Remote sensing, land cover changes, and vector-borne diseases: use of high spatial resolution satellite imagery to map the risk of occurrence of cutaneous leishmaniasis in Ghardaïa, Algeria.

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9 October 2014 Infection, Genetics and Evolution xxx (2014) xxx–xxx 1

Contents lists available at ScienceDirect

Infection, Genetics and Evolution journal homepage: www.elsevier.com/locate/meegid 6 7

Remote sensing, land cover changes, and vector-borne diseases: Use of high spatial resolution satellite imagery to map the risk of occurrence of cutaneous leishmaniasis in Ghardaïa, Algeria

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Service d’Eco-Epidémiologie Parasitaire, Institut Pasteur Alger, Algiers, Algeria UPR AGIRs, CIRAD, F-34398 Montpellier, France c UMR TETIS, CIRAD, F-34398 Montpellier, France d CIRAD, UMR CMAEE, F-34398 Montpellier, France e INRA, UMR CMAEE, F-34398 Montpellier, France b

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a r t i c l e

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Rafik Garni a,b, Annelise Tran b,c,⇑, Hélène Guis d,e, Thierry Baldet d,e, Kamel Benallal a, Said Boubidi a, Zoubir Harrat a

i n f o

Article history: Received 24 April 2014 Received in revised form 25 September 2014 Accepted 29 September 2014 Available online xxxx

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Keywords: Leishmaniasis Cutaneous leishmaniasis Leishmania major Leishmania killicki Geographic Information System Risk mapping Remote sensing Algeria

a b s t r a c t Ghardaïa, central Algeria, experienced a major outbreak of cutaneous leishmaniasis (CL) in 2005. Two Leishmania species occur in this region: Leishmania major (MON-25) and Leishmania killicki (MON-301). The two species are transmitted respectively by the sandflies Phlebotomus papatasi and Phlebotomus sergenti and probably involve rodent reservoirs with different ecologies, suggesting distinct epidemiological patterns and distribution areas. The aims of this study were to establish risk maps for each Leishmania species in Ghardaïa, taking into account the specificities of their vectors and reservoirs biotopes, using land cover and topographical characteristics derived from remote sensing imagery. Using expert and bibliographic knowledge, habitats of vectors and reservoirs were mapped. Hazard maps, defined as areas of presence of both vectors and reservoirs, were then combined with vulnerability maps, defined as areas with human presence, to map the risk of CL occurrence due to each species. The vector habitat maps and risk maps were validated using available entomological data and epidemiological data. The results showed that remote sensing analysis can be used to map and differentiate risk areas for the two species causing CL and identify palm groves and areas bordering the river crossing the city as areas at risk of CL due to L. major, whereas more limited rocky hills on the outskirts of the city are identified as areas at risk of CL due to L. killicki. In the current context of urban development in Ghardaïa, this study provides useful information for the local authorities on the respective risk areas for CL caused by both parasites, in order to take prevention and control measures to prevent future CL outbreaks. Ó 2014 Published by Elsevier B.V.

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1. Introduction

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Leishmaniasis is a parasitic vector-borne disease caused by Protozoa of the genus Leishmania, transmitted by the bite of female sandflies belonging to the genus Phlebotomus in the Old World and Lutzomyia in the New World. It occurs in 98 countries and

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comprises two diseases, visceral leishmaniasis (VL) and cutaneous leishmaniasis (CL) which are estimated to affect between 0.2–0.4 and 0.7–1.2 million cases worldwide respectively (Alvar et al., 2012). CL is more widely distributed than VL. In North Africa CL has been increasing since the 1980s, both in terms of incidence and distribution (Aoun and Bouratbine, 2014). Algeria belongs to

Abbreviations: BI, brightness index; CL, cutaneous leishmaniasis; DEM, digital elevation model; GPS, Global Positioning System; MSAVI, Modified Soil Adjusted Vegetation Index; NDVI, Normalized Difference Vegetation Index; SPOT, Satellite Pour l’Observation de la Terre; SRTM, Shuttle Radar Topography Mission. ⇑ Corresponding author at: CIRAD, Maison de la Télédétection, 500 rue Jean-Francois Breton, F34093 Montpellier, France. Tel.: +33 4 67 54 87 36. E-mail addresses: [email protected] (R. Garni), [email protected] (A. Tran), [email protected] (H. Guis), [email protected] (T. Baldet), [email protected] (K. Benallal), [email protected] (S. Boubidi), [email protected] (Z. Harrat). http://dx.doi.org/10.1016/j.meegid.2014.09.036 1567-1348/Ó 2014 Published by Elsevier B.V.

