Arboviral disease record data - Dengue and Chikungunya, Brazil, 2013-2020.

Autor: da Silva Neto SR; Universidade de Pernambuco, Programa de Pós-Graduação em Engenharia da Computação, Recife, 50720-001, Brazil., Tabosa de Oliveira T; Universidade de Pernambuco, Programa de Pós-Graduação em Engenharia da Computação, Recife, 50720-001, Brazil., Teixiera IV; Universidade de Pernambuco, Programa de Pós-Graduação em Engenharia da Computação, Recife, 50720-001, Brazil., Medeiros Neto L; Universidade de Pernambuco, Programa de Pós-Graduação em Engenharia da Computação, Recife, 50720-001, Brazil., Souza Sampaio V; Fundação de Medicina Tropical Dr. Heitor Vieira Dourado, Manaus, 69040-000, Brazil.; Instituto Todos pela Saúde, São Paulo, 01310-942, Brazil., Lynn T; Irish Institute of Digital Business, Dublin City University, Dublin, 9, Ireland., Endo PT; Universidade de Pernambuco, Programa de Pós-Graduação em Engenharia da Computação, Recife, 50720-001, Brazil. patricia.endo@upe.br.
Jazyk: angličtina
Zdroj: Scientific data [Sci Data] 2022 May 10; Vol. 9 (1), pp. 198. Date of Electronic Publication: 2022 May 10.
DOI: 10.1038/s41597-022-01312-7
Abstrakt: One of the main categories of Neglected Tropical Diseases (NTDs) are arboviruses, of which Dengue and Chikungunya are the most common. Arboviruses mainly affect tropical countries. Brazil has the largest absolute number of cases in Latin America. This work presents a unified data set with clinical, sociodemographic, and laboratorial data on confirmed patients of Dengue and Chikungunya, as well as patients ruled out of infection from these diseases. The data is based on case notification data submitted to the Brazilian Information System for Notifiable Diseases, from Portuguese Sistema de Informação de Agravo de Notificação (SINAN), from 2013 to 2020. The original data set comprised 13,421,230 records and 118 attributes. Following a pre-processing process, a final data set of 7,632,542 records and 56 attributes was generated. The data presented in this work will assist researchers in investigating antecedents of arbovirus emergence and transmission more generally, and Dengue and Chikungunya in particular. Furthermore, it can be used to train and test machine learning models for differential diagnosis and multi-class classification.
(© 2022. The Author(s).)
Databáze: MEDLINE
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