Intelligent epidemiological surveillance in the Brazilian semiarid
IEEE Int. Conf. E-Health Netw., Appl. Serv., HEALTHCOM
; 2021.
Article
in English
| Scopus | ID: covidwho-1214727
ABSTRACT
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. © 2021 IEEE.
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Observational study
Country/Region as subject:
South America
/
Brazil
Language:
English
Journal:
IEEE Int. Conf. E-Health Netw., Appl. Serv., HEALTHCOM
Year:
2021
Document Type:
Article
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