Evaluating the Surveillance System for Spotted Fever in Brazil Using Machine-Learning Techniques - Lopez, Diego Montenegro; de Mello, Flávio Luis; Giordano Dias, Cristina Maria; Almeida, Paula; Araújo, Milton; Magalhães, Monica Avelar; Gazeta, Gilberto Salles; Brasil, Reginaldo Peçanha (Frontiers in Public Health, 2017)
This work analyzes the performance of the Brazilian spotted fever (SF) surveillance system in diagnosing and confirming suspected cases in the state of Rio de Janeiro (RJ), from 2007 to 2016 (July) using machine-learning techniques. Of the 890 cases reported to the Disease Notification Information System (SINAN), 11.7% were confirmed as SF, 2.9% as dengue, 1.6% as leptospirosis and 0.7% as tick bite allergy, with the remainder being diagnosed as other categories (10.5%) or unspecified (72.7%). This study confirms the existence of obstacles in the diagnostic classification of suspected cases of SF by clinical signs and symptoms. Unlike man-capybara contact (1.7% of cases), man-tick contact (71.2%) represents an important risk indicator for SF. The analysis of decision trees highlight some clinical symptoms related to SF patient death or cure, such as: respiratory distress, convulsion, shock, petechiae, coma, icterus and diarrhea. Moreover, cartographic techniques document patient transit between RJ and bordering states and within RJ itself. This work recommends some changes to SINAN that would provide a greater understanding of the dynamics of SF and serve as a model for other endemic areas in Brazil.