GIS and Remote Sensing: A Cutting Edge technology for Epidemiology

 

 

An epidemic is a disease that spreads rapidly among many people in a community at the same time. A pandemic is an outbreak of global proportions. It happens when a novel virus emerges among humans – it causes serious illness and is easily human transmissible (spreads easily from person-to-person). The word pandemic comes from the Greek pandemos meaning "pertaining to all people". The Greek word pan means "all" and the Greek word demos means "people". A pandemic covers a much wider geographical area, often worldwide. A pandemic also infects many more people than an epidemic. An epidemic is specific to one city, region or country, while a pandemic goes much further than national borders. A pandemic is usually caused by a new virus strain or subtype – a virus human either have no immunity against, or very little immunity. If immunity is low or non-existent the virus is much more likely to spread around the world if it becomes easily human transmissible. Pandemics generally cause much higher numbers of deaths than epidemics. The social disruption, economic loss, and general hardship caused by a pandemic are much higher than what an epidemic can cause. An epidemic is when the number of people who become infected rises well beyond what is expected within a country or a part of a country. When the infection takes place in several countries at the same time it then starts turning into a pandemic.

 

 

 

 

 

 

 

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.

 

 

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.

 

 

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.

 

Summary

 

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.