Identification of risk areas as a method of surveillance of dengue cases (2022)
- Authors:
- USP affiliated authors: DELBEM, ALEXANDRE CLÁUDIO BOTAZZO - ICMC ; LOPES, GESIEL RIOS - ICMC
- Unidade: ICMC
- DOI: 10.5753/ercemapi.2022.225892
- Subjects: DENGUE; ÁREA DE RISCO; VULNERABILIDADE
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Publisher: SBC
- Publisher place: Porto Alegre
- Date published: 2022
- Source:
- Título: Anais
- Conference titles: Escola Regional de Computação do Ceará, Maranhão e Piauí - ERCEMAPI
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
LOPES, Gesiel Rios e PELARIGO, Karina Jorge e DELBEM, Alexandre Cláudio Botazzo. Identification of risk areas as a method of surveillance of dengue cases. 2022, Anais.. Porto Alegre: SBC, 2022. Disponível em: https://doi.org/10.5753/ercemapi.2022.225892. Acesso em: 12 fev. 2026. -
APA
Lopes, G. R., Pelarigo, K. J., & Delbem, A. C. B. (2022). Identification of risk areas as a method of surveillance of dengue cases. In Anais. Porto Alegre: SBC. doi:10.5753/ercemapi.2022.225892 -
NLM
Lopes GR, Pelarigo KJ, Delbem ACB. Identification of risk areas as a method of surveillance of dengue cases [Internet]. Anais. 2022 ;[citado 2026 fev. 12 ] Available from: https://doi.org/10.5753/ercemapi.2022.225892 -
Vancouver
Lopes GR, Pelarigo KJ, Delbem ACB. Identification of risk areas as a method of surveillance of dengue cases [Internet]. Anais. 2022 ;[citado 2026 fev. 12 ] Available from: https://doi.org/10.5753/ercemapi.2022.225892 - Use of multicriteria analysis and map algebra to identify risk areas for multiple health aggravations
- MultiMapas: abordagem multiobjetivo para construção de mapas coropléticos de dados heterogêneos multifontes
- Allocation and Sizing of Distributed Generation with Data Mining Code of Repositories: DAMICORE
- MultiMaps: a tool for decision-making support in the analyzes of multiple epidemics
- Proposal of a framework for improving multi-criteria decision-making related to epidemics using heterogeneous spatial data and evolutionary algorithms
- Identification of risk areas using spatial clustering to improve dengue monitoring in urban environments
- Evolutionary algorithms for enhanced public health mapping
- On the effectiveness of genetic algorithms for the multidimensional knapsack problem
- Multimodality and the linkage-learning difficulty of additively separable functions
- Using smart sampling to discover promising regions and increase the efficiency of differential evolution
Informações sobre o DOI: 10.5753/ercemapi.2022.225892 (Fonte: oaDOI API)
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