GIS AND REMOTE SENSING: A CUTTING EDGE TECHNOLOGY FOR EPIDEMIOLOGY
Risk and Impact
Pandemics have occurred throughout history and appear to be increasing in frequency, particularly because of the increasing emergence of viral disease from animals.
Pandemic risk is driven by the combined effects of spark risk (where a pandemic is likely to arise) and spread risk (how likely it is to diffuse broadly through human populations).
Probabilistic modeling and analytical tools such as exceedance probability (EP) curves are valuable for assessing pandemic risk and estimating the potential burden of pandemics.
Pandemics can cause significant, widespread increases in morbidity and mortality and have disproportionately higher mortality impacts on LMICs.
Pandemics can cause economic damage through multiple channels, including short-term fiscal shocks and longer-term negative shocks to economic growth.
Individual behavioral changes, such as fear-induced aversion to workplaces and other public gathering places are a primary cause of negative shocks to economic growth during pandemics.
Some pandemic mitigation measures can cause significant social and economic disruption.
In countries with weak institutions and legacies of political instability, pandemics can increase political stresses and tensions. In these contexts, outbreak response measures such as quarantines have sparked violence and tension between states and citizens
Role of GIS and Remote Sensing
Geographic information systems (GIS) and related technologies like remote sensing are increasingly used to analyze the geography of disease, specifically the relationships between pathological factors (causative agents, vectors and hosts, people) and their geographical environments. Imageries from remote sensing satellites can be used to describe the sources and geographical distributions of disease agents, identified regions in time and space where people may be exposed to environmental and biological agents, and mapped and analyzed spatial and temporal patterns in health outcomes. Although GIS show great promise in the study of disease, their full potential will not be realized until environmental and disease surveillance systems are developed that distribute data on the geography of environmental conditions, disease agents, and health outcomes over time based on user-defined queries for user-selected geographical areas.
GIS has in-built facilities of conventional and the scientific knowledge of traditional, fundamental concepts of formal mapping with signs and symbols, variety of colours, shades, lines and poly lines, patterns, etc. GIS has computer-aided designs, symbols and colours for thematic mapping or customised mapping, and perhaps, embedding mapping facilities, overlay analysis, cluster analysis, nearest neighborhood analysis, pattern recognition, temporal analysis, interpolation of point data (Kriging, Co-Kriging, Universal Kriging), spatial correlation, fussy analysis, linear determinant analysis, the probability of minimum and maximum likelihood analysis etc., of geospatial analysis of thematic information. Thus, remote sensing and GIS could be used for mapping and studying and analysing the information relevant to the epidemics or pandemics with reference to space and time.
GPS for epidemic surveillance
Integrated remote sensing and GPS under the GIS umbrella is also used for disease surveillance and disease epidemic
control. GPS is used directly on top of a map for site specific location to collect field data in real time, convert and log real-time GPS coordinates. It assists field surveys to collect information continuously and to automatically update the geographic coordinates with minimum 500 points. GPS Geo-coding allows collecting and attributing field data directly into one’s geospatial database engine (GIS software) in real time. GPS can map the vector’s breeding habitats with site specifications including house locations, streets, house type, and locality of the areas.
Mapping point data and interpolate contour surface
GIS is used for mapping epidemiological data and for spatial interpolation of data for areas where data is not available or un-surveyed. The disease rate is mapped initially with graduated point symbols for the areas whose data is collected and then the interpolation of the contour surface is created for predicting the disease rate for the unsurvey areas. Thus it can map the disease infection in vast areas, with or without availability of epidemiological data and can be used in action plan for implementing disease surveillance, management of disease control programmes and for disease management.
Real time mapping and structured querying
GIS facilitates for structured querying and decision making process at certain level. The structured spatial queries relevant to demographic features, disease prevalence, environmental aspects and the socio-economic risk factors have been provided the diffusion of disease transmission, and hence, the action plan for the disease control operations can be taken to prevent the disease epidemics in countries. The web mapping API are becoming important, mainly the embed customized web mapping GIS which has user interface facilities for browsing, querying, and table sorting and drawing the disease epidemiological information in different part of the countries.
Spatial prediction of disease transmission
The geostatistical analysis of remote sensing and climate, geo-environmental variables, the spatial models provides significant and reliable results and the guidelines of algorithms for predicting the people of community at risk of disease transmission with reference to space and time. Different models can be developed encompassing various parameters like altitude, temperature, rainfall, relative humidity etc... , to derive certain disease indices and finally construct the disease map. The visual interpretation of the multi spectral and multi temporal satellite sensors data products derived from the earth observation resource satellites and the meteorological satellites can be used for mapping the breeding habitats and the range of NDVI values derived from the satellite data are highly significant with the vector abundance and the spatial occurrences of vector borne diseases. The vectors and vector borne diseases have been directly controlled by the geo-climatic variables (altitude, mean annual temperature, mean annual rainfall, potential evapotranspiration, readily available soil moisture, soil types and water logging potential, and terrain slope, and the normalized difference vegetation index (NDVI) of space-borne remote sensing data.
Optimum health service coverage
The spatial clustering, nearest neighborhood analysis can be performed for easy understanding the spatial pattern and disease clustering and the spatial ring buffering provide optimum service coverage of the patients. The different distance rule can be created over the disease distribution map, using spatial ring buffering technique at GIS platform. The minimum, maximum and the mean distances of each disease cluster are then calculated against to each distance rule/ ring buffering. This help to open up self-help health service centers throughout the country with better public health management.
The gradual change in climate due to global warming are bound to alter the ecology, habits and habitats of hosts, agent and vectors and so drastically that incidences of vector and waterborne diseases are bound to increase in endemic areas. Further, these diseases may expand the geographical range and new epidemic and pandemic of these diseases may occur in areas previously free of disease. So there is need of (1) construction of potential risk areas maps based on conditions favorable to vector proliferation of exotic diseases before their entry in new country, (2) development of early warning system before harmful exposures, (3) improved prevention initiative targeting ingress of risk, (4) reduction of environmental-related diseases, and (5) improvement in bio-terrorism event information management. Remote sensing and GIS are becoming essential in public health for mapping the geographical aspects of vector borne diseases, bio-diversity of vectors, viral diseases, parasites, bacterial diseases and studying the geo-environmental risk factors associated with disease occurrences.It is being used not only to map the spatial distribution of disease prevalence but is becoming an essential tool for disease surveillance, epidemiological research, and providing the guidelines for decision making.