Spatial Modelling of Malaria Risk Zones Using Environmental, Anthropogenic Variables and GeograPhical Information Systems Techniques

David Chikodzi^1,

¹Department of Physics, Geography and Environmental Science, Great Zimbabwe University, Masvingo

Cite this paper

David Chikodzi. Spatial Modelling of Malaria Risk Zones Using Environmental, Anthropogenic Variables and GeograPhical Information Systems Techniques. Journal of Geosciences and Geomatics. 2013; 1(1):8-14. doi: 10.12691/JGG-1-1-2

Abstract

Malaria is one of the major public health problems in Zimbabwe. The research was aimed at deriving a predictive model for malaria epidemiology in the Masvingo province of Zimbabwe at a scale that is sensitive to local changes in risk factors. Eight risk factors were used in the model build up. Each risk factor was first spatially classified in a geographic information system (GIS) according to how it promotes malaria incidence. The factors were then weighted using a pair wise comparison matrix which is part of analytical hierarchy process (AHP). The final malaria prediction model was then prepared by combining all risk factors and their derived weights through the index overlay model in a GIS. Results showed that northern districts of Chivi, Masvingo and Gutu have the least risk of malaria epidemic while as the southern districts of Chiredzi and Mwenezi have the highest risk. In terms of area, places classified as low risk covered 18.86%, moderate risk 35.67% and high risk 45.45% of the total area of the province. Predictions made by the derived model compared favourably with observations from field trials, health clinics and other models being used in Zimbabwe but had finer spatial coverage than previous models.