Please cite this article in press as: Garni, R., et al. Remote sensing, land cover changes, and vector-borne diseases: Use of high spatial resolution satellite imagery to map the risk of occurrence of cutaneous leishmaniasis in Ghardaïa, Algeria. Infect. Genet. Evol. (2014), http://dx.doi.org/10.1016/ j.meegid.2014.09.036

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the ten countries with the highest number of CL cases, with these ten countries accounting for 70–75% of CL incidence in the world (Alvar et al., 2012). Transmission cycles of leishmaniasis are complex, implicating large numbers of parasites, vectors and reservoirs. Several Leishmania taxa can cause CL, each of which is involved in specific epidemiological cycles, i.e. associated with specific vectors and, for some, with specific reservoir hosts. Chaara et al. (2014) reviewed the epidemiological features of leishmaniasis in the Maghreb region (Chaara et al., 2014). In Algeria until recently, two forms of CL had been described: the sporadic form caused by Leishmania infantum in the North, and the classic form caused by Leishmania major in central and southern parts of the country. Between 2004 and 2006, one other taxon, Leishmania killicki, was detected in Ghardaïa (central Algeria) (Harrat et al., 2009) and in Constantine City (North-eastern Algeria) (Mihoubi et al., 2008). The present study focused on the epidemiological cycles occurring in Ghardaïa. In 2005, a major outbreak of CL occurred in the province of Ghardaïa: 2040 human cases were recorded, many of them from urban areas. Besides the first detection of L. killicki in Algeria, this outbreak also led to the description of a new enzyme variant L. killicki (MON-301), which was shown to coexist sympatrically with L. major MON-25 (Harrat et al., 2009). The two species causing CL were associated with their typical vectors, Phlebotomus papatasi for L. major and Phlebotomus sergenti for L. killicki as the latter is a variant of L. tropica (Ben Ismail et al., 1987; Boubidi et al., 2011; Harrat et al., 2009; Jaouadi et al., 2012; Tabbabi et al., 2011). CL due to L. major is of the zoonotic type, involving wild rodents of the genera Psammomys and Meriones, in particular with Psammomys. obesus and Meriones. shawi as reservoirs (Belazzoug, 1983; Izri et al., 1992). CL due to L. killicki also seems to be zoonotic, as Ctenodactylus gundi was found naturally infected by L. killicki in Tunisia (Bousslimi et al., 2012; Jaouadi et al., 2011), and different studies in Tunisia suggest that L. killicki infections differ from L. tropica, with strictly zoonotic transmission (Haouas et al., 2012; Maubon et al., 2009). Furthermore, in Ghardaïa, P. sergenti was found naturally infected in the neighbourhood of Massoutiera mzabi (‘Mzab Gundi’) burrows (Boubidi et al., 2011), suggesting that this rodent, which is particularly abundant in this area, could also act as a reservoir. The zoonotic transmission of L. killicki in Ghardaïa is also supported by the studies on the CL cases in this region, showing that no secondary cases were reported in the household or in the vicinity of confirmed CL cases (Epidemiology and Prevention Department of the Ministry of Health of the district of Ghardaïa, unpublished). Control of CL remains difficult as no vaccine exists and tools for interrupting transmission (vector and reservoir control) are limited. It is therefore essential to better understand the epidemiological cycles and develop methods to anticipate the occurrence of outbreaks or the establishments of new foci (Chaves and Pascual, 2006). This paper set out to map risk areas for the occurrence of both species causing CL (L. major and L. killicki) in several urban areas of Ghardaïa province, using eco-epidemiological knowledge on their respective vectors and hosts, and remotely sensed data. The approach developed here consisted in using satellite imagery and a digital elevation model to map the land cover of the study area. Expert and bibliographic knowledge on CL vectors and suspected reservoirs was then used to map CL hazard (Vector Reservoir) and risks (Hazard Vulnerability, vulnerability being defined as residential areas). These maps were validated using ground truth data for the land cover map, and entomological data for the vector habitat maps. As the cases of CL in Ghardaïa could not be associated with each of the two Leishmania species (L. major and L. killicki), the available epidemiological data were only used to assess the global consistency of the CL risk map.

2. Material and methods

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2.1. Study area

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Ghardaïa wilaya (province) is located in the central part of the Northern Sahara, 500 km south of the capital Algiers (Fig. 1a), on a limestone plateau with valleys and ravines. It is a desertic region covering an area of 86,650 km2, located between 1° and 5° East (about 200 km) and 31°300 and 33° North (about 450 km). The climate is dry and arid, rainfall is low and erratic, and the vegetation is sparse. The average monthly temperature is maximal in July (36 °C) and minimal in January (12 °C). Ghardaïa wilaya comprises 400,000 inhabitants located in 13 municipalities, of which four (Ghardaïa and three neighbouring municipalities, namely Bounoura, Dhayet Bendhahoua, and El Atteuf) were included in the study area (Fig. 1b).

