patterns generated by Self Organizing Map (SOM). The objective of this chapter is to bring insight about the need for GIS in epidemiological studies. This chapter also highlights the significance of spatial analysis in finding the distribution of diseases which would help the health-care professionals in controlling the diseases. This chapter also presents details about the scientific methods and tools involved in GIS.
In the second section, an introduction to health information management and its importance in epidemiology is presented. Third section explains the basic concepts in GIS, its applications, future directions with respect to forecasting. Fourth section explains case studies on hotspot analysis that has been performed over epidemiological data collected from various sources. Fifth section presents case studies with respect to epidemiological data analysis. Sixth section presents some of the spatial analysis and modeling techniques that can be used. Seventh section explains some of the GIS software that is used widely. The chapter concludes by providing some future research directions. Finally, the related terms are listed for better understanding of this chapter.
Key Terms in this Chapter
Connectivity: Two linear features A, B are said to be connected when there is path to traverse from A to B and B to A.
Point: Point is a geographic location represented in Latitude and Longitude.
Temporal Autocorrelation: The measure of relationship between variable indexed by time by correlating variable with itself at some delay.
Epidemic: Infectious disease rapidly spread within short duration or time period.
Layer: Geographic dataset is represented using symbols and labels.
Topology: Physical structures represented as point, line, and polygon describing connectivity, adjacency and continuity.
Map Algebra: Mathematical set theory based algebraic operations used for analysis and manipulation of geographic data.
Database Schema: The representation of entity and attribute in a database is schema.
Diffusion Modeling (Disease Spreading/Transmission): The estimation of disease spread using scientific formulations.
Spatial Statistics/Spatial Analysis: Formal mathematical techniques applied to study properties of spatial features.
Thematic Map: A Layer describing geographic features in a dataset represented using specific theme.
Elevation: The height of a geographic location is represented using contour lines.
Network: Collection of nodes interconnected via communication link.
Entity: Physical object in real world is an entity in database.
Mortality Rate: Death rate of particular community.
Map Clusters: The identification of hotspot, outlier, spatial similarity based on geographic features that are statistically significant. They can be visualized as layers in GIS.
Geographic Information System GIS -: The software that integrates storage, analysis, mapping and visualization of both spatial and a-spatial data of a geographic area.
Spatial Epidemiology: The study of disease spread considering risk factors in behavioral, genetics, environmental etc., in various geographic locations of interest.
Geo-Database: It is organized collection of spatial data in raster and vector format.
Latitude: The angle ranges from 0° at the equator to 90° at the poles.
Cartesian Co-Ordinate: Point coordinate on real axis.
Vector Data: The spatial data stored in the form of point, line and polygon constitute vector data format. Example: shapefiles
Topography: The mapping of earth surface that comprises of hilly terrains, land cover, natural and man-made structures etc.
Cluster Analysis: Grouping of candidate dataset without prior knowledge of its identity.
Communicable Disease: Diseases transmitted from person to person, one organism to another etc.
Longitude: The angle ranges from 0°at Prime meridian to +180° East and -180° West.
Feature Classes: Represent homogenous collection of spatial features in terms of point, line and polygon. For example area of land cover region is represented as polygon feature.
Frequency: The count of number of disease in various categories.
Polygon: The closed path of finite length forms polygon.
Determinant: Factors influencing disease spread.
Infectious Disease: The disorders due to living organism.
Geo-Referencing: It is mapping raster image by associating it with real co-ordinate system.
Incidence: The rate of occurrence of an undesirable medical condition.
Data Base Management System (DBMS): The collection of software program that organizes, manipulates data in database.
Geo-Epidemiology: Study of etiology of diseases.
Prevalence: The phenomenon by which a medical condition found to exists or found to be common.
Heatmap: Density of data points representing geographic feature is analysed using heatmap.
Spatial Data: Geographic location of an existing physical object or constructed structures represented using co-ordinate system is spatial data.
A-Spatial Data: Data that do not represent a geographic location such as color, shape, size, type etc., are a-spatial data.
Map Projection: Transformation in which a point coordinate on earth is projected on a plane.
Features: Set of attributes that describes the spatial object.
Base Map: The map that provides the fundamental information upon which other maps can be built.
Data Model: The way of organizing and representing data is data model.
Buffer: Polygon surrounding a geographic feature in selection is buffer space which is used for proximity analysis.
Hotspot Analysis: Spatial locations identified based on statistical significance measure.
Zonal Operation: Zone in raster data is area of interest in which raster spatial analysis is performed using the cells present in the zone.
Geocoding: It is the process of transforming physical identity of an object to location in point coordinates form.
Distribution: The pattern describing the propagation of disease.
Prognosis: Outcome of disease that could be predicated or likely to occur as expected.
Geometry: Points on plane connected to form different shapes.
Metadata: Data that provides description of existing data.
Attribute Table-Database: Database that contains information about the geographic features.
Overlay: Map overlay is operation performed for relating different geographic features by superimposing more than one dataset with different themes.
Etiology: The study on origin of disease and its causes.
Nearest Neighbor: It is proximity analysis in which nearest neighbor is computed based on distance.
Control Point: The point coordinate on real world that references the geographic features on map.
Node: In network, node is a point that either initiates or terminates communication.
Spatial Autocorrelation: The measure of a phenomenon correlated to itself in geographic space.