Intelligent Epidemiological Surveillance in the Brazilian Semiarid

Right after the Chinese example in conducting COVID-19 epidemic originated in Wuhan, the readiness to detect and respond by health authorities to local (sometimes global) epidemics has become central lately. Within the idea of health 4.0, information about the individual is essential in supporting public community health policies. This paper presents a proposal for an epidemiological surveillance system applied to arboviruses. Data mining techniques and Machine Learning (ML) are used to design mathematical models for detecting epidemics enhanced by Aedes Aegypti (vector for dengue, chikungunaya, yellow fever and zica). Based on data, it is proposed an adaptive manner to reach better stability on results. A Prove of Concept (PoC) is presented for dengue epidemics detection, a common endemic disease in the semiarid region of Brazil.