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2.2. Review of expert knowledge on CL vectors and reservoirs in Algeria

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In Central Algeria, CL caused by L. major involves the sand fly species P. papatasi as the vector (Izri et al., 1992) and wild rodents (gerbils P. obesus and M. shawi) as reservoirs (Belazzoug, 1983; Izri et al., 1992). On the other hand, CL caused by L. killicki involves the sand fly species P. sergenti (Boubidi et al., 2011); the rodent species M. mzabi is suspected of being a reservoir of L. killicki (Boubidi et al., 2011; Harrat et al., 2009). The respective species acting as vectors and reservoirs of CL are associated with different environmental conditions. P. papatasi requires the presence of vegetation cover and humid soils, whereas P. sergenti can also be found in drier areas such as rocky soils (Adam et al., 1960; Gouat and Gouat, 1984; Gouat, 1988; Gouat et al., 1984; Izri et al., 1992; Schlein and Jacobson, 1999). The sand rodents P. obesus and M. shawi are present in plains, vegetation covers and sandy soils, whereas M. mzabi can live in much drier conditions (rocky soils), in hillside burrows (Adam et al., 1960; Gouat and Gouat, 1984; Gouat, 1988; Gouat et al., 1984; Izri et al., 1992; Schlein and Jacobson, 1999). Thus, the distribution of CL caused by L. major may be associated with the presence of large vegetation areas, such as palm groves, close to human settlements (see Fig. 1b and c), whereas CL caused by L. killicki may more likely occur in dry areas (see Fig. 1b and e). The risk of CL occurrence may be lower in dense urban areas (Fig. 1d), because of the absence of wild rodent reservoirs in those areas. Table 1 summarizes the environmental conditions of the distri- Q4 bution of vectors and reservoirs of both species causing cutaneous leishmaniasis in central Algeria (Adam et al., 1960; Fichet-Calvet et al., 2000; Gouat and Gouat, 1984; Gouat, 1988; Gouat et al., 1984; Izri et al., 1992; Kravchenko et al., 2004; Muller et al., 2011; Schlein and Jacobson, 1999; Wasserberg et al., 2003).

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2.3. Mapping the environmental conditions suitable for vectors and reservoirs of CL

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A map of environmental elements that modulate the habitats of vectors and reservoirs of CL (i.e. vegetation, soil type (sand or rock), soil moisture, see Table 1) was produced using satellite imagery and a digital elevation model (DEM). Spectral and textural indices were extracted from Satellite Pour l’Observation de la Terre (SPOT) images and combined with elevation data in a decision-tree classification. The classification was evaluated by comparing classification results with reference points.

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2.3.1. Elevation data A DEM is a representation of the topography of an area of land. DEM data files contain the elevation of a given area – usually at an

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Fig. 1. Location of the study area: (1a) Ghardaïa wilaya within Algeria. The red dot indicates the study area (1b) 2004 SPOT image covering the study area (Bands: Near infrared, Red, Green). (1c) Ghardaia oasis. (1d) Detail of Ghardaia urban area, 2004 SPOT image (Panchromatic mode). (1e) New agglomeration on steep rocky soils.

Table 1 Bibliographic review of the environmental determinants of the distribution of vectors and reservoirs of both species causing cutaneous leishmaniasis, L. major and L. killicki, in Central Algeria.

Environmental variables

Vegetation Soil moisture Soil Flat type rocky ground Sloping rocky ground Sandy soil

CL due to L. major

CL due L. killicki

Vector Phlebotomus papatasi

Reservoir Sand rodents: Meriones shawi, Psammomys obesus

Vector Phlebotomus sergenti

Reservoir Massoutiera mzabi

1 1 0

1 1 0

1 1 1

0 0 0

0

0

1

1

1

1

0

0

References

Adam et al. (1960), Fichet-Calvet et al. (2000), Gouat and Gouat (1984), Gouat (1988), Gouat et al. (1984), Izri et al. (1992), Kravchenko et al. (2004), Muller et al. (2011), Schlein and Jacobson (1999), Wasserberg et al. (2003)

CL: cutaneous leishmaniasis; 1: favourable; 0: unfavourable. 195 196 197 198 199 200 201

interval of a fixed geographical grid. Here, a 90-m resolution DEM derived from the SRTM (Shuttle Radar Topography Mission) of the U.S. space agency (NASA) was used to calculate the elevation and the slope for each point of the study area. 2.3.2. SPOT images Satellite images offer the possibility of extracting diverse items of information such as vegetation cover or soil moisture. Thus, two

