**3. Leishmaniasis in the Old World**

Elnaiem 1998 [17] in Sudan, investigating the importance of the effect of environmental data (obtained from digital records collected by satellites), such as: rainfall, minimum and maxi‐ mum temperatures, soil class, vegetation and land-surface-temperature indices, on a popula‐ tion of *Phlebotomus orientalis*, observed a significant association of this sand fly with the presence of the tree species *Acacia seyal* and *Balanites aegyptiaca* and with the black cotton (vertisolic) soils of eastern Sudan. The authors also showed that positive sites were found to have significantly higher annual mean maximum and minimum daily temperatures and the annual mean maximum normalized-difference vegetation index (NDVI) value was also found to be significantly higher in these in comparison with those places where no *P. orientalis* were found.

Bern et al 2005 [18] studied the spatial patterns and risk factors for anthroponotic visceral leishmaniasis in Bangladesh. Integrating the GIS approach with data related to history, active case detection, and serologic screening, from residents had kala-azar, they observed that the risk was highest for persons 3–45 years of age, and no significant difference by sex. Considering the age-adjusted multivariable models, 3 factors were identified: proximity to a previous kalaazar patient, bed net use in summer and cattle per 1,000 m2. The authors observed no difference by income, education, or occupation; land ownership or other assets; housing materials and condition; or keeping goats or chickens inside bedrooms. The results confirmed a strong clustering occurrence and suggested that insecticide-treated nets could be effective in pre‐ venting kala-azar.

In this study, the households were mapped by a GPS and the data were processed into ArcGis. Through the GIS data, distances were determined from the household to the closest kala-azar cases in the previous year. Kernel density estimation was used to estimate cattle per 1,000 m2 in order to calculate the effect of cows, oxen or calves on the kala-azar risk for nearby residents.

have been dwelling on earth for millions of years before of us and it certainly represents that

The first studies on leishmaniasis utilizing the geoprocessing technology were carried out in the 90s. After that, several groups from different parts of the world have studied important epidemiological aspects of this disease through the integration of results obtained from serological techniques, biological characteristics and population analysis of vectors and hosts with environmental factors such as: elevation, temperature parameters, mean monthly precipitation, relative humidity, land surface temperature parameters (including amplitude),

In the following section we presented a chronological review including the more relevant papers, originated from studies achieved in the Old World and New World, using the above-

Elnaiem 1998 [17] in Sudan, investigating the importance of the effect of environmental data (obtained from digital records collected by satellites), such as: rainfall, minimum and maxi‐ mum temperatures, soil class, vegetation and land-surface-temperature indices, on a popula‐ tion of *Phlebotomus orientalis*, observed a significant association of this sand fly with the presence of the tree species *Acacia seyal* and *Balanites aegyptiaca* and with the black cotton (vertisolic) soils of eastern Sudan. The authors also showed that positive sites were found to have significantly higher annual mean maximum and minimum daily temperatures and the annual mean maximum normalized-difference vegetation index (NDVI) value was also found to be significantly higher in these in comparison with those places where no *P. orientalis* were

Bern et al 2005 [18] studied the spatial patterns and risk factors for anthroponotic visceral leishmaniasis in Bangladesh. Integrating the GIS approach with data related to history, active case detection, and serologic screening, from residents had kala-azar, they observed that the risk was highest for persons 3–45 years of age, and no significant difference by sex. Considering the age-adjusted multivariable models, 3 factors were identified: proximity to a previous kalaazar patient, bed net use in summer and cattle per 1,000 m2. The authors observed no difference by income, education, or occupation; land ownership or other assets; housing materials and condition; or keeping goats or chickens inside bedrooms. The results confirmed a strong clustering occurrence and suggested that insecticide-treated nets could be effective in pre‐

In this study, the households were mapped by a GPS and the data were processed into ArcGis. Through the GIS data, distances were determined from the household to the closest kala-azar

they have skills we not elucidated yet.

132 Leishmaniasis - Trends in Epidemiology, Diagnosis and Treatment

normalized-difference vegetation index NDVI and land cover.

**2. Overview**

mentioned approach.

found.

venting kala-azar.

**3. Leishmaniasis in the Old World**

Ryan et al 2006 [19], studying visceral leishmaniasis in Kenya, used *Leishmania*-specific antibodies to estimate the seroprevalence and GIS and spatial clustering techniques to study the presence of spatial clusters in two villages. In only one of the villages, significant associa‐ tions among seropositivity and house construction, age, and proximity to domestic animal enclosures were found. In the same place, a significant spatial cluster of VL was found and the spatial distribution of cases in the two villages was different with respect to risk factors, such as presence of domestic animals. The authors suggested that disease control efforts could be focused on elimination of sand fly habitat, placement of domestic animal enclosures, and targeted use of insecticides.

