An essential need: creating opportunities for veterinary students and graduates to gain an appreciation of responsibilities and opportunities in global veterinary issues.

Globalisation trends and bioterrorism issues have led to new concerns relating to public health, animal health, international trade and food security. There is an imperative to internationalise and strengthen global public health capacity by renewed emphasis on veterinary public health in veterinary education and increasing opportunities for elective experiential learning in public practice programmes for veterinary students. Recent experience with a US-Brazil Higher Education Consortia Program is used as an example of potential ways in which veterinary students can gain an appreciation for global veterinary issues.

Biology-based mapping of vector-borne parasites by geographic information systems and remote sensing.

Application of growing degree day-water budget analysis and satellite climatology to vector-borne parasites will be reviewed to demonstrate the value of using the unique thermal-hydrological preferences and limits of tolerance of individual parasite-vector systems to define the environmental niche of disease agents in the landscape by modern geospatial analysis methods. Applications of geospatial modeling will be illustrated by examples on fascioliasis, malaria, leprosy and leishmaniasis.

Biology-based mapping of vector-borne parasites by Geographic Information Systems and Remote Sensing.

Applications of growing degree day-water budget analysis and satellite climatology to vector-borne parasites are reviewed to demonstrate the value of using the unique thermal-hydrological preferences and limits of tolerance of individual parasite-vector systems to define the environmental niche of disease agents in the landscape by modern geospatial analysis methods.

Use of Geographic Information Systems in the development of prediction models for onchocerciasis control in Ethiopia.

A risk assessment model was developed for onchocerciasis distribution and its control in Ethiopia using Geographic Information System (GIS) methods. GIS data analysis was done to generate 3 separate risk models using selected environmental features of (1) earth observing satellite data on Normalized Difference Vegetation Index (NDVI) and midday Land Surface Temperature (LST) prepared from AVHRR sensor data of the Global land 1-km project for the years 1992 and 1995, (2) FAO agroclimatic databases from the Crop Production System Zone (CPSZ) of the Intergovernmental Authority on Development (IGAD) sub-region of East Africa, and (3) a climate-based forecast index based on the growing degree days (GDD) and water budget concepts. Parasitological data used for the analysis were published and unpublished reports of onchocerciasis surveillance made between 1969 and 2000 in various parts of the country. Analysis of queries based on 1992 and 1995 annual wet and dry season data of the Global land 1-km project resulted in a predictive value of 95.1%, 94.0% and 96.3%, respectively, using data values extracted from buffers centered on sites above 5% prevalence. The model based on CPSZ data predicted an endemic area that best fit the distribution of sites over 5% prevalence; the query was based on CPSZ values of average altitude (442-2134 m), annual mean temperature (18-28 degrees C), annual rainfall (822-1980 mm), annual potential evapotranspiration (1264-1938 mm), rain minus potential evapotranspiration (-124 - 792 mm), average NDVI x 100 (2000-5000) and average terrain percent slope (9-34). The climate-based model based on GDD and water-budget predicted high risk to severe risk areas in the western and southwestern parts of the country. All three of the models predicted suitable areas for the transmission of onchocerciasis outside known endemic areas, suggesting the need for ground-based validation and potential application to current community-directed treatment programs with ivermectin (CDTI) for control of onchocerciasis in Ethiopia.

Modeling the distribution of Schistosoma mansoni and host snails in Uganda using satellite sensor data and Geographical Information Systems.

