Remote Sensing for Mitigation of Epidemics (page 1 of 3)
Geographic distribution of vector-borne diseases is mostly determined by environmental factors that condition both the pathogenic agent and its vectors (Manguin and Boussinesq 1999). Over the past three decades, the use of remote sensing techniques in epidemiological studies has evolved significantly due to two factors:
(i) the rapid development in spatial information technologies (remote sensing, Geographical Information Systems and Global Positioning System) and
(ii) the development of disease ecology, a sub-discipline of epidemiology which investigates the biological, physical and anthropogenic links between the environment and disease and consequently accounts for spatial variation in transmission (Graham et al., 2004).
As shown in a number of comprehensive reviews on the use of remote sensing and GIS for epidemiological applications (e.g. Thomson and Connor, 2000; Hay et al., 2000, Kalluri et al., 2007), a growing number of epidemiologists are now taking advantage of the reductions in cost and increases in ease of access.
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The KEEO will acquire data from satellites not currently available in real-time within Egypt, including NASA’s Terra MODIS and Aqua MODIS and the Chinese Space Agency’s Fung Yun 1 D MVISR.
KEEO will join a group of already existing ground-receiving stations in Egypt, some of which are listed here:
- The Egyptian National Authority for Remote Sensing & Space Science (NARSS) Receiving Station at Aswan, Egypt
- The European Space Agency (ESA) Meteosat receiving station in Cairo, Egypt
- The NARSS-managed AVHRR receiving station in Cairo, Egypt
- The NARSS-managed CBERS receiving station in Cairo, Egypt
Remote Sensing Data Sources
KEEO will be a real-time ground-station, ingesting data from multiple satellites which are operated by various countries. These include:
- The Chinese FY-1d satellite which provides MVISR data
- The French earth observation satellite, SPOT
- The European satellite, Meteosat
- The USA NOAA satellite, which has the AVHRR sensor
- The China-Brazil earth resources satellite, CBERS
Remote Sensing Software
The analysis of remote sensing data requires specialized analytical software tools to support scientific research that addresses disaster mitigation and environmental sustainability. This section describes such analytical software tools:
MultiSpec is being developed at the Laboratory for Applications of Remote Sensing (LARS) and the Purdue Terrestrial Observatory (PTO) at Purdue University, West Lafayette, IN, by David Landgrebe and Larry Biehl. It results from an on-going multiyear research effort which is intended to define robust and fundamentally based technology for analyzing multispectral and hyperspectral image data, and to transfer this technology to the user community in as rapid a manner as possible.
The results of the research are implemented into MultiSpec and made available to the user community via the download pages. MultiSpec © with its documentation© is distributed without charge. MultiSpec is copyrighted by Purdue Research Foundation. MultiSpec may not be repackaged and/or sold in any way. MultiSpec is modified periodically, so check the web page for the latest version which may include new features and bug fixes. The latest versions of MultiSpec can be downloaded here.
2) Spatio-Temporal Epidemiological Modeller
The Spatio-Temporal Epidemiological Modeler (STEM) tool is designed to help scientists and public health officials create and use spatial and temporal models of emerging infectious diseases. These models could aid in understanding, and potentially preventing, the spread of such diseases. Policymakers responsible for creating strategies to contain diseases and prevent epidemics need an accurate understanding of disease dynamics and the likely outcomes of preventive actions.
In an increasingly connected world with extremely efficient global transportation links, the vectors of infection can be quite complex. STEM facilitates the development of advanced mathematical models, the creation of flexible models involving multiple populations (species) and interactions between diseases, and a better understanding of epidemiology. The STEM application has built in Geographical Information System (GIS) data for almost every country in the world. It comes with data about country borders, populations, shared borders (neighbors), interstate highways, state highways, and airports. This data comes from various public sources.
STEM is designed to make it easy for developers and researchers to plug in their own models. It comes with spatiotemporal Susceptible/Infectious/Recovered (SIR) and Susceptible/Exposed/Infectious/Recovered (SEIR) models pre-coded with both deterministic and stochastic engines. The parameters in any model are specified in XML configuration files. Users can easily change the weight or significance of various disease vectors (such as the weights of highways, shared borders, airports, etc). Users can also create their own unique vectors for disease.
Further details are available in the user manual and design documentation. Download and installation information is provided here. Once STEM is installed on the computer, a sample scenario gives more information about how diseases are modeled in STEM: get scenario here.