Authors:
Marcos Barreto
1
;
Juracy Bertoldo
1
;
Alberto Sironi
1
and
Vanderson Sampaio
2
Affiliations:
1
AtyImoLab, Computer Science Department, Federal University of Bahia (UFBA), Salvador and Brazil
;
2
Amazonas State Foundation for Health Surveillance (FVS-AM), Manaus, Brazil, State University of Amazonas (UEA), Manaus and Brazil
Keyword(s):
Data Analytics, Data Linkage, Visual Mining, Data as a Service.
Abstract:
Malaria is still a worrying disease worldwide, being responsible for around 219 million cases reported in 2017 and around 435,000 deaths a year. The consensus among researchers, governmental bodies and health professionals is that many countries have relapsed their investments and surveillance actions after a few years of apparent disease reduction. Brazil is within such countries and, consequently, is presenting a constant increase in the number of reported cases since 2016 (more than 20% a year). Given this context, the National Malaria Control Program (NMCP) promotes several actions to redirect the country towards the malaria elimination path. Among such actions, the improvement of the surveillance ecosystem is considered crucial to allow efficacy of control actions, including vector control as well as early diagnosis and prompt treatment. In this paper, we present our efforts in designing a visual mining tool allowing descriptive and predictive analytics over an integrated databa
se comprising malaria surveillance data, climate and vector control data. This tool has been used as a “data service” by NMCP and partner researchers for validation purposes. So far, our results have demonstrated that surveillance and combat actions can be highly improved by using this tool.
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