The potential value of MODIS satellite sensor data on Normalized Difference Vegetation Index (NDVI) and land surface temperatures (LST) for describing the distribution of the Schistosoma mansoni-"Biomphalaria pfeifferi"/Biomphalaria sudanica parasite-snail system in inland Uganda, were tested by developing annual and seasonal composite models, and iteratively analysing for their relationship with parasite and snail distribution. The dry season composite model predicted an endemic area that produced the best fit with the distribution of schools with > or =5% prevalence. NDVI values of 151-174, day temperatures of 26-36 degrees C, and night temperatures of 15-20 degrees C were used as criteria for the prediction model. Using the same approach with host snail data indicated that most of Uganda is suitable "B. pfeifferi"/B. sudanica habitat, except for possibly the north-eastern region of the country. The parasite, however, appears to be restricted in its distribution in both the north-eastern and the south-western regions of Uganda. The absence of disease in the south-west can not be attributed to the absence of snail hosts. Results suggest a combination of satellite sensor data on temperature and standard climate data on precipitation, as the best ecological determinants of the S. mansoni-"B. pfeifferi"/B. sudanica system. Satellite composite models and logistic regression analysis, suggest low night time temperature as one of the significant factors inhibiting S. mansoni transmission in the south-western highland areas of Uganda. The developed models are, however, unique, representing species-specific ecologic preferences of the S. mansoni-"B. Pfeifferi"/B. sudanica system in inland Uganda. Further validation studies are needed to test the value of the model in other countries in East Africa.

Geographical Information Systems risk assessment models for zoonotic fascioliasis in the South American Andes region.

The WHO recognises Fasciola hepatica to be an important human health problem. The Andean countries of Peru, Bolivia and Chile are those most severely affected by this distomatosis, though areas of Ecuador, Colombia and Venezuela are also affected. As part of a multidisciplinary project, we present results of use of a Geographical Information Systems (GIS) forecast model to conduct an epidemiological analysis of human and animal fasciolosis in the central part of the Andes mountains. The GIS approach enabled us to develop a spatial and temporal epidemiological model to map the disease in the areas studied and to classify transmission risk into low, moderate and high risk areas so that areas requiring the implementation of control activities can be identified. Current results are available on a local scale for: (1) the northern Bolivian Altiplano, (2) Puno in the Peruvian Altiplano, (3) the Cajamarca and Mantaro Peruvian valleys, and (4) the Ecuadorian provinces of Azuay, Cotopaxi and Imbabura. Analysis of results demonstrated the validity of a forecast model that combines use of climatic data to calculate of forecast indices with remote sensing data, through the classification of Normalized Difference Vegetation Index (NDVI) maps.

Seasonal dynamics of Fasciola hepatica burdens in grazing Timahdit sheep in Morocco.

Seasonal transmission of Fasciola hepatica was observed in sentinel sheep and the dynamics of the snail intermediate host, Lymnaea truncatula, was followed over a 3-year study period in the Middle-Atlas mountains in Morocco. High fluke burdens were recorded in both lambs and ewes in the fall and winter, suggesting that transmission occurred in late spring. Fluke burdens ranged from one to 302 in ewes and from one to 345 in lambs. Infections with 200 or more flukes were always fatal. A unique feature of this study was the annual cyclical fluctuation of the fluke burdens. Burdens reached maximum levels during the winter and then declined to low numbers by late spring and summer. This suggested self-regulation which may be dependent on breed resistance or may be related to forage factors, including lack of forage (nutritional stress). Snail populations were cyclical and correlated with fluke transmission as observed in the sentinel sheep. The weather was observed to affect the snail populations which in turn limited fluke transmission.

Use of LANDSAT MSS imagery and soil type in a geographic information system to assess site-specific risk of fascioliasis on Red River Basin farms in Louisiana.

A geographic information system (GIS) was constructed in an ERDAS environment using maps of soil types from the USDA Soil Conservation Service, LANDSAT satellite multispectral scanner data (MSS), boundaries for 25 study farms, and slope and hydrologic features shown in a two-quadrangle (USGS, 7.5') area in the Red River Basin near Alexandria, Louisiana. Fecal sedimentation examinations were done in the fall of 1989, spring of 1990, and fall and winter of 1990-1991 on 10-16 random samples per herd. Fecal egg shedding rates for F. hepatica ranged from 10-100% prevalence and 0.3-21.7 eggs per two grams of feces (EP2G). For Paramphistomum spp., a rumen fluke also transmitted by F. bulimoides but not affected by flukicides, egg shedding rates ranged from 10-91% prevalence and 0.1-42.8 EP2G. Soil types present ranged from sandy loams to hydric, occasionally flooded clays. Herd Paramphistomum spp. egg shedding rates increased with the proportion of hydric clays present, adjusted for slope and major hydrologic features. F. hepatica infection intensity followed a similar trend, but were complicated by differing treatment practices. Results suggest that earth observation satellite data and soil maps can be used, with an existing climate forecast based on the Thornthwaite water budget, to develop a second generation model that accounts for both regional climate variation and site-specific differences in fascioliasis risk based on soils prone to snail habitat.

