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Towards a kala azar risk map for Sudan: mapping the potential distribution of Phlebotomus orientalis using digital data of environmental variables
Author(s) -
Thomson M. C.,
Elnaiern D. A.,
Ashford R. W.,
Connor S. J.
Publication year - 1999
Publication title -
tropical medicine and international health
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.056
H-Index - 114
eISSN - 1365-3156
pISSN - 1360-2276
DOI - 10.1046/j.1365-3156.1999.00368.x
Subject(s) - woodland , geography , visceral leishmaniasis , vector (molecular biology) , logistic regression , altitude (triangle) , distribution (mathematics) , geographic information system , physical geography , ecology , forestry , leishmaniasis , cartography , biology , statistics , mathematics , mathematical analysis , biochemistry , geometry , gene , recombinant dna , immunology
Summary The need to define the geographical distribution of Phlebotomus orientalis results from its importance as the dominant vector of kala azar (visceral leishmaniasis) in Sudan. Recent epidemics of this disease in southern and eastern Sudan caused an estimated 1 deaths and have renewed the impetus for defining the ecological boundaries of the vector. This information is an essential prerequisite to the production of a risk map for kala azar. This study uses data on the presence and absence of P. orientalis from 44 collecting sites across the central belt of Sudan. A logistic regression model was used to estimate the probability of the presence of P. orientalis at each collecting site as a function of climatic and environmental variables (rainfall; temperature; altitude; soil type and the satellite‐derived environmental proxies – Normalized Difference Vegetation Index and Land Surface Temperature). The logistic regression model indicates mean annual maximum daily temperature and soil type as the most important ecological determinants of P. orientalis distribution. An initial risk map was created in a raster‐based geographical information system which delineates the area where P. orientalis may occur. This map was then refined using a mask layer indicating the known rainfall‐based boundaries of the distribution of Acacia‐Balanites woodland – a woodland type known to be associated with the distribution of this vector. The predictive performance of the risk map is discussed.