SPOT-5 images covering the study area were acquired (Table 2, Fig. 1b). The two 2004 images (multispectral and panchromatic images) were used to produce a land cover map of the study area. Indeed, the optical sensors on board the SPOT satellite measures electromagnetic energy reflected from the ground in different wavelengths, and at a spatial resolution of 10 m in multispectral mode, and 2.5 m in panchromatic mode. This measurement is used to calculate the spectral reflectance, which corresponds to the ratio


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R. Garni et al. / Infection, Genetics and Evolution xxx (2014) xxx–xxx Table 2 Characteristics of the DEM and SPOT images.

a

Acquisition date

Sensor/satellite

Spatial resolution (spectral resolutiona)

Source

2000 2004/10/13

SRTM HRG-1/SPOT-5

90 m 10 m (MS) 2.5 m (PAN)

MS: multi spectral (green, red, near infrared and middle infrared spectral bands); PAN: panchromatic (one band).

Table 3 Spectral and textural indices derived from SPOT images. Index

Definition

Equations

References pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi 2

MSAVI2

Revised version of the Modified Soil Adjusted Vegetation Index

MSAVI2 ¼ 2NIRþ1

Moisture

TC3 ¼ 0:223 G þ 0:012 R 0:543 NIR þ 0:81 MIR

BI

Third Tasseled-Cap band which can be interpreted as an index of ‘‘wetness’’ Brightness index

Texture

Local variance of the grey level co-occurrence matrix

BI ¼

ð2NIRþ1Þ 8ðNIRRÞ 2

pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi G2 þR2 þNIR2 þMIR2 4

Qi et al. (1994) Crist and Cicone (1984) Major et al. (1990) Haralick et al. (1973)

NIR: near infrared, R: red, G: green, MIR: middle infrared.

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of the radiation reflected by a surface to that of the incident radiation in a particular wavelength interval. As each type of land cover has a specific spectral signature (i.e. a specific reflectance values for different wavelengths), the reflectance values measured by the SPOT satellite may enable its detection and characterization. 2.3.3. Calculation of spectral indices from the Spot images To characterize the habitat of the different species of CL vectors and reservoirs, several spectral and textural indices were calculated from the SPOT images: the Modified Soil Adjusted Vegetation Index (MSAVI), the moisture index of Tasseled-Cap transformation, the soil brightness index and a texture index (Table 3). The MSAVI (Qi et al., 1994) is a soil-adjusted vegetation index that seeks to address some of the limitations (underestimation of vegetated surfaces) of the classical Normalized Difference Vegetation Index (NDVI) when applied to areas with a high degree of exposed soil surface, which was the case of our study area where vegetation was sparse. We used the revised version of MSAVI (MSAVI2) as developed by Qi et al. (1994) (Table 3). The Tasseled-Cap Transformation (Crist and Cicone, 1984) is a conversion of the original reflectance bands of a multispectral image into a new set of bands with interpretations that are useful for mapping vegetation and wetness. It is performed by taking a linear combination of the original image reflectance bands. Here we computed the third Tasseled-Cap band which can be interpreted as an index of ‘‘wetness’’ (e.g., soil or surface moisture) (Table 3). The soil brightness index (BI) reflects the changes in colour between bare rocks and rocky soils on the one hand and bare sandy soils on the other; the latter having higher values of BI than the former (Major et al., 1990) (Table 3). Texture indices can be derived from co-occurrence matrices (Haralick et al., 1973) which are defined as the distribution of cooccurring pixel values in a given neighbourhood. For example, indices such as the contrast, variance, or the entropy of co-occurrence matrices all reflect the degree of heterogeneity of the image, allowing the detection of structural patterns in the image. Here we used the index of variance to detect the residential areas from the panchromatic image (Table 3). 2.3.4. Classification of the study area A decision tree-based classification was carried out to map the various land cover types and elevation characteristics relevant to

map habitats of CL vectors, reservoirs and hosts (Fig. 2). This method consisted in establishing a dichotomous classification by thresholding the spectral, textural and topographical indices (Decision Tree function, ENVI IDL software 4.8, Exelis, Boulder, C0, USA). Threshold values of the MSAVI and texture indices, applied to distinguish areas with vegetation, residential areas, and bare soils were defined from a Receiver-Operating-Curve (ROC) analysis, based on 180 randomly generated points in the study area, for which the class (vegetation, residential areas, and bare soils) had been identified using Google Earth image interpretation (Venard et al., 2010). In the absence of ground-truth data on the other classes (rocky soils, very dry soils and other soils), thresholds applied to the soil BI and to the moisture index were derived from an unsupervised classification process in two classes. In total, six classes were mapped (Fig. 2): vegetation areas, residential areas, flat rocky soils, steep rocky soils, very dry soils and other soils (i.e. moderately humid soils). The resulting land cover map had a spatial resolution of 2.5 m.

