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Malaria risk mapping and prediction in Cote d'Ivoire

Van Wijk, Charles M. (1999-12)

Thesis (M.Sc.) -- University of Stellenbosch, 1999.

Thesis

ENGLISH ABSTRACT: Malaria, being the most important vector-borne disease in Africa, is still leaving an ever-increasing trail of health and economic impediments within the developing world. Since malaria's spatial distribution and intensity are influenced by numerous environmental variables, medical geography, the study of the spatial human-environmental interrelationship of disease, can make a significant contribution to modelling disease risk for different geographic locations, as based on environmental determinants. The investigation consequently included the development of malaria risk maps within northern Cote d'Ivoire, an analysis of its spatial variation through exploratory spatial data analysis and Geographical Information System (GIS) functionality, and the search for predictive models using environmental variables. Malaria transmission intensities of the Anopheles fonestus and Anopheles gambiae mosquito species have been studied as factors having a direct influence on the malaria transmission cycle, whilst indirect environmental influencing factors which were studied included: wetland rice production, water bodies and their flooding pattern, temperature and relative humidity ranges, amount of rainfall and insolation, distances between aquatic breeding grounds and villages, and vegetation cover. Data have been obtained from ongoing surveys carried out by Institut Pierre Richet/OCCGE and the WARDA Health Research Consortium within a random selection of 24 villages in northern Cote d 'Ivoire, district level meteorological observations, and GIS and satellite imagery computations. The data sets have been manipulated, standardised and reduced to three malaria risk indicators, and five uncorrelated environmental measures through principal component analysis. The application of exploratory spatial data analysis, in which the spatial capabilities of GIS played a significant role, involved: a) Mapping the disease's spatial distribution and extreme risk areas by means of proportional circle and probability maps. b) Exploring and modelling first and second order spatial disease variation through Triangulated Irregular Network (TIN) interpolation, semi-variograms, and trend surface analysis. c) Visual and statistical detection of associations between malaria and environmental determinants, by means of proportional circle maps and multiple regression analysis, in the search for predictive models. Residuals of final models have been mapped to investigate possible patterns in over- and underestimation. A general increase in disease incidence and transmission levels during the study period, as well as areas of high risk have been identified. Investigation of first order variation highlighted these high risk areas. Semi-variograms revealed a high degree of spatial variability, with little or no spatial dependence between observations. No clear second order variation could therefore be identified, nor any reasonable first or second order spatial variation model be deduced. Relative humidity and temperature measures appear visually to be associated with malaria occurrence, and were statistically confirmed to account for a substantial percentage of disease variance through regression modeling, together with distance and vegetation density measures. However, the small proportion of variance explained by some models, together with the irregular spatial distribution and high levels of residuals, made reliable prediction impossible. It is therefore clear that the relationships between malaria morbidity or transmission and the environmental factors are complex and often highly site specific, possibly requiring higher order polynomial functions and a wide spectrum of determinants, still to be identified and included in subsequent studies.

AFRIKAANSE OPSOMMING: Malaria is tans die belangrikste en dodelikste insekoordraagbare siekte in Afrika, wat 'n al hoe groter wordende gesondheids- en ekonomiese impak laat in die ontwikkelende wereld. Aangesien die verspreiding en intensiteit van malaria be"invloed word deur 'n groot aantal omgewingsfaktore, kan mediese geografie - die studie van siektes se ruimtelike mens-omgewing interverwantskappe - 'n beduidende rol speel ter voorspelling van gesondheidsrisiko' s in verskillende geografiese areas, soos gebaseer op omgewingskenmerke. Die ondersoek het dus die volgende ingesluit: die ontwikkeling van malariarisikokaarte vir die noordelike Ivoorkus studiegebied, 'n ontleding van ruimtelike variasies deur eksploratiewe ruimtelike data-analise en Geografiese Inligtingstelsel (GIS) funksionaliteit, en die soektog na betroubare voorspellingsmodelle soos gebaseer op omgewingsfaktore. Die intensiteit van malaria-oordrag deur die Anopheles funestus en Anopheles gambiae muskietspesies is bestudeer as faktore wat 'n direkte invloed het op die malaria-oordrag siklus, terwyl indirekte omgewingsfaktore wat bestudeer is ingesluit het: vleilandrysverbouing, waterliggame en hul vloedingspatrone, temperatuur- en relatiewe humiditeitsgrense, hoeveelheid reenval en sonskyn, afstande tussen geskikte broeigebiede en menslike woongebiede, en plantegroei digtheid. Malaria en omgewingsdata is verkry vanuit voortgesette opnames gedoen deur Institut Pierre Richet/OCCGE en die WARDA Gesondheidsnavorsing Konsortium in 'n steekproefvan 24 noord-Ivoorkus woongebiede, distriksvlak meteorologiese observasies, en GIS en satellietbeeld verwerkings. Ingesamelde data is gemanipuleer, gestandaardiseer en verwerk tot drie indikators van malariarisiko, en vyf onverwante omgewingsfaktore soos bepaal deur hoofkomponentanalise. Die toepassing van eksploratiewe ruimtelike data-analise, waarin die ruimtelike funksionaliteit van GIS 'n beduidende rol gespeel het, het behels: a) Die kartering van die siekte se ruimtelike verspreiding en van hoerisikogebiede deur middel van proporsionele puntsimbool- en waarskynlikheidskaarte. b) Die ondersoek en modellering van eerste- en tweede-orde ruimtelike siektevariasies deur getrianguleerde onreelmatige netwerk model (TIN) interpolasie, semivariogramme en oppervlakspatroon-analise. c) Die vasstelling van sigbare en statistiese verhoudings tussen malariavoorkoms en omgewingsfaktore, deur proporsionele puntsimboolkaarte en meervoudige regressieanalise, in die soeke na betroubare voorspellingsmodelle. Residuele van finale modelle is gekarteer om moontlike patrone in oor- en onderskatting te ondersoek. 'n Algemene toename in siektevoorkoms en -oordrag is gedurende die navorsingsperiode geldentifiseer. Areas met 'n hoe malariarisiko is bepaal, en die ondersoek tot eerste-orde ruimtelike variasies het hierdie hoerisikogebiede beklemtoon. Semi-variogramme het 'n hoe mate van ruimtelike variasie getoon, met min, indien enige, ruimtelike verband tussen observasies. Geen duidelike tweede-orde variasie kon dus bepaal word nie, asook geen aanvaarbare eerste- of tweede-orde ruimtelike variasiemodel afgelei word nie. Relatiewe humiditeit- en temperatuurmaatstawwe is visueel gerdentifiseer as moontlike omgewingsfaktore wat die voorkoms van malaria kan belnvloed, en is ook statisties deur regressiemodellering bevestig om 'n relatief groot persentasie van siektewisseling te verklaar, tesame met metings van afstande en plantegroeidigtheid. Die lae vlak van variansie verklaar deur sommige modelle, tesame met oneweredige ruimtelike verspreiding en hoe waardes van residuele, het betroubare voorspellings egter onmoontlik gemaak. Dit is derhalwe duidelik dat die verhoudings tussen malariavoorkoms of -oordrag en omgewingsfaktore kompleks, en dikwels tot spesifieke kombinasies in 'n area beperk is. Dit benodig waarskynlik hoer-orde polinomiale funksies en 'n wyer spektrum omgewingsdeterminante, wat nog gerdentifiseer en in opvolgstudies gebruik kan word.

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