Modeling A Vulnerability Index For Leprosy Using Spatial Analysis And Artificial Intelligence Techniques In A Hyperendemic Municipality In The Amazon

Leprosy is still a public health problem in the world and in Brazil. The environment conducive to the proliferation of this disease has a close relationship with precarious socioeconomic conditions, such as housing, sanitation, schooling and income. The present work analyzed an approach to combat leprosy through the modeling of a vulnerability index for the disease based on the socioeconomic information of the microregions defined by the census tracts belonging to the municipality of Santarem. Through the use of the information that compose this index, the process of creating clusters was carried out using the Kohonen self organizing maps of the census tracts analyzed according to their vulnerability to leprosy. Four clusters were found representing very high, low, and very low vulnerability for leprosy among the census tracts. We analyzed the 240 sectors that make up the urban area of Santarem, where 132 were classified in clusters with high vulnerability and 108 in clusters with low vulnerability. The study demonstrated that the use of organized socioeconomic information in an index that could express the vulnerability of census tracts to leprosy and the organization of these sectors in clusters are very powerful tools in the decision support process applied to the fight against leprosy in hyperendemic regions.