Geographical Information System and Geostatistical Modelling Approach for Spatial Risk Assessment of Tuberculosis Dynamics

Abstract

This paper explores the scenario of tuberculosis (TB) dynamics and to evaluate risk factors contributing to local cases in Shah Alam using geographical information system (GIS) and geostatistical approach. This risk assessment is important to model the overall potential hazards factors that cause harm to local TB. Geostatistical model was applied to predict TB risk surface according to the existing locations of TB risk factors. The unknown points of risk TB locations were defined based on known points of the existing locations using GIS and logistic regression analysis.  The risk map of TB has estimated 102 high risk localities in the study areas. Most the risk locations were concentrated around northern zone, central zone and a few areas around southern zone especially at 10 main sections of U17, U18, U19, U20, S7, S17, S18, S20, S27 and S28. Seven influential risk factors are identified to contribute to the local cases, including high risk group, Socio-economic status (SES), population, type of houses, human mobility, urbanisation, and distance to of factory. These results stimulate new attributes of risk factor and interpretation on the local disease phenomena. The combinations of GIS and geostatistical method have also demonstrated its geographical neighbourhood capabilities to predict local TB risk dynamics.