Geographic information systems and the environmental risk of schistosomiasis in Bahia, Brazil.

A geographic information system was constructed using maps of regional environmental features, Schistosoma mansoni prevalence in 30 representative municipalities, and snail distribution in Bahia, Brazil to study the spatial and temporal dynamics of infection and to identify environmental factors that influence the distribution of schistosomiasis. Results indicate that population density and the duration of annual dry period are the most important determinants of prevalence of schistosomiasis in the areas selected for study. Maximum rainfall, total precipitation during three consecutive months, annual maximum or minimum temperatures, and diurnal temperature difference were not shown to be significant factors influencing S. mansoni prevalence in local populations or distribution of snail hosts. Prevalence of the disease was highest in the coastal areas of the state. Higher prevalence tended to occur in areas with latossolo soil type and transitional vegetation.

Temperature data from satellite imagery and the distribution of schistosomiasis in Egypt.

Polar orbiting environmental satellites operated by the National Oceanographic and Atmospheric Administration acquire daytime and nighttime thermal infrared measurements of the earth's surface around the world at a spatial resolution of 1.1 km. Day-night pairs of this imagery from the Advanced Very High Resolution Radiometer (AVHRR) were processed to produce temperature maximum, temperature minimum, and diurnal temperature difference (dT) maps of the lower Nile River valley. Nile delta subsets of the dT maps for August 16, 1990 and February 14, 1991 were analyzed in detail. Values of dT at specific locations were derived using the median of 5 x 5 pixels centered on the latitude and longitude of 41 survey sites listed in 1935, 1983, and 1990 schistosomiasis surveys of the Nile Delta. A Spearman correlation coefficient matrix revealed an inverse relationship between site dT values for August 16, 1990 and February 14, 1991 and prevalence of Schistosoma mansoni in the 1935 and 1983 surveys. For S. haematobium, a positive association of site dT values and prevalence was seen for 1935 only. A significant association was observed between 1935 S. mansoni prevalence and that observed in 1983 and 1990; S. haematobium prevalence in 1935 was not correlated with the later surveys. The results suggest that AVHRR thermal difference maps reflect regional hydrologic conditions that can be used as a predictor of environmental risk of schistosomiasis for control program management.

New tools: potential medical applications of data from new and old environmental satellites.

The last 40 years, beginning with the first TIROS (television infrared observational satellite) launched on 1 April 1960, has seen an explosion of earth environmental satellite systems and their capabilities. They can provide measurements in globe encircling arrays or small select areas, with increasing resolutions, and new capabilities. Concurrently there are expanding numbers of existing and emerging infectious diseases, many distributed according to areal patterns of physical conditions at the earth's surface. For these reasons, the medical and remote sensing communities can beneficially collaborate with the objective of making needed progress in public health activities by exploiting the advances of the national and international space programs. Major improvements in applicability of remotely sensed data are becoming possible with increases in the four kinds of resolution: spatial, temporal, radiometric and spectral, scheduled over the next few years. Much collaborative research will be necessary before data from these systems are fully exploited by the medical community.

Use of satellite remote sensing and geographic information systems to model the distribution and abundance of snail intermediate hosts in Africa: a preliminary model for Biomphalaria pfeifferi in Ethiopia.

Geographic information system (GIS) risk models for the snail-borne diseases caused by Schistosoma spp. and Fasciola spp. have recently been developed based on climate and satellite-retrieved data on temperature and vegetation coverage. By using these models, it was possible to describe a relationship between vegetation index (Normalized Differences Vegetation Index (NDVI)), land surface temperature (T(max)) and disease prevalence, but little reference was made to the distribution of the corresponding intermediate host snail. Presence of the intermediate host snail is a key factor determining distribution of the disease in sub-Saharan Africa and a good snail distribution mode would probably mirror the endemic area of schistosomiasis. In the present analysis, it was shown that snail distribution data corresponds with schistosomiasis prevalence data in relation to a forecast model based on NDVI and T(max) data derived from the Advanced Very High Resolution Radiometer (AVHRR) onboard the National Oceanic and Atmospheric Administration satellite series. The 'best fit' model included NDVI values from 125 to 145 and a T(max) data range of 10-32 degrees C. This model included 92.3, 90.4 and 94.6% of the positive snail sample sites in GIS query overlay areas extracted from annual, dry season and wet season composite maps, respectively. For other sites in Africa, other NDVI and T(max) ranges may be more appropriate, depending on the species of snail present, a topic that will be examined in further studies.

