Spatial and Statistics for Profiling Risk Factors of Diseases: A Case Study of Tuberculosis in Malaysia

Abstract

Abstract Understanding concepts of a proper disease transmission risk is not a straightforward process. In the context of tuberculosis (TB) dynamics, the concepts require the exploration of two meticulous criteria to produce an accurate epidemic modelling of the risk areas of the disease. The criteria include interpreting the biological transmission of the disease and applying multidisciplinary approaches. Spatial statistics were used to evaluate the preferences of risk factors in Shah Alam, Malaysia. GIS-multicriteria decision making (MCDM) method and logistic regression method were specifically integrated to select the local risk factors and seven influential factors were ranked accordingly i.e. human mobility, high risk group, socio-economic status (SES), population, type of house, distance of factory and urbanisation. Each has relative risk rate that affects the cases and the combination of them will even impact more on the overall risk concentration of TB. Human–based factors are identified as dominant effects to the risk than biophysical factors, for example, a location of TB risk will be increased by four times if individuals are living together with people who have TB disease for a particular time period. This geospatial method is expected to predict a better factor prediction in identifying hotspot areas of the disease.