Keywords

AHP, GIS, index overlay model, Masvingo province, malaria

Copyright

This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

References

[1]	Teveredzi, V. Malaria in pregnancy, T.O.T., Case Study,: Harare, Zimbabwe.2000.
[2]	Crees, M.J., Mhlanga, T.H. Malaria Prevalence In Zimbabwe and Parasite Survey Of 1983. Zimbabwe Science News. 19:114-117. 1985.
[3]	Mabaso MLH, Craig M, Vounatsou P, Smith T: Towards empirical description of malaria seasonality in southern Africa: the example of Zimbabwe.Trop Med Int Health 2005, 10:909-918.
[4]	Zhou, Xiao-Nong, Shu-Shen Zhou, Xia Zi-Guang. Malaria elimination strategy and challenges in People’s Republic of China. Malaria Journal 2012.(Suppl 1):P138.
[5]	USAID. PRESIDENT’S MALARIA INITIATIVE: Zimbabwe Malaria Operational Plan FY 2013. http://pmi.gov/countries/mops/fy13/zimbabwe_mop_fy13.pdf. 2013. [Accessed Aug 14, 2013].
[6]	World Health Organization International travel and health: situations as on 1 January 2008. Geneva: World Health Organization. 2008.
[7]	Freeman, T.W. Binga District - Base Line Survey Of The Malaria Situation. Save The Children Fund - UK. Unpublished. 1995.
[8]	Waller LA, Carlin BP, Xia H, Gelfand AE. Hierarchical spatio-temporal mapping of disease rates. J Am Stat Assoc 92: 607-617.
[9]	MacNab YC, and Dean CB. Autoregressive spatial smoothing and temporal spline smoothing for mapping rates. Biometrics 57: 949-956.
[10]	Snow RW, Marsh K,and Le Sueur D. The need for maps of transmission intensity to guide malaria control in Africa. Parasitol Today 12: 455-457.
[11]	Haydon DT, Randall DA, Matthews L, Knobel DL, Tallents LA. Low-coverage vaccination strategies for the conservation of endangered species. Nature 443: 692-695.
[12]	Caldas de Castro M, Yamagat Y, Mtasiwa D, Tanner M, Utzinger J, (2004) Integrated urban malaria control: a case study in Dar es Salaam, Tanzania. Am J Trop Med Hyg 71: 103-117, 2004.
[13]	Gemperli A, Sogoba N, Fondjo E, Mabaso M, Bagayoko M. Mapping malaria transmission in West and Central Africa. Trop Med Int Health 11: 1032-1046.2006.
[14]	Kiszewski A, Mellinger A, Spielman A, Malaney P, Sachs S,(2004) A global index representing the stability of malaria transmission. Am J Trop Med Hyg 70: 486-498. 2004.
[15]	Central Statistics Office (CSO) “Zimbabwe Population Profile” http://www.zimstat.co.zw/dmdocuments/Census/Census.pdf 2012.
[16]	Chikodzi, D and G. Mutowo, G.“Agro-Ecological Zonation of Masvingo Region: Land Suitability Classification Factoring In Climate Change, Variability Swings and New Technology”. 1:318.
[17]	Malherbe.J, F.A Engelbrecht, W. A. Landman, and C.J Engelbrecht,“Tropical systems from the Southwest Indian Ocean making landfall over the Limpopo River Basin, Southern Africa: a historical perspective”, International Journal of Climatology, Royal Meteorological Society, 2012.
[18]	Ebi K.L. ,J Hartman, N. Chan,J. Mcconnell, M. Schlesinger, J.Weyant. Climate Suitability for Stable Malaria Transmission in Zimbabwe Under Different Climate Change Scenarios Climatic Change Volume 73, Issue 3 , pp 375-393. 2005.
[19]	Crees, M.J., Mhlanga, T.H. Malaria Prevalence In Zimbabwe and Parasite Survey Of 1984. Zimbabwe Science News. 19:114-117.1985.
[20]	USGS. Digital elevation Models (DEM) http://data.geocomm.com/dem/. 2001. [Accessed Mar 15, 2004].
[21]	GLOVIS. Landsat Image Download. www.glovis.com. 2013[Accessed Sept, 2013].
[22]	ITC. ILWIS user manual. International Institute for Aerospace Survey and Earth Sciences (ITC), Netherlands.2005.
[23]	Jepson, W.F.Moutia, A. and Courtois, C. The malaria problem in Mauritius: the bionomics of Mauritian Anopheline. Bulletin Entomological Research 38: 177-208. 1947.
[24]	Le Sueur, D. Aspects Of Survival Of Anopheles arabiensis. PhD Thesis - Still In Publication. Medical Research Institute, Durban, South Africa. 1994.
[25]	Bayoh M. N, Lindsay S. W. Effect of temperature on the development of the aquatic stages of Anopheles gambiae sensu stricto (Diptera: Culicidae). Bull. Entomol. Res. 2003, 93:174-179, 2003.
[26]	Minakawa N, Sonye G, Mogi M, Yan G. Habitat characteristics of Anopheles gambiaes.s. larvae in a Kenyan highland. Med. Vet. Entomol. 2004, 18:301-305, 2004.
[27]	Kebede A, McCann JC, Kiszewski AE, Ye-Ebiyo Y. New evidence of the effects of agro-ecologic change on malaria transmission. American Journal of Tropical Medicine and Hygiene. 2005;73:676-680.
[28]	Rincón-Romero M. E and Londoño J.E. Mapping malaria risk using environmental and anthropic variables. Rev Bras Epidemiol 2009; 12(3): 338-54.
[29]	Kazembe, Lawrence N, Immo Kleinschmidt, Timothy H Holtz and Brian L Sharp .Spatial analysis and mapping of malaria risk in Malawi using point-referenced prevalence of infection data. International Journal of Health Geographics 2006, 5:41.
[30]	Saaty, T.L. The Analytic Hierarchy Process, New York: McGraw Hill. International, Translated to Russian, Portuguese, and Chinese, Revised editions, Paperback (1996, 2000), Pittsburgh: RWS Publications.2001.
[31]	Carter. B. Geographic Information System for Geoscientists: Modelling with GIS, Pergamon, Ontario, PP. 319-470, 1991.
[32]	Ministry of Health. National Health Information System, Ministry of Health and Child Welfare (2012). Harare.
[33]	MARA. Zimbabwe: Distribution of Endemic Malaria. http://www.mara.org.za, July 2001. [Accessed Feb. 10, 2013].
[34]	Taylor, P., Mutambu, S.L. A review of the malaria situation in Zimbabwe with special reference to the period 1972-1981. Transactions of the Royal Society of Tropical Medicine & Hygiene. 80(1):12-9. 1986.
[35]	World Health Organization (2011) An Information bulletin of the WCO Zimbabwe Vol. 8, May 2011.