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Figure from:Human Health: Ecosystem Regulation of Infectious Diseases

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indoor resting Anopheles subpictus Grassi in a new irrigation project in S Lanka. Journal of Medical Entomology, 29, 577-581. References

Table 5 indoor resting Anopheles subpictus Grassi in a new irrigation project in S Lanka. Journal of Medical Entomology, 29, 577-581. References

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4 Disability-adjusted life year: years of healthy life lost, a measure of disease burden for the gap between actual health of a population compared with an ideal situation where everyone lives in full health into old age. (WHO World Health Report 2004) Table 14.1. Burden of Infectious and Parasitic Disease in 2003, by WHO Region and Mortality Stratum. Mortality stratum is a way of dividing up the WHO regions, which are based on geography, into units which are more similar in terms of health performance (i.e., separating Australia, Japan, and New Zealand out from China, the Philippines, and others in the Western Pacific Region, and Canada, the United States, and Cuba from the rest of the Americas, where health status is poorer). They are based on WHO estimates of adult and child mortality, with some arbitrary threshold to group them into different classes (see second column in the table). The data are based on nationally reported health statistics, although there is sometimes some estimation by WHO if national statistics are poor or non-existent. (WHO 2004)
4 Disability-adjusted life year: years of healthy life lost, a measure of disease burden for the gap between actual health of a population compared with an ideal situation where everyone lives in full health into old age. (WHO World Health Report 2004) Table 14.1. Burden of Infectious and Parasitic Disease in 2003, by WHO Region and Mortality Stratum. Mortality stratum is a way of dividing up the WHO regions, which are based on geography, into units which are more similar in terms of health performance (i.e., separating Australia, Japan, and New Zealand out from China, the Philippines, and others in the Western Pacific Region, and Canada, the United States, and Cuba from the rest of the Americas, where health status is poorer). They are based on WHO estimates of adult and child mortality, with some arbitrary threshold to group them into different classes (see second column in the table). The data are based on nationally reported health statistics, although there is sometimes some estimation by WHO if national statistics are poor or non-existent. (WHO 2004)
Figure 14.2. Relationships between Society, Ecosystem Services, and Human Infectious Diseases
Figure 14.2. Relationships between Society, Ecosystem Services, and Human Infectious Diseases
Increasingly in recent years, meteorological satellite data have been used to help model the spatial and seasonal dynamics of dis- ease transmission and develop early warning systems (Connor et al. 1998). These relatively low cost and easy-to-use data sources have become familiar to public health services in Africa. For ex- ample, environmental data indicating areas at risk of malaria epi- demics are beginning to be routinely incorporated into the WHO/UNICEF-supported disease surveillance software Health- Mapper that is widely used by ministries of health (WHO 2001). Ecological niche modeling is an approach used in biogeogra- phy to predict the distributional range of species from existing occurrence data (Anderson et al. 2003). Using genetic algorithms as a decision tool in a GIS containing layers of environmental information (such as topography, climate, and vegetation), epide- miological and spatial risk stratification can be achieved from data on the location of vectors or pathogens. This approach has been successfully used in the case of Chagas disease and for vectors of leishmaniasis and filovirus infections (Peterson et al. 2002, 2004a, 2004b).
Increasingly in recent years, meteorological satellite data have been used to help model the spatial and seasonal dynamics of dis- ease transmission and develop early warning systems (Connor et al. 1998). These relatively low cost and easy-to-use data sources have become familiar to public health services in Africa. For ex- ample, environmental data indicating areas at risk of malaria epi- demics are beginning to be routinely incorporated into the WHO/UNICEF-supported disease surveillance software Health- Mapper that is widely used by ministries of health (WHO 2001). Ecological niche modeling is an approach used in biogeogra- phy to predict the distributional range of species from existing occurrence data (Anderson et al. 2003). Using genetic algorithms as a decision tool in a GIS containing layers of environmental information (such as topography, climate, and vegetation), epide- miological and spatial risk stratification can be achieved from data on the location of vectors or pathogens. This approach has been successfully used in the case of Chagas disease and for vectors of leishmaniasis and filovirus infections (Peterson et al. 2002, 2004a, 2004b).