Disaster Cycle Phase:
Depending on the research question or the public health application, the appropriate resolution of the data varies temporally, spatially, and, for satellite data, spectrally and radiometrically. Regardless of the scale used to address a research or public health question, the temptation is always there to extrapolate from fine-resolution data or to interpolate from coarse resolution studies. In both cases, the relevance of data and analyses conducted on one spatial level to other levels cannot be taken for granted. Spatial heterogeneity on the micro-scale may not be detected using coarse spatial resolution, and conversely, general patterns on the macro-scale may not be detected using fine spatial resolution. Two studies are described where the transmission dynamics and risk of infection was assessed on the micro-scale starting with household level studies in one community, and the study area was extended gradually to consider several communities and sources for vectors or intermediate hosts. In a study of Chagas disease in northwest Argentina, the reinfestation process of communities by the main domestic vector was analyzed using spatial statistics; sources within and outside communities as well as the distance of reinfestation were identified. In a study of urinary schistosomiasis in coastal Kenya, age dependent and directional focal clustering of infections was detected around some aquatic habitats, and a hydrological model was developed to detect least cost dispersal routes that allow snails to reinfest dried-up habitats. Some general aspects of focal statistics are discussed. Several general questions need to be considered in geospatial health studies, including the following: (i) what are the best criteria for selecting the spatial (and temporal) unit of intervention and analysis? (ii) how do the key measures of risk and transmission dynamics vary with scale? (iii) how do we integrate processes occurring at diverse spatial and temporal scales? All of these questions can only be addressed through solid biological, epidemiological and socio-economic understanding of the system in time and space.
Kitron, U. et al. (2006): Upscale or Downscale: Applications of Fine Scale Remotely Sensed Data to Chagas Disease in Argentina and Schistosomiasis in Kenya. Geospatial Health, Vol. 1, 49-58.