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Geographic Information Systems for the study and control of infectious diseases
Data and Files Required for a GIS and their Sources
A GIS contains four types of information and computer files: geographic, map, attribute, and data-point files. In general, modeling involves the integration of GIS with standard statistical and health science methods. Spatial interaction models analyze and predict the movements of people, information, and goods from place to place. By accurately modeling these movements, it is possible to identify areas most at risk for disease transmission and thus target intervention efforts. Spatial diffusion models analyze and predict the spread of phenomena over space and time and have been widely used in understanding spatial diffusion of diseases. By incorporating a temporal dimension, these models can predict how diseases spread, spatially and temporally, from infected to susceptible people in an area. Spatial variation in health related data is well known, and its study is a fundamental aspect of epidemiology. Representation and identification of spatial patterns play an important role in the formulation of public health policies. Some of the graphic and exploratory spatial data analytic techniques are:
- Point Patterns: As the name implies, also known as dot maps, attempt to display the distribution of health events as data locations. The ability to overlay data locations with other relevant spatial information is a general tool of considerable power. It is useful for delimiting areas of case occurrences, identification of contaminated environmental sources, visual inspection of spatial clusters, and analyzing health care resources distribution. A classical example of point pattern analysis in epidemiology is the identification of the source of cholera spread in London.
- Line Patterns: Vectors or lines are graphic resources that aid in the analysis of disease diffusion and patient-to-health care facilities flow. In their simplest form, lines indicate the presence of flow or contagion between two subregions which may or may not be contiguous. Arrows with widths proportional to the volume of flow between areas are important tools to evaluate the health care needs of different locations. Use of line pattern analysis is quite common in epidemiology to describe the diffusion of several epidemics, such as the international spread of AIDS.
- Area Patterns: The first stage of data analysis is to describe the available data sets through tables or one-dimensional graphics, such as the histogram. For spatial analysis, the obvious option is to present data on maps, with the variable of interest divided into classes or categories, and plotted using colours or hachures within each geographic unit, know as a choropleth map. The use of stem-and-leaf plots to classify data before area pattern analysis is more intuitive, easier to use and presents another method of incorporating dynamic graphics into GIS for use.
- Surface and Contour Patterns: Data of epidemiological or public health interest often occur as spatial information during each of several time epochs. The analytical techniques described previously require the pooling of information in administrative areas with well-defined geographic boundaries, and the presentation of the spatial process with maps constrained to them.
- Statistical Monitoring: A common measure used by epidemiologists to identify increases in case occurrence of diseases, is the ratio of case numbers at a particular time to past case occurrence using the mean or median.
- Time Series Analysis: The common analytical framework uses time series models to forecast expected numbers of cases, followed by comparison with the actual observation. Detection of changes from historical patterns through forecast error uses the difference between the actual and estimated values at each point in time. In contrast to other monitoring schemes, time series methods use the correlation structure of the data at different time intervals in making estimates.
- Temporal Cluster Analysis: Detection of temporal clusters, understood as a change in the frequency of disease occurrence, is important to stimulate research into the causes, and to encourage the development of preventive strategies. Detection of increases in the rate of occurrence of a disease uses either the time interval of successive events, or the number of events on specified time intervals.
- Spatio -Temporal Analytic Techniques: Space-time interaction among health events or between health events and environmental variables is as an important component for epidemiological studies and public health surveillance. The bulk of the development in spatio-temporal patterns of health problems has been based on modeling and simulation because of the paucity of available data sets.
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