Validation of a mapping and prediction model for human fasciolosis transmission in Andean very high altitude endemic areas using remote sensing data.

The present paper aims to validate the usefulness of the Normalized Difference Vegetation Index (NDVI) obtained by satellite remote sensing for the development of local maps of risk and for prediction of human fasciolosis in the Northern Bolivian Altiplano. The endemic area, which is located at very high altitudes (3800-4100 m) between Lake Titicaca and the valley of the city of La Paz, presents the highest prevalences and intensities of fasciolosis known in humans. NDVI images of 1.1 km resolution from the Advanced Very High Resolution Radiometer (AVHRR) sensor on board the National Oceanic and Atmospheric Administration (NOAA) series of environmental satellites appear to provide adequate information for a study area such as that of the Northern Bolivian Altiplano. The predictive value of the remotely sensed map based on NDVI data appears to be better than that from forecast indices based only on climatic data. A close correspondence was observed between real ranges of human fasciolosis prevalence at 13 localities of known prevalence rates and the predicted ranges of fasciolosis prevalence using NDVI maps. However, results based on NDVI map data predicted zones as risk areas where, in fact, field studies have demonstrated the absence of lymnaeid populations during snail surveys, corroborated by the absence of the parasite in humans and livestock. NDVI data maps represent a useful data component in long-term efforts to develop a comprehensive geographical information system control program model that accurately fits real epidemiological and transmission situations of human fasciolosis in high altitude endemic areas in Andean countries.

Satellite climatology and the environmental risk of Schistosoma mansoni in Ethiopia and east Africa.

Annual and seasonal composite maps prepared from the normalized difference vegetation index (NDVI) and earth surface maximum temperature (T(max)) satellite data from the archives of the Global land 1-km program of the United States Geological Survey (USGS) were studied for. their potential value, using geographic information system (GIS) methods, as surrogates of climate data in the development of environmental risk models for schistosomiasis in Ethiopia. Annual, wet season and dry season models were developed and iteratively analyzed for relationships with Schistosoma mansoni distribution and infection prevalence rates. Model-predicted endemic area overlays that best fit the distribution of sites with over 5% prevalence corresponded to values of NDVI 125-145 and T(max) 20-33 degrees C in the annual composite map, NDVI 125-145 and T(max) 18-29 degrees C for the wet season map, and NDVI 125-140 and T(max) 22-37 degrees C for the dry season map. The model-predicted endemic area was similar to that of a prior model developed using an independent agroecologic zone data set from the United Nations Food and Agriculture Organization (FAO). Results were consistent with field and laboratory data on the preferences and limits of tolerance of the S. mansoni-Biomphalaria pfeifferi system. Results suggest that Global 1-km NDVI and T(max), when used together, can be used as surrogate climate data for development of GIS risk assessment models for schistosomiasis. The model developed for Ethiopia based on global 1-km satellite data was extrapolated to a broader area of East Africa. When used with FAO agroecologic zone climate data limits of <27 degrees C for average annual mean temperature and annual moisture deficits (annual rain-annual potential evapotranspiration) of <-1300 mm, the model accurately represented the regional distribution of the S. mansoni-B. pfeifferi system in the East Africa extrapolation area.

Use of thermal and vegetation index data from earth observing satellites to evaluate the risk of schistosomiasis in Bahia, Brazil.