Sudhakar et al., 2006 [20] in a study carried out in India, analyzed in Silicon graphic image processing system, using ERDAS software, some data obtained from a remote sensing satellite.

The GIS functions were applied to quantify the remotely sensed landscape proportions of 5 km2 buffer in determined places of high occurrence of sand flies in endemic and nonendemic areas. Through the combination of remote sensing (RS) and geographical information system (GIS) they developed landscape predictors of sand fly abundance an indicator of human vector contact and as a measure of risk prone areas.

It was indicated, that the environmental factors such as type and density of settlements, proximity to these with that of water bodies, marshy areas with succulent weed cover and also crops of high succulent in nature like sugarcane, bananas coupled with local prevailing conditions had definitely interactive influencing effect of vector density and also incidents of vector borne diseases.

Rossi et al 2007 [21] in Southern Italy, applied GIS and SR to analyze the distribution of the *Leishmania infantum-Phlebotomus perniciosus* parasite-vector system in relation to environmen‐ tal features of two opposite sides (coastal and Apennine) of an area of intense transmission of human and canine leishmaniasis.

The cumulative density, a term determined by the authors as the number of specimens/m2 of sticky trap/two nights, of this vector species was related as significantly more abundant in the coastal side. The authors suggested that the predominance of green vegetated environments in the coastal side, in contrast with the predominance of urban environment in the Apennine side, could be responsible for the different *P. perniciosus* densities between the areas.

Ready 2010 [22] reported that climate change could affect leishmaniasis distribution, by the effect of temperature on parasite development in insect vector, or because of the effect of environmental variation on the range and seasonal abundances of the sand fly species.

He also suggested that bio-climate zones and their vegetation indicators vary regionally, and continuing climate change could alter the patterns of land cover and land use. Thus, the GISbased spatial modeling of the Emerging Diseases in a changing European Environment was providing analysis of alterations in climate and land cover and their effects on sand flies.

Bhunia et al 2010 [23] in India, through satellite imagery complemented with a GIS database, estimated parameters such as altitude, temperature, humidity, rainfall and the normalized difference vegetation index (NDVI) for correlation with the distribution of Kala-azar. They observed that the highest prevalence was below 150 m of altitude with very few cases located above the 300 m level and a low NDVI value ranges correlated with a high occurrence of the disease. They also showed, that most of the cases occurred in non-vegetative areas or low density vegetation zones highlighting that the low density vegetation zones were significant for the *P. argentipes* vector distribution in the disturbed areas.

Khanal et al 2010 [24] in Nepal, merged results from a serological test made in humans and domestic animals with GIS technology to evaluate the exposure to *L. donovani* on two popu‐ lations in a recent focus of visceral leishmaniasis (VL). They used a Poisson regression model to evaluate the risk of infection in humans associated with seropositive animals in the prox‐ imities of the household. It was also demonstrated that seropositive animals and humans were spatially clustered and the presence of positive goats, past VL cases and the proximity to a forest island increased the risk of occurrence of seropositivity in humans. The authors also suggested that goats might play some role in the distribution of *L. donovani*, in the VL focus studied.

Bhattarai et al 2010 [25] also in Nepal, with the purpose of determining possible reasons for persistence of VL during inter-epidemic periods, they mapped cases *Leishmania* infections among apparently healthy persons and animals in an area of active VL transmission. The results of a bivariate K-function analysis showed the occurrence of spatial clustering of *Leishmania* spp.–positive persons and domestic animals, addition the investigation through classification tree, determined that the proximity of *Leishmania* spp.–positive goats ranked as the first risk factor for *Leishmania* infection among persons.

Salahi-Moghaddam et al 2010 [26] in Iran performed a serological study on a population of dogs from an endemic area.

No significant correlation between topographic conditions and the prevalence of positive cases was observed after regression analysis. Nevertheless, positive correlations were found in relation to the amount of rainfall, between infected dogs with high titers (≥1/640) and the number of days with temperatures below 0 °C during one year. The same correlation was observed when they were considered past meteorological records, conversely the humidity showed an inversely correlated with the *Leishmania* infections.

The authors suggested that in mapped areas the prevailing low temperatures could represent an important factor influencing the distribution of leishmaniasis.

More recently, Bhunia et al 2013 [27] in India, assumed that the utilization of GIS and RS technologies on the control of VL dates back to the late 2000s and those control programs have mostly focused on mapping prevalence and association of *Phlebotomus argentipes* habitats, predicting transmission risk in relation to ecological transformation.

Besides, the authors proposed that the multiplicity of satellite and sensors technics offer relevant data to assembly spatial, spectral and temporal scales. They also argued about the advantages of remotely sensed imagery technology in studies in sand fly ecology and vectorborne diseases, by the generation of a proper household breeding documentation at higher spatial resolution.
