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Simarro, P. P., Cecchi, G., Franco, J. R., Paone, M., Diarra, A., Ruiz-Postigo, J. A., Fèvre, E. M., Mattioli, R. C., & Jannin, J. G. (2012). Estimating and Mapping the Population at Risk of Sleeping Sickness. PLoS Neglected Tropical Diseases, 6, e1859.
https://doi.org/10.1371/journal.pntd.0001859

has been cited by the following article:

• TITLE: Environmental Mapping of Potential Habitats for Anthropod Vectors of Trypanosomiasis in Northern Nigeria

  AUTHORS: Sadiq Abdullahi Yelwa, Kabiru Bilalu, Abdulhamid Yahuza, Nura Garba

  KEYWORDS: Trypanosomiasis, Habitat, Cost Effective Measures, Principal Component Analysis (PCA), El–Niño Southern Oscillations (ENSO)

  JOURNAL NAME: Journal of Geoscience and Environment Protection, Vol.9 No.9, September 18, 2021

  ABSTRACT: This paper presents analysis of a cost effective methodology using remotely sensed data analysed within a geographical systems (GIS) environment for mapping out potential habitats of anthropod vectors responsible for trypanosomiasis in northern Nigeria. These geographical locations are areas with high population of livestock in Nigeria. Animal Trypanosomiasis is considered as an arthropod-borne viral disease which is endemic in about 37 countries of the sub-Saharan Africa and in particular northern Nigeria. This anthropod-borne viral disease remains a threat to both humans and livestock in many communities, and the outbreak of such diseases is shown to relate to fluctuations in the changing climate mostly experienced whenever there are changes in global precipitation which also relates to changes in sea surface temperatures otherwise known as “El Niňo Southern Oscillations” (ENSO). Monthly Satellite imageries in the form of Normalised Difference Vegetation Index (NDVI) at 250 meters spatial resolution obtained from NASA-MODIS/CMD were subjected to principal component analysis utilizing the standardized principal components within a GIS environment and supplemented with Digital Elevation Model (DEM) data in the analysis. Results from the maps showed that pockets of probable habitats of these anthropod vectors responsible for trypanosomiasis mostly located around forest islands characterized by dry woodland and savanna, and in some cases around gallery forests and few lowland and riverine areas. This study provides baseline information for policy makers in Nigeria and other stakeholders as a cost effective measure for mapping potential habitats for anthropod vectors responsible for trypanosomiasis in Northern Nigeria. Further studies are encouraged so as to clearly understand the magnitude and actual locations of the habitat of this vector and find ways of targeting their locations for minimizing or even eradicating these vectors.