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Rogers and Williams (1993) describe the application of NOAA-AVHRR GAC-NDVI data and synoptic meteorological temperature data to the problem of predicting the larger-scale distribution of Glossina morsitans in Zimbabwe, Kenya and Tanzania. Temperature data (a critical climatic variable in determining the survival of tsetse) were included in the analysis by interpolating data from meteorological stations to grid squares covering the whole of
PRINCIPLES AND PRACTICE OF CLINICAL PARASITOLOGY
Zimbabwe. When these data were combined with NDVI variables in a linear discriminant analysis, the historical distribution of G. morsitans in Zimbabwe, as described in Ford and Katondo (1977), was predicted with an accuracy of over 80%, thereby indicating the utility of remotely sensed data in predicting fly distributions at broader spatial scales. However, the statistical difficulties of selecting those climatic and remotely sensed variables of apparent importance in determining the observed distribution pattern were highlighted by Rogers and Randolph (1993). These authors re-assessed the distributions of G. morsitans in Zimbabwe, Kenya and Tanzania via predictions from a discriminant analysis of several components of NDVI (monthly mean, minimum, maximum and range), elevation and synoptic temperature data. Although they were able to predict the distribution to an overall accuracy of 82%, the key variables contributing most to the prediction varied between the countries (Figures 2.12-2.14, see Plates I-III). This could suggest that at the very least the environmental-vector abundance relationship varies at regional scales, thereby precluding the building of general global predictive models. Alternatively, the results may indicate difficulties with the analysis of complex multivariate data. Recent work investigating the application of temporal Fourier analysis (Rogers and Williams, 1993) and multivariate techniques based on likelihood principles (Robinson et al.,
1997) to climate and remotely sensed vegetation data for predicting fly distributions has attempted to address this issue.
The recent renewed global interest to achieve control of this disease has reinitiated efforts to gain a better understanding of the geographic distribution of lymphatic filariasis at all spatial scales from global, regional to within-endemic country scales (Michael and Bundy, 1997). Recent disease mapping activities have therefore focused on mapping the available information on geographic patterns of infection and disease cases, both for descriptive purposes and for the provision of data for measures of need and
populations at risk, using data at the global and regional scales (Michael and Bundy, 1997). The distribution of cases at a finer spatial scale, however, was undertaken by Thompson et al. (1997), who applied an integrated RS-GIS approach to understanding disease distribution among villages within the Southern Nile Delta.
Mapping and Analysis of Filariasis Distribution at the Global and Regional Levels
Michael and Bundy (1997) used a newly assembled database on country-specific estimates of case prevalence (Michael et al., 1996), to construct the first maps of the spatial distribution of lymphatic filariasis case prevalences at both the global and regional levels (Figure 2.15A,B). A striking feature of the resulting maps was the high degree of geographical heterogeneity observed in the estimated country prevalences. In general, countries with bancroftian filariasis (the more important of the two disease forms) in Asia and South America appear to have lower prevalences compared to estimated country prevalences in the sub-Saharan African and Pacific Island regions (Figure 2.15A). The map for brugian filariasis (Figure 2.15B) appears to be relatively more homogeneous, although there is a slightly higher prevalence in the eastern regions of the distribution.
The authors investigated the apparent spatial heterogeneity for bancroftian filariasis distribution using simple statistical models for assessing the significance of area data (Cliff and Hagett, 1988). In particular, the approach of Poisson probability mapping was employed to construct maps of the statistical significance of the difference between disease risk in each study area and the overall risk averaged over the entire map. Such a mapping procedure not only stabilizes the individual prevalence rates for population size variations (which contributes to apparent heterogeneity), but may also provide a tool for highlighting truly anomalous areas (Bailey and Gatrell, 1995). The global probability map for bancroftian filariasis is displayed in Figure 2.16 and, although as expected the transformation of the country prevalences to a
Fig 2.15 Geographical distributions of bancroftian (A) and brugian (B) filariasis case prevalences based on the crude GBD estimates. Circles denote the corresponding prevalences (%) estimated for various Pacific islands and vary in size proportionately with the prevalence of each island. The figures in brackets indicate the number of countries