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2.3.5. Validation of the classification The classification was validated by selecting 180 validation points per class (using Google image interpretation) and comparing their observed land cover type to the classification results through a confusion matrix. The accuracy measurements computed were the total accuracy percentage and the Kappa index. The higher these indices were, the better the concordance was between the results of the classification and the validation points (Cohen, 1960; Foody, 2002).

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2.4. Mapping CL hazard, vulnerability and risk areas

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To map the risk areas for the occurrence of CL cases in the study area, we defined the risk as a combination of vulnerability (the presence of human hosts = residential areas) and hazard (the presence of both vectors and reservoirs, allowing pathogen transmission) (Fig. 3). In this first attempt to map the areas at risk for CL, we neglected a potential anthroponotic transmission of L. killicki and considered only zoonotic transmission for three main reasons: (i) according to the studies on the CL cases in this region, no secondary cases were reported in the household or in the vicinity of confirmed CL cases (Epidemiology and Prevention Department of the Ministry of Health of the district of Ghardaïa, unpublished); (ii) the sand fly species involved in the transmission of L. killicki,

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Fig. 2. Classification decision-tree. Decision-tree nodes are in grey, coloured boxes are the final classes.

Fig. 3. Schematic representation of vulnerability, hazard and risk.

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P. sergenti, was found naturally infected in the neighbourhood of M. mzabi burrows in Ghardaïa (Boubidi et al., 2011); M. mzabi is a particularly abundant rodent species in the region, belonging to the same family as C. gundi, sharing the same ecology and found naturally infected by L. killicki in Tunisia (Jaouadi et al., 2011). By applying expert and bibliographic knowledge on vector and reservoir habitat preferences (Section 2.2, Table 1) to the land cover map, habitat maps of vectors and reservoirs were produced, following similar expert-based approaches applied on vectorborne diseases (Hongoh et al., 2011; Sanchez-Vizcaino et al., 2013; Tran et al., 2013). Each pixel was classified as propitious (pixel value set to 1) or not (pixel value set to 0) to the presence of either vector or reservoir species of the two CL forms, due to L. major and L. killicki (Geographic Information System software: ArcGIS Spatial Analysis Tools). The resulting habitats maps had the same spatial resolution as the land cover map (2.5 m). 2.4.1. Validation of the vector habitat maps Available sand fly collection data were used to validate the vector habitat map (the accuracy of the reservoir habitat maps could not be assessed, because very few observational data were available in the study area). The main objective of the entomological studies from which these data were taken was to identify L. killicki vector species in the Ghardaïa region (Boubidi et al., 2011).

Sandflies were collected using CDC light traps for species identification and sticky traps for density estimation. Samples were taken in 2008 and 2009, twice a month from April to November, using 100 sticky traps and 4 CDC light traps. Sticky traps were installed biweekly throughout the vectorial season (April–November). Light traps were installed on two consecutive nights in June and in October. Traps were set at 15 sites located in urban, suburban and wild rocky hill areas representative of the Ghardaïa area. The geographical location of each site was located using a Global Positioning System (GPS) receiver. For each trapping location, the observed proportion of P. sergenti was computed as the number of P. sergenti trapped divided by the sum of the P. sergenti and P. papatasi, and was compared to the predicted proportion of P. sergenti derived from vector habitat maps.

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2.4.2. Mapping CL hazard Hazard is the threat arising from the presence in the same place at the same moment of vectors and reservoirs involved in the transmission cycle of CL. To map areas of potential CL transmission, we took into account the flight distance capacity of Phlebotomus sp. from breeding sites to potential reservoirs or humans. Mark – release – recapture studies conducted on sand flies in arid areas concluded on flight distance capacities varying from 200 to 700 m (Alexander and Young, 1992; Alexander, 1987; Doha et al., 1991; Schlein, 1987). We chose the maximum flight distance of 700 m to avoid underestimation of the hazard areas. The hazard map resulted from the intersection between the habitats of reservoirs and the habitat and potential dispersal area of vectors, for both forms of CL (ArcGIS Spatial Analysis Tools: application of a Gaussian low-pass filter with radius = 700 m), and had a spatial resolution of 2.5 m.

