Disease Surveillance and Monitoring using GIS Precipitation Precipitation, especially in the form of rainfall, can affect disease transmission via the effects of normal, as well as severe (i.e., flooding and drought), events on vector populations. Flooding can influence disease transmission in a number of ways, most notably by increasing run-off and disturbing breeding grounds and habitats (Clark, 1993). Wind and ocean currents Sea-surface temperature, height, and concentration of nutrients in seawater are associated with waterborne diseases. Ocean currents and tides are connected with various epidemiological patterns (Colwell, 1996). Human population movement Human population movement (HPM) is a term that encompasses a variety of ways that people travel from one area to another. Population movement has historically contributed to the spread of many infectious diseases that have left their mark on human growth and progress. Humans travel for a variety of reasons and causes. The understanding of these factors is the first stage in controlling the development and spread of communicable diseases. These various factors include push and pull factors, circulation, temporal dimensions, spatial dimensions and migration. Depending on these factors the infectious diseases are categorized and the transmission settings and the vulnerable groups are tabulated in Table I. Circulation Daily: Leaving place of residence for up to 24 hours (e.g. commuting, trading, and cultivation) Periodic: Period varies from 1 day to 1 year but usually of shorter duration than seen in seasonal circulation (e.g. trading, pilgrimage, mining, and tourism). Seasonal: Period defined by marked seasonality in the physical or economic environment (e.g. fishing, laboring, and pastoralism). Long-term: Absence from place of residence for longer than 1 year (e.g. urbanization, colonization, and traders). Migration: Long-term: Population movement resulting in a permanent change of residence. (e.g. urbanization, refugees, and colonization).   
Case Study This case aims at finding out the population that is vulnerable to vector borne disease in Birla Institute and Technology and Science, Pilani (BITS) campus. The digital map of the campus is taken and the basic operations such as Geo referencing, digitizing, etc. are carried out. The different hostels, institute, staff quarters, wells, market places were digitized as a polygon theme and the road network was also digitized as a separate theme. The first phase utilizes landscape epidemiology to explore the relationship between landscape elements and the vectors breeding sites. The goal of this phase was to assess the capabilities of GIS and remote sensing to identify high vector breeding sites. The approach utilizes landscape composition methods. Using remotely sensed data to distinguish between different landscapes elements, it was determined that dairies, stagnating water, areas of vegetation had the highest vector abundance. The vectors must find larval habitats, blood meal sources and resting sites within a 1-km radius, in order to successfully reproduce. In the second phase GIS was used to determine the landscape composition of a 1-km buffer around each site. Using stepwise discriminant and regression analyses, it was determined that the whole BITS campus was vulnerable to the vector disease. The only restriction is that the climatic factors influence the breeding of the vectors. The climatic conditions of Pilani don’t favour many vectors to survive. |