Assessing the effect of Aedes (Stegomyia) aegypti (Linnaeus, 1762) control based on machine learning for predicting the spatiotemporal distribution of eggs in ovitraps (2022)
- Authors:
- USP affiliated authors: PIOVEZAN, RAFAEL - FSP ; AZEVEDO, THIAGO SALOMÃO DE - FSP ; SALLUM, MARIA ANICE MUREB - FSP
- Unidade: FSP
- DOI: 10.1016/j.dialog.2022.100003
- Subjects: AEDES; DENGUE; APRENDIZADO COMPUTACIONAL; VIGILÂNCIA; DISTRIBUIÇÃO ESPACIAL
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: Dialogues in Health
- ISSN: 2772-6533
- Volume/Número/Paginação/Ano: v.1, art. 100003 [9p.], 2022
- Este periódico é de acesso aberto
- Este artigo é de acesso aberto
- URL de acesso aberto
- Cor do Acesso Aberto: gold
- Licença: cc-by
-
ABNT
PIOVEZAN, Rafael et al. Assessing the effect of Aedes (Stegomyia) aegypti (Linnaeus, 1762) control based on machine learning for predicting the spatiotemporal distribution of eggs in ovitraps. Dialogues in Health, v. 1, p. art. 100003 [9], 2022Tradução . . Disponível em: https://doi.org/10.1016/j.dialog.2022.100003. Acesso em: 09 jan. 2026. -
APA
Piovezan, R., Azevedo, T. S. de, Faria, E., Veroneze, R., Von Zuben, C. J., Von Zuben, F. J., & Sallum, M. A. M. (2022). Assessing the effect of Aedes (Stegomyia) aegypti (Linnaeus, 1762) control based on machine learning for predicting the spatiotemporal distribution of eggs in ovitraps. Dialogues in Health, 1, art. 100003 [9]. doi:10.1016/j.dialog.2022.100003 -
NLM
Piovezan R, Azevedo TS de, Faria E, Veroneze R, Von Zuben CJ, Von Zuben FJ, Sallum MAM. Assessing the effect of Aedes (Stegomyia) aegypti (Linnaeus, 1762) control based on machine learning for predicting the spatiotemporal distribution of eggs in ovitraps [Internet]. Dialogues in Health. 2022 ;1 art. 100003 [9].[citado 2026 jan. 09 ] Available from: https://doi.org/10.1016/j.dialog.2022.100003 -
Vancouver
Piovezan R, Azevedo TS de, Faria E, Veroneze R, Von Zuben CJ, Von Zuben FJ, Sallum MAM. Assessing the effect of Aedes (Stegomyia) aegypti (Linnaeus, 1762) control based on machine learning for predicting the spatiotemporal distribution of eggs in ovitraps [Internet]. Dialogues in Health. 2022 ;1 art. 100003 [9].[citado 2026 jan. 09 ] Available from: https://doi.org/10.1016/j.dialog.2022.100003 - SARS-CoV-2 and COVID-19: A genetic, epidemiological, and evolutionary perspective
- Linhagens do pensamento político
- The influence of urban heat islands and socioeconomic factors on the spatial distribution of Aedes aegypti larval habitats
- CER: referência mundial em entomologia de saúde pública
- Regional variation in life history traits and plastic responses to temperature of the major malaria vector Nyssorhynchus darlingi in Brazil
- Systematic notes of Anopheles Konderi and its first record in Paraná state, Brazil
- Redescription of Anopheles (Nyssorhynchus) antunesi Galvão & Amaral and description of a new species of the Myzorhynchella Section (Diptera: Culicidae) from Serra da Mantiqueira, Brazil
- Spatial distribution of arboviral mosquito vectors (Diptera, Culicidae) in Vale do Ribeira in the South-eastern Brazilian Atlantic Forest
- Habitat suitability of Anopheles vector species and association with human malaria in the Atlantic Forest in south-eastern Brazil
- Mitochondrial COI gene as a tool in the taxonomy of mosquitoes Culex subgenus Melanoconion
Informações sobre o DOI: 10.1016/j.dialog.2022.100003 (Fonte: oaDOI API)
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