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2.4.3. Mapping CL risk Risk reflects how important consequences of the hazard could be. It results from the combination of hazard and vulnerability, the latter being defined by the size of the population at risk that is present. Here, vulnerability is defined as the habitat of the susceptible hosts (humans), corresponding to the ‘residential areas’ class derived from the SPOT-image classification (Section 2.3.4). Thus, risk maps for CL due to L. major and CL due to L. killicki were obtained by intersecting the two CL hazard maps (Section 2.4.3)

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and vulnerability maps. Finally, a global risk map for CL in Ghardaïa was obtained by defining for each pixel as global CL risk value the maximum value of the risk index for CL due to L. major and the one for CL due to L. killicki. The risk maps for CL in Ghardaïa thus took values ranging between 0 (low risk) and 1 (high risk for CL occurrence), and had a spatial resolution of 2.5 m. 2.4.4. Assessment of CL risk maps Epidemiological data from the records of the Epidemiology and Prevention Department of the Ministry of Health of the district of Ghardaïa in 2004 were used to assess the global consistency of CL risk maps. Cases were defined as patients with a positive diagnosis of smears taken from the edge of the active lesions, stained by Giemsa and examined for the presence of Leishmania parasites (Harrat et al., 2009). Due to a lack of information on the exact address of the patients, the location was reported at the district level, which we located using a Global Positioning System (GPS) receiver. In total, 249 cases were reported in 2004 in the study area, of which 141 (56.6%) were in Ghardaïa, 72 (28.9%) in Bounoura, 15 (6.0%) in Dhayet Bendhahoua, and 21 (8.5%) in El Atteuf. Because the diagnosis of CL cases in Ghardaïa did not enable a distinction to be made between the two Leishmania species (L. major and L. killicki), it was not possible to perform a separate validation of each CL risk map. However, the global consistency of the risk maps was evaluated by comparing the reported number of CL cases to the global CL risk index, defined in Section 2.4.3, for each district having reported more than five cases in 2004 (ArcGIS Spatial Analysis Tools, Extraction Tool).

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3. Results

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3.1. Environmental map of the study area and validation

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The environmental map derived from 2004 SPOT images by a decision-tree classification is presented in Fig. 4a. According to the image classification, 1.7% of pixels within the study area were classified as urban areas, 25.0% as vegetation (including palm groves and other vegetation types), and 73.3% as soils (rocky soils: 47.0%, other soils: 26.3%). Accuracy measurements of the 2004 land cover map showed excellent agreement between the classification results and the 180 validation points, obtained from Google Earth images interpretation on vegetation, soils and residential areas (total accuracy = 98.9%, Kappa index = 0.98). Yet, the accuracy of the soil classes (i.e. rocky soils, very dry soils, and other soils) could not be assessed as no ground-truth data were available for these classes.

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3.2. Maps of vectors and reservoirs, hazard, and CL risk maps

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The hazard distribution maps for each CL species resulting from the intersection of corresponding reservoir and vector habitats are presented in Fig. 4b (see Supplementary File S1 for the separate distribution maps of vectors and reservoirs of L. major and L. killicki). By construction, as result of crossing expert and bibliographic knowledge on vector and reservoir habitats (Table 1) and the land cover map (Fig. 4a), the hazard of CL due to L. major appeared to be much greater in the study area than that of L. killicki whose distribution was closely related to confined areas and rocky foothills of the mountains that surround the Mzab valley. Hazard-free areas for both CL forms comprised (i) desert areas such as dunes and rocky plateau areas, which are dry and nonvegetated, and where rodent reservoir species cannot develop,

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and also (ii) urban areas within the city where the rodent reservoir species cannot develop because of dense human settlement. The cutaneous leishmaniasis risk map (Fig. 4c) results from the intersection between hazard maps of both CL forms and the populated areas. The maps obtained showed that risk areas due to L. major were located mainly inside the city, whereas those due to L. killicki were located on the borders of towns and in the foothills of mountains. Most of the hazard areas due to L. killicki (Fig. 4b) disappear from the risk map (risk = hazard * vulnerability, Fig. 4c) for CL due to L. killicki, as no human population was present. On the other hand, the risk of L. major was greater in the palm groves and areas bordering the river Mzab which crosses the city, than in the densely populated areas, as it depended on the presence of P. papatasi, present in vegetation areas, and on the presence of rodent reservoir species, absent from dense human settlements.

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3.3. Assessment of vector habitat maps and CL risk maps

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The entomological field collections enabled the capture of a total of 1028 P. papatasi and 437 P. sergenti. P. papatasi was trapped at all of the 15 sites, whereas P. sergenti was only collected at 5 sites, all located on the outskirts of town (Fig. 5a). Although the distribution map for P. sergenti predicted a large presence of this vector species throughout the study area (Fig. S1), the results of entomological collections suggested that its distribution was limited to the outskirts of the city. On the other hand, the presence of P. papatasi, as predicted by the vector distribution map, was confirmed by the entomological field collections. Finally, the comparison of vector maps and entomological results (Fig. 5b) showed a good agreement between observed and predicted values for the proportion of P. sergenti (Bravais–Pearson correlation coefficient R = 0.57). The locations of the 249 cases, reported in 2004 are presented in Fig. 5a. In total, 22 districts were infected in 2004, of which 15 reported more than 5 CL cases. The comparison of the location of the 2004 cases and the global CL risk index (Fig. 5c) showed that the number of CL cases increased with the global CL risk index, indicating a fair agreement between observed data and the predicted risk index (Bravais–Pearson correlation coefficient = 0.50).

