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Frontiers in Public Health 2017

Evaluating the Surveillance System for Spotted Fever in Brazil Using Machine-Learning Techniques.

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Diego Montenegro Lopez
Flávio Luis de Mello
Cristina Maria Giordano Dias
Paula Almeida
Milton Araújo
Monica Avelar Magalhães
Gilberto Salles Gazeta
Reginaldo Peçanha Brasil

키워드

요약

This work analyses 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 highlights 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.

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