Visual Data Mining of SARS Distribution Using Self-Organization Maps

Abstract

From a public health perspective, the socioeconomic conditions correlate with occurrences of infectious diseases. Our premise is that the number of SARS patients is a non-linear function of socioeconomic effects that are not normally distributed among regions. The objective was to integrate multivariate data sets representing social and economic factors to evaluate the hypothesis that regions with similar socioeconomic characteristics exhibit similar distributions of SARS disease. The SOM algorithm used the intrinsic distributions of 21 social and economic variables to classify 31 regions into five clusters. SOM determined clusters were compared with the distributions of SARS outcomes. The result picture shows that the variability between regions clusters was significant with respect to the distribution of SARS occurrence. Our study demonstrated a positive relationship between socioeconomic conditions and SARS outcomes in regions using the SOM method to overcome data and methodological challenges traditionally encountered in public health research. Results demonstrated that community health can be classified using socioeconomic variables and that the SOM method may be applied to multivariate socioeconomic health studies.