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4. Discussion

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This study was a first attempt to map the risk of CL transmission in the urban areas of Ghardaïa in central Algeria. It was based on the distribution of the specific vectors and reservoirs and the common host (human) of L. major and L. killicki. The approach developed in this study thus enabled mapping of the current expert and bibliographic knowledge on the respective vectors and reservoirs of each CL species, producing synthetic hazard and risk indices. Recently, different expert-based approaches such as MultiCriteria Decision Analysis have been proved to be useful in data-scarce contexts, or in vector-borne disease-free areas, were risk-based surveillance measures need to be implemented (Hongoh et al., 2011; Sanchez-Vizcaino et al., 2013; Tran et al., 2013). Those methods have the great advantages of making it possible to generate maps showing areas suitable for the disease in the absence of data, provided that the necessary knowledge is available; they are also relatively quick to implement (Stevens et al., 2013). Yet, it is important to bear in mind that there is a great danger of circular reasoning in those approaches if the experts involved in the defining the environmental preferences of vectors and reservoirs base their choices on the results of trapping data used for map validation. As for data-driven risk mapping methods, data sets used for validation should be independent from the data

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Fig. 4. From land cover to CL risk map, Ghardaïa, Algeria, 2004. (4a) Land-cover map derived from classification of SPOT imagery. (4b) Hazard maps for L. major and L. killicki species causing cutaneous leishmaniasis. (4c) 2004 CL risk maps for L. major and L. killicki species (zoom).

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sets used for model building. Thus, to avoid this, the results of the entomological trapping in Ghardaïa were not included in the reference list used in Table 1; moreover, the researchers who reviewed the bibliographic knowledge on vector habitat (RG, TB) were not involved in the vector trapping (person in charge of sampling and entomological protocol: SB). Earlier studies mapped the risk of CL occurrence in Egypt (Samy et al., 2014), in Argentina (Quintana et al., 2012) and in North America (Gonzalez et al., 2010), and of canine leishmaniasis due to L. infantum in France (Chamaille et al., 2010) and in Europe (Franco et al., 2011). All of them were developed on a national or

continental scale, using climatic variables as predictors of the risk of leishmaniasis, and ecological niche modelling. Thus, the innovative nature of our approach was three fold: first, it was developed on a local scale, compatible with surveillance and control measures, and adapted to urban planning decisions; second, it was based on land cover maps derived from high spatial resolution satellite imagery, facilitating the updating of the risk maps, and the simulation of risk areas according to land cover changes scenarios. Finally, as it was based on expert and bibliographic knowledge, our method was particularly suited to data-scarce contexts, when data on the distribution of vectors and reservoirs are lacking.


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Fig. 5. Assessment of vector habitat and risk maps. (5a) Location of reported 2004 cutaneous leishmaniasis cases and of sandfly collections. Background: 2004 SPOT image. (5b) Bi-dimensional representation of the observed and predicted proportions of P. sergenti. (5c) Bi-dimensional representation of cutaneous leishmaniasis cases reported in 2004 in the study area, Ghardaïa, Algeria, according to the global CL risk derived from SPOT imagery. The black lines are the regression lines.

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Our results showed that hazard areas for CL due to L. major were more widespread than those due to L. killicki. Indeed, according to the expert and bibliographic knowledge (Table 1), P. papatasi, the vector of L. major, was more abundant in areas with more humid soils, capable of supporting desert vegetation, than in areas with low soil moisture and less vegetation (Muller et al., 2011; Wasserberg et al., 2003). Thus, because P. papatasi was present exclusively in vegetation areas or sandy humid soils, the hazard zones for L. major were related to vegetation zones, which are abundant on soft and irrigated ground (crops, palm groves, trees), and which are also suitable areas for L. major rodent reservoirs. In particular, P. obesus, the main reservoir host of L. major, is a Saharan herbivore feeding on salty leaves (Chenopodiacae) which are its primary food source (Fichet-Calvet et al., 2000). P. papatasi, the vector of L. major, finds an ideal environment and blood meals in the burrows of rodents to maintain the zoonotic Leishmania transmission cycle. By contrast, the hazard of CL due to L. killicki was limited and restricted to rocky areas that surrounded the urban areas, corresponding to the distribution areas of M. mzabi, the suspected reservoir of L. killicki. Although the vector P. sergenti had a rather broad distribution over the study area according to expert and bibliographic knowledge (Table 1 and Fig. S1), a comparison of the distribution maps for CL vectors (Fig. S1) with the results of entomological collections (Fig. 5a) suggested that P. sergenti seemed to favour dry rock habitats for breeding and resting around the urban

