Modelling the Spatial Distribution of the Anopheles Mosquito for Malaria Risk Zoning Using Remote Sensing and GIS: A Case Study in the Zambezi Basin, Zimbabwe

Remote Sensing and Geographic Information System was used to develop a spatial risk malaria distribution model based on environmental suitability for survival of the Anopheles gambie sp. Complex (A. arabiensis and A. gambae), the vector known to transmit malaria in Zimbabwe (Masendu, 1996). Employing geostatistical techniques, spatial analysis of environmental factors that contribute to the spread of the malaria vector was conducted to develop a malaria risk model that could be used in effective malaria control planning in Zimbabwe. The study was conducted in the Piriwiri, Umfuli and Magondi communal lands of Zimbabwe. A model was developed that defined malaria hot spots in the communal lands where attention must be given in developing plans and strategies for malaria control. Environmental data collected from satellite images and validated by fieldwork were used in the study. Based on expert knowledge, specific environmental factors favourable for Anopheles malaria vector were identified. This information was then used to predict the suitability of the area for the Anopheles mosquito using Indicator Kriging Algorithm (Isaacs et al., 1989). This method calculated the probability of exceeding an environmental indicator threshold (this allowed the prediction that a particular area (location) in the communal lands is suitable for the survival and spread of the Anopheles) and integrated them into a potential vector distribution model for the area. This model was used to determine areas that are potentially risky for malaria. Again the spatial distribution of malaria was calculated, based on clinical malaria data and accessibility to the clinics, and compared with the potential vector distribution zones to determine areas with high malaria risk. Except a few areas in Umfuli that were highly favourable for the Anopheles mosquito, most of the communal lands were not suitable for anopheles to survive indicating that malaria incidences are generally associated with highly favourable areas for the vector. Combining GIS and remote sensing applications with geostatistical analysis is a promising approach to define malaria risk areas in Zimbabwe. However, further quantitative research is necessary to validate the relationships within the malaria transmission system, especially on the vector and the human environment aspects.

[1]  Raja Sengupta.  Simulation Modelling within Collaborative Spatial Decision Support Systems Using "Cause-Effect" Models and Software Agents , 2009 .

[2]  M. Craig,et al.  Towards empirical description of malaria seasonality in southern Africa: the example of Zimbabwe , 2005, Tropical medicine & international health : TM & IH.

[3]  Xinyue Ye,et al.  Online Flood Information System: REST-Based Web Service , 2014, Int. J. Appl. Geospat. Res..

[4]  Penelope Vounatsou,et al.  Rise in malaria incidence rates in South Africa: a small-area spatial analysis of variation in time trends. , 2002, American journal of epidemiology.

[5]  G. Chowell,et al.  The spatial and temporal patterns of falciparum and vivax malaria in Perú: 1994–2006 , 2009, Malaria Journal.

[6]  Marlize Coleman,et al.  Using the SaTScan method to detect local malaria clusters for guiding malaria control programmes , 2009, Malaria Journal.

[7]  K. Kloter,et al.  Manual on environmental management for mosquito control with special emphasis on malaria vectors. , 1983, WHO offset publication.

[8]  Introductory remarks on the application of remote sensing and geographic information systems to epidemiology and disease control , 1991 .

[9]  S. Wisely,et al.  Patterns of Spatio-Temporal Distribution, Abundance, and Diversity in a Mosquito Community from the Eastern Smoky Hills of Kansas , 2013, Journal of vector ecology : journal of the Society for Vector Ecology.

[10]  B. Mayala,et al.  Spatio-temporal variation in malaria transmission intensity in five agro-ecosystems in Mvomero district, Tanzania. , 2010, Geospatial health.

[11]  Timothy L. Nyerges,et al.  Design Considerations for an Internet Portal to Support Public Participation in Transportation Improvement Decision Making , 2006 .

[12]  H. Masendu.  The role of Anopheles arabiensis (Diptera: Culicidae) in malaria transmission and control in Gokwe and Binga districts, Zimbabwe.. , 1996 .

[13]  U. Haque,et al.  Spatial malaria epidemiology in Bangladeshi highlands , 2009, Malaria Journal.

[14]  Nathan Chan,et al.  Climate Suitability: For Stable Malaria Transmission in Zimbabwe Under Different Climate Change Scenarios , 2002 .

[15]  M. Soltani,et al.  Spatial analysis and mapping of malaria risk in an endemic area, south of Iran: a GIS based decision making for planning of control. , 2012, Acta tropica.

[16]  Freek D. van der Meer,et al.  Sequential indicator conditional simulation and indicator kriging applied to discrimination of dolomitization in GER 63-channel imaging spectrometer data , 1994 .

[17]  P. H. Martin,et al.  Malaria and climate: sensitivity of malaria potential transmission to climate , 1995 .

[18]  S. Fawcus,et al.  Maternal mortality in rural and urban Zimbabwe: social and reproductive factors in an incident case-referent study. , 1993, Social science & medicine.

[19]  S. Fawcus,et al.  A community based investigation of causes of maternal mortality in rural and urban Zimbabwe. Maternal Mortality Study Group. , 1995, The Central African journal of medicine.

[20]  A. Kraemer,et al.  Mapping Urban Malaria and Diarrhea Mortality in Accra, Ghana: Evidence of Vulnerabilities and Implications for Urban Health Policy , 2012, Journal of Urban Health.

[21]  R. Hunt,et al.  Distribution of mosquitoes belonging to the Anopheles gambiae complex, including malaria vectors, south of latitude 15°S. , 1993 .

[22]  A. Worku,et al.  Malaria Infection Has Spatial, Temporal, and Spatiotemporal Heterogeneity in Unstable Malaria Transmission Areas in Northwest Ethiopia , 2013, PloS one.

[23]  Alemayehu Midekisa,et al.  Spatial synchrony of malaria outbreaks in a highland region of Ethiopia , 2012, Tropical medicine & international health : TM & IH.

[24]  J. M. V. Oomen,et al.  Incorporation of disease-control measures in irrigation, a multi-faceted task in design, construction, operation , 1990 .

[25]  D. Ayele,et al.  Spatial distribution of malaria problem in three regions of Ethiopia , 2013, Malaria Journal.

[26]  C. Holland,et al.  Ascaris co-infection does not alter malaria-induced anaemia in a cohort of Nigerian preschool children , 2013, Malaria Journal.

[27]  Yong Wang,et al.  Spatio-temporal analysis of malaria incidence at the village level in a malaria-endemic area in Hainan, China , 2011, Malaria Journal.

[28]  S. Chandiwana,et al.  Incorporating a human health component into the integrated development and management of the Zambezi River Basin: report of a PEEM mission to Zimbabwe, Zambia and Mozambique , 1994 .

[29]  M. Carling,et al.  Spatial Patterns of Avian Malaria Prevalence in Zonotrichia capensis on the Western Slope of the Peruvian Andes , 2013, Journal of Parasitology.

[30]  J. Weyant,et al.  Climate Suitability for Stable Malaria Transmission in Zimbabwe Under Different Climate Change Scenarios , 2005 .

[31]  Dick J. Veltman,et al.  Modulatory Effects of the Piccolo Genotype on Emotional Memory in Health and Depression , 2013, PloS one.