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ARTICLES

Using GIS to Model and Forecast HIV/AIDS Rates in Africa, 1986–2010

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Pages 33-53 | Received 01 Jul 2005, Accepted 01 Jun 2007, Published online: 31 Jan 2008

Figures & data

Figure 1 HIV surveillance data points.

Figure 1 HIV surveillance data points.

Figure 2 Interpolated smooth surface maps of HIV/AIDS prevalence rates for 1986, 1990, 1994, 1999, and 2003 for IDW and kriging. IDW = inverse distance weighted.

Figure 2 Interpolated smooth surface maps of HIV/AIDS prevalence rates for 1986, 1990, 1994, 1999, and 2003 for IDW and kriging. IDW = inverse distance weighted.

Figure 3 HIV/AIDS prevalence rates (percent) by country for 1986, 1990, 1994, 1999, and 2003 for IDW and kriging. IDW = inverse distance weighted.

Figure 3 HIV/AIDS prevalence rates (percent) by country for 1986, 1990, 1994, 1999, and 2003 for IDW and kriging. IDW = inverse distance weighted.

Table 1 Average HIV/AIDS prevalence rates derived from the IDW interpolation method and smoothed using EPP software, 1986–2010

Table 2 Comparison of UNAIDS figures with IDW, kriging, and simple means for selected years using Kolmogorov–Smirnov Z test

Table 3 Comparison of HIV/AIDS rates for 2003 from IDW, kriging, UNAIDS, and simple averages

Table 4 Projected population living with HIV using the EPP software package, 2004–2010

Figure 4 Curve fitting for HIV/AIDS prevalence rates for selected countries in east and central Africa.

Figure 4 Curve fitting for HIV/AIDS prevalence rates for selected countries in east and central Africa.

Figure 5 Curve fitting for HIV/AIDS prevalence rates for selected countries in West Africa.

Figure 5 Curve fitting for HIV/AIDS prevalence rates for selected countries in West Africa.

Figure 6 Curve fitting for HIV/AIDS prevalence rates for selected countries in southern Africa.

Figure 6 Curve fitting for HIV/AIDS prevalence rates for selected countries in southern Africa.

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