Guo et al. The former was obtained by subtracting the water regions unsupervised classification in the dry season image from the wet season image, while the latter was obtained using NDVI and tasseled-cap transformation indices brightness, greenness and wetness index to extract the regions with vegetation coverage in the dry season. Then overlaying the two layers-water difference regions and vegetation coverage-the intersecting regions satisfying the above two features were defined as potential snail habitats[ 52 ].
The same approach was adopted by Yang et al. This method was believed to be a good approach for identifying the potential habitats suitable for snails, but we noted that they also used the environmental indicators from RS images to extract the regions with vegetation coverage and applied the method of unsupervised classification to identify the water regions related to snail habitats. To improve this approach, Zhang et al. Besides, some new methods for classifying RS images were also investigated during this period. For example, Niu et al.
Zhao and Bao predicted the spatial distribution of snail habitats based on the combined datasets of Landsat TM images and GIS thematic data e.
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Dong et al. While Ju et al. Xu et al. They found that NDVI is a sensitive index for assessing disease associations[ 62 ]. Gao et al. Yang et al. This is the first spatial study in the real sense by simultaneously taking into account spatial autocorrelation and predictors in the models. The snail habitats were first extracted based on two indices of NDVI and NDWI suggested by[ 55 , 65 , 68 — 70 ]; then the relationships between the schistosomasis data and the potential risk factors e. Finally, 6 ATS were located by overlaying the above detected high-risk regions of schistosomiasis and the snail habitats extracted from RS images[ 65 ].
This is a promising approach for sustainable control of schistosomiasis.
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From the above review, we can identify four characteristics in the application of RS techniques for disease control in China:. Started late, but developed rapidly. Chinese researchers recognized the possibility of using RS in disease control in , approximately fifteen years later than other international researchers But closer international collaborations have resulted in a fast pace to catch up with the recent progress. RS applications were mainly at the low e.
The high spatial resolutions such as QuickBird and SPOT images are rarely used and microwave imagery is absent, which may be caused by the high cost of obtaining those images. From the early simple analysis methods to the latest research approaches. In the very beginning, researchers only were able to use the basic unsupervised or supervised classification algorithm on RS images to detect different objects on the earth, but now many modern methods such as the fuzzy classification technique and artificial network analysis have been combined into the process of RS classification and spatial data modeling.
RS applications have been extended from single diseases to multiple diseases e. The achievements in using RS in disease control are obvious, but exciting developments are sparse. Here some key problems and challenges are highlighted, which will be discussed with schistosomiasis and malaria as examples.
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Snails are the sole intermediate host of schistosomiasis, hence most RS studies are on snail habitats because its distribution is consistent with that of schistosomiasis to a great degree. It needs to be recognized that the presence of snail habitats is just a necessary condition for schistosomiasis, not a sufficient condition[ 73 ]. But the actual studies are always simplified such as only vegetation and water factors are considered. Snails are the sole intermediate host of schistosomiasis, so the distribution of schistosomiasis is always indicated by snail habitats, which can be detected through the RS-extracted environmental conditions.
But there may be two limitations in such studies[ 75 ]: a there are only fundamental correlations between the snail habitats and schistosomiasis, so the direct causal relationship linking environmental conditions to vector distribution or abundance remains to be established; b schistosomiasis risk is more closely related to the abundance of infected snails, rather than the simple presence of snails, or total abundance of snails. To differentiate the positive snail habitats from negative ones is more important, and the integrated two-step modeling framework proposed by Zhang et al.
In recent years, there has been increasing interest in integrating RS-extracted variables within the process of spatial data modeling to identify high-risk regions of diseases. The disease incidence or prevalence is used to estimate disease risk but there are distinct discrepancies between risk and disease occurrence incidence or prevalence. For instance, the widespread use of preventative measures for malaria e.
For those diseases, four types of region with different implications for disease control could be created: high incidence and high risk, high incidence and low risk, low incidence and high risk, and low incidence and low risk. This distinction has been completely ignored. RS techniques have been widely used for many diseases around the world, such as Lyme disease, paracoccidioidomycosis, ebola fever, hantavirosis, Saint-Louis encephalitis, Rift Valley fever, West Nile virus, dracunculiasis, echinococcosis, fascioliasis, filariasis, leishmaniasis, malaria, trypanosomiasis, schistosomiasis and Vibrio cholera [ 77 — 80 ].
Current RS studies on human health in China are only targeted at detecting the spatial distribution of different objects, such as snail habitats, which are closely related with the disease of interest e.
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The temporal dynamics of the objects determining the disease distribution has been rarely discussed. Public health practitioners always have difficulties in accessing the latest RS images and the related environmental materials, and there is a lack of effective intersectoral collaboration. With respect to high resolution RS images, the costs are too high for routine applications. More importantly, extracting the information from RS images and applying it in studies of disease control needs multidisciplinary techniques such as geography, RS, biology, ecology, computer science, and so on, which are beyond the abilities of most ordinary users or even groups.
In the past, only the traditional RS classification approaches e. All previous RS researches for human health in China have been space-based static studies and have not considered the attribute of time, which may be important for monitoring and forecasting studies. For example, Linthicum et al.
This can be sufficient lead time for decision-makers to take preventive measures and shows encouraging prospect for disease control. As applied users, we only care about how to obtain RS data and how to use it for disease control. As such, we make one key assumption that the obtained RS data is reliable. Very few have ever thought about the issues of RS data quality, although data quality in spatial studies is critical.
The authors concluded that these dataset are not directly inter-changeable[ 89 ]. Nowadays, many different types of RS data can be used either freely or at low costs, but whether the study results will change or even reverse if different RS data was used, how big is the difference, how to adjust for the differences and so on, have not been studied.
In China, only vector-borne diseases have been explored by RS techniques, and schistosomiasis is the most widely studied disease. Extending the experiences and methods of applying RS techniques from vector-borne disease studies to other studies such as water- and soil- borne diseases is very meaningful and should be conducted as early as possible. Presently, RS images with high spatial resolutions are very expensive.
This has prohibited their wide usage and conventional applications. But this situation is changing. This will possibly become the trend for distributing high resolution RS images in the future at least for academic researchers. So exploring their potential benefits for disease control is necessary. Since the low earth orbit Television Infrared Observation Satellite TIROS-1 was launched on April 1, , many satellites have been operated to provide earth observing information, which includes the measurement of land, ocean, clouds, radiation, and trace gases e.
Besides, the natural hazards such as volcanoes can also be observed and monitored through RS images. Without the need for personnel to conduct on-site observations, the endangerment to human life has been minimized. The wide availability of environment- and climate-related data derived from RS images has stimulated a new and promising research direction, studying the relationships between RS-based estimates of air pollution and human health.
For example, Evans et al. Pathogens use many different modes, such as direct contact e. In most cases, the probability of transmission will decline dramatically with distance from an infected host. Hence, the factors affecting the spatial positions of pathogens, hosts and vectors, and their probability of close encounter, are fundamentally important to disease dynamics[ 75 ]. Some factors such as environmental and climatic determinants of transmission are readily available from RS sources[ 93 ], so RS techniques are particularly useful for the study of viral, bacterial and parasitic infections.
These rely on intermediate hosts to complete their life cycles or on vectors for their spread, which are particularly vulnerable to changes in environmental factors such as temperatures, humidity and vegetation. The Market Driven Organization. George S Day. The Genius Is Inside.
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