A geographic information system (GIS) was constructed using maps of regional agroclimatic features, vegetation indices and earth surface temperature data from environmental satellites, together with Schistosoma mansoni prevalence records from 270 municipalities including snail host distributions in Bahia, Brazil to study the spatial and temporal dynamics of infection and to identify environmental factors that influence the distribution of schistosomiasis. In an initial analysis, population density and duration (months) of the annual dry period were shown to be important determinants of disease. In cooperation with the National Institute of Spatial Research in Brazil (INPE), day and night imagery data covering the state of Bahia were selected at approximately bimonthly intervals in 1994 (six day-night pairs) from the data archives of the advanced very high resolution radiometer (AVHRR) sensor of the National Oceanic and Atmospheric Administration (NOAA)-11 satellite. A composite mosaic of these images was created to produce maps of: (1) average values between 0 and +1 of the normalized difference vegetation index (NDVI); and (2) average diurnal temperature differences (dT) on a scale of values between 0 and 15 degrees C. For each municipality, NDVI and dT were calculated for a 3x3 pixel (9 km(2) area) grid and analyzed for relationships to prevalence of schistosomiasis. Results showed a statistically significant relationship of prevalence to dT (rho=-0.218) and NDVI (rho=0.384) at the 95% level of confidence by the Spearman rank correlation coefficient. Results support use of NDVI, dT, dry period climatic stress factors and human population density for development of a GIS environmental risk assessment model for schistosomiasis in Brazil.

Application of geographic information systems and remote sensing to schistosomiasis control in China.

Progress in China on developing prediction models using remote sensing, geographic information systems and climate data with historical infection prevalence and malacology databases is reviewed. Special reference is made to the effects of the Yangtze river Three Gorges dam project on environmental changes that may impact changes in the spatial and temporal distribution and abundance of Schistosoma japonicum in China, and the future success of disease control programs.

Mapping the potential distribution of Phlebotomus martini and P. orientalis (Diptera: Psychodidae), vectors of kala-azar in East Africa by use of geographic information systems.

The distribution of two principal vectors of kala-azar in East Africa, Phlebotomus martini and Phlebotomus orientalis were analysed using geographic information system (GIS) based on (1) earth observing satellite sensor data: Normalized Difference Vegetation Index (NDVI) and midday Land Surface Temperature (LST) derived from advanced very high resolution radiometer (AVHRR) of the global land 1km project of United States Geological Survey (USGS), (2) agroclimatic data from the FAO Crop Production System Zone (CPSZ) of the Intergovernmental Authority on Development (IGAD) sub-region, and (3) the FAO 1998 soils digital map for the IGAD sub-region. The aim was to produce a predictive risk model for the two vectors. Data used for the analysis were based on presence and absence of the two species from previous survey collections in the region (mainly Ethiopia, Kenya and Somalia). Annual, wet season and dry season models were constructed. Although all models resulted in more than 85% positive predictive values for both species, the best fit for the distribution of P. martini was the dry season composite (NDVI 0.07-0.38 and LST 22-33 degrees C) with a predictive value of 93.8%, and the best fit for P. orientalis was the wet season composite (NDVI -0.01 to 0.34 and LST 23-34 degrees C) with a predictive value of 96.3%. The two seasonal composites models derived from satellite data were largely similar with best fit models developed based on the CPSZ climate data: average altitude (12-1900m), average annual mean temperature (15-30 degrees C), annual rainfall (274-1212mm), average annual potential evapotranspiration (1264-1938mm) and readily available soil moisture (62-113mm) for P. martini; and average altitude (200-2200m), annual rainfall (180-1050mm), annual mean temperature (16-36 degrees C) and readily available soil moisture (67-108mm) for P. orientalis. Logistic regression analysis indicated LST dry season composite of the satellite data, average altitude, mean annual temperature and readily available soil moisture of the CPSZ data as the best ecological determinants for P. martini while LST annual composite was the only important ecological determinant for P. orientalis. Spearman's rank correlation revealed several factors to be important determinants for the distribution of the two vectors. None of the soil types analysed appeared to be important determinant for the two species in East Africa, unlike in Sudan where P. orientalis is mainly associated with eutric vertisol (black cotton clay soil).

Geographic information systems as a tool for control program management for schistosomiasis in Egypt.