areas of Ghardaïa; its distribution thus corresponded to that of the rodent species M. mzabi (Fig. S1), whose burrows may provide breeding sites for P. sergenti (Feliciangeli, 2004). According to our results, the risk areas for CL due to L. major covered a large part of the valley of the city of Ghardaïa, while the risk distribution for leishmaniasis due to L. killicki was limited to dwellings bordering on the urban areas (Fig. 4c). This was generally precarious housing, but recently, new buildings have been built in the gaps of mountains and peaks (Fig. 1e), in areas in contact with the wild cycle of leishmaniasis due to L. killicki, causing human clinical cases in these settlements. The expansion of the city to the surrounding valleys and foothills may lead, in the future, to an increase in the number of L. killicki cases in these new settlements. With respect to this question of urbanization growth, the CL hazard maps were complementary to the CL risk maps as they highlighted the potential CL risk areas in non-constructed zones, allowing the integration of health issues in the urbanization decision processes. On the other hand, the CL risk maps highlighted the actual spatial heterogeneity of risk for CL in the city of Ghardaïa, and provided a tool for targeting entomological and epidemiological surveillance and control programmes in risk areas. The main limitations of our mapping approach concerned the validation data, both on the land cover map (validation of the soil types, rocky vs sandy soils), and on the distribution of vectors, reservoirs, and CL cases. In this study all available epidemiological and entomological data collected in previous surveys (Boubidi et al.,


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2011) were used to assess the vector habitat and risk maps, with fair to good agreement between predicted entomological and epidemiological risk indices and observed data (Fig. 5). Yet, additional field studies are crucial to fully validate the risk maps. First of all, trapping studies on both vectors and reservoirs should be implemented to validate the habitat maps, using a sampling scheme based on the land cover map, adjusted together by GIS experts, entomologists, ecologists, and epidemiologists. Moreover, the role of M. mzabi as a reservoir host in the transmission cycle of L. killicki should be determined. On the other hand, accurate identification of parasites from human CL samples and the precise location of patients in their homes would help to determine the ratio between CL cases caused by L. major and CL cases caused by L. killicki, and a better assessment of the risk maps predicting different distributions of the two species causing CL in the urban areas of Ghardaïa. The vector and reservoir maps, as well as risk maps, produced in this study could be used to better target the spatial sampling of vectors, reservoirs and humans to detect and discern the two forms causing CL in Ghardaïa. The temporal dynamics of sandfly and rodent populations, which are driven by climate factors as recently studied with regard to CL incidence in central Tunisia (Toumi et al., 2012), could be taken into account to provide temporal maps of the vector and reservoir distributions and the seasonal risk of CL transmission. Moreover, anthroponotic transmission of L. killicki, which was not modelled in our study, could be taken into account, giving different weights to wild reservoirs (M. mzabi) and human hosts, respectively. The comparison of the two resulting risk maps would help in discussing the respective roles of the anthroponotic vs zoonotic nature of L. killicki transmission. Other prospects for this work could be (i) to apply the method proposed here in other endemic CL foci where different species of Leishmania (ie L. infantum, L. major) are present in similar ecological and epidemiological contexts (Boudrissa et al., 2012) and (ii) to combine our local scale/land cover based-approach with national scale/climate-based approaches (Quintana et al., 2012; Samy et al., 2014). This would lead to a better understanding of the climatic, environmental and anthropogenic drivers of CL in Algeria.

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5. Conclusions

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We developed a Geographic Information System-based approach to map the risk of CL in Ghardaïa, Algeria. The approach proposed is very flexible, allowing the assimilation of updated knowledge on the distribution of CL vectors and reservoirs, and updated land cover maps derived from satellite imagery. Moreover, the mapping method proposed here could be used to assess the risk of CL according to different scenarii of urban planning or agricultural development in rural areas. Given the importance of CL as one of the main public health problem in Algeria and more largely in the Maghreb region, both in terms of clinical and economic aspects, such tools are needed in the implementation of surveillance and control programmes against leishmaniasis, in support of local authorities. Indeed, the prediction of epidemics and early warning remain a high research priority to improve the response of CL control programmes and risk assessment for future land use, in the absence of a vaccine and efficient prevention methods.

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Acknowledgments

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The ‘‘SPOT-5’’ images were acquired for this study under the CIRAD ‘‘Emergence’’ ATP project, and benefited from the programme (ISIS incentive to scientific use of SPOT images) of the CNES (Centre National d’Etudes Spatiales). The authors thank Peter Biggins, CIRAD, for his help in English editing. They also thank the

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two anonymous reviewers for their comments and suggestions on the earlier version of the manuscript. They significantly contributed to its improvement.

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Appendix A. Supplementary data

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Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.meegid.2014.09. 036.

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