SPATIAL IDENTIFICATION OF HIGH-RISK OF HIV/AIDS IN KEFFI LGA USING SPATIAL AUTOCORRELATION AND KRIGING INTERPOLATION.

Abstract

Quite often, authorities and policies maker are confronted with the challenges to serve
society with the little resources at her disposal. More so, the need to distribute the limited
resources correctly to the needed persons and location remain nonnegotiable in the
present dispensation. Indeed, it became very expedient to sort for means of allocating
these minimum resources to the needy against all odds. The study seek to identify
communities with severe cases of HIV virus across Karu Local Government Area of
Nasarawa State and communities at high risk to this peril. The study used secondary data
from the Keffi General hospital, which covered a period of ten (10) years, from 2013 to
2023. The study made used of Moran’s I Statistics, Kriging Model and Semivariogram
model, and employed the ArcGIS software to analyzed the data. The finding shows that
the Moran’s I statistics recorded a positive value of 0.154147, z-score of 5.062777 and p-
value 0.0000 which is statistically significant and cluster. The Semivariogram showed
that spatial autocorrelation flatten out at the range of 0.605 while the kriging model give
the prediction of communities with high risk of HIV virus. This study conclude that
resources should be allocated to the identified communities alongside with intervention
program such as, campaign programs and medical awareness to stop further prevalence
of this virus.