During a 4-year study a geographic information system (GIS) risk model was constructed for predicting the relative risk of schistosomiasis in Kafr El-Sheikh governorate, Egypt. A 1-year 1990-1991 time series on diurnal temperature difference (dT) prepared from the advanced very high resolution radiometer (AVHRR) sensor on the NOAA-11 satellite was used to develop a regional risk model for the Nile delta based on thermal-hydrological domains. A May 15, 1990 Landsat TM scene (path 177, Row 38) was used to develop a local 'village-scale' environmental risk model based on higher resolution satellite sensor data (30 m picture element size at earth surface). Four of ten classes derived from a tasseled cap (Tcap) transformation of the Landsat TM scene were shown to be significantly related to a 5-year Schistosoma mansoni prevalence database from the Ministry of Health. A risk model was developed based on dT and the proportional area of the four Tcap classes in 5 km(2) buffer zones centered on rural health unit (RHU) reporting units. Available historical data on S. mansoni and its snail host Biomphalaria alexandrina, as well as recent field collected data were gathered and incorporated as separate themes. Model validation was done using data collected on snail population bionomics-infection rates, water quality, underground water table and cercariometry at 13 hydrologically representative sites. The role of soil type, water table and water quality was studied at 79 of 154 rural health unit sites. The model permitted retrieval of relevant data by RHU point location. For the first time in Egypt, the Kafr El-Sheikh GIS schistosoma prediction model can support MOH efforts to make more accurate control program decisions based on environmental predilection sites of endemic Schistosomiasis mansoni.

Use of landsat TM satellite surveillance data to measure the impact of the 1998 flood on snail intermediate host dispersal in the lower Yangtze River Basin.

To assess the impact of the 1998 flood on snail distribution in the lower Yangtze River Basin, two study areas were selected, one in the Poyang Lake region, and the other along the Yangtze River in Jiangsu province. Using image analysis software, geocoded Landsat TM data were used to create TNDVI maps based on the formula TNDVI=Sqrt[(band4-band3/band4+band3)+0.5]. The images taken in the flood season were classified to produce a map depicting water and land. The images taken during springtime were processed and classified based on TNDVI. Composite images were created based on the time difference analysis, combining the flood season maps and spring vegetation maps to produce a map in which potential snail habitats were identified. When compared with ground survey data collected in the spring of 2000, the correspondence rate between potential snail habitats identified by image analysis of 1998-1999 Landsat TM data and ground survey data was over 90% in both regions. Results indicate that ecology based Landsat TM image analysis provides a new way to predict snail distribution under specific environmental conditions associated with the extent of the annual flood season.

A global network for the control of snail-borne disease using satellite surveillance and geographic information systems.

At a team residency sponsored by the Rockefeller Foundation in Bellagio, Italy, 10-14 April 2000 an organizational plan was conceived to create a global network of collaborating health workers and earth scientists dedicated to the development of computer-based models that can be used for improved control programs for schistosomiasis and other snail-borne diseases of medical and veterinary importance. The models will be assembled using GIS methods, global climate model data, sensor data from earth observing satellites, disease prevalence data, the distribution and abundance of snail hosts, and digital maps of key environmental factors that affect development and propagation of snail-borne disease agents. A work plan was developed for research collaboration and data sharing, recruitment of new contributing researchers, and means of access of other medical scientists and national control program managers to GIS models that may be used for more effective control of snail-borne disease. Agreement was reached on the use of compatible GIS formats, software, methods and data resources, including the definition of a 'minimum medical database' to enable seamless incorporation of results from each regional GIS project into a global model. The collaboration plan calls for linking a 'central resource group' at the World Health Organization, the Food and Agriculture Organization, Louisiana State University and the Danish Bilharziasis Laboratory with regional GIS networks to be initiated in Eastern Africa, Southern Africa, West Africa, Latin America and Southern Asia. An Internet site, www.gnosisGIS.org, (GIS Network On Snail-borne Infections with special reference to Schistosomiasis), has been initiated to allow interaction of team members as a 'virtual research group'. When completed, the site will point users to a toolbox of common resources resident on computers at member organizations, provide assistance on routine use of GIS health maps in selected national disease control programs and provide a forum for development of GIS models to predict the health impacts of water development projects and climate variation.