Machine-learning-based forecasting of dengue fever in Brazilian cities using epidemiologic and meteorological variables (2022)
- Authors:
- USP affiliated authors: RODRIGUES, FRANCISCO APARECIDO - ICMC ; ROSTER, KIRSTIN INGRID OLIVEIRA - ICMC
- Unidade: ICMC
- DOI: 10.1093/aje/kwac090
- Subjects: DENGUE; SAÚDE PÚBLICA; APRENDIZADO COMPUTACIONAL; PREDIÇÃO
- Keywords: epidemiologic methods; feature selection
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título do periódico: American Journal of Epidemiology
- ISSN: 0002-9262
- Volume/Número/Paginação/Ano: v. 191, n. 10, p. 1803–1812, 2022
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
ROSTER, Kirstin e CONNAUGHTON, Colm e RODRIGUES, Francisco Aparecido. Machine-learning-based forecasting of dengue fever in Brazilian cities using epidemiologic and meteorological variables. American Journal of Epidemiology, v. 191, n. 10, p. 1803–1812, 2022Tradução . . Disponível em: https://doi.org/10.1093/aje/kwac090. Acesso em: 25 set. 2024. -
APA
Roster, K., Connaughton, C., & Rodrigues, F. A. (2022). Machine-learning-based forecasting of dengue fever in Brazilian cities using epidemiologic and meteorological variables. American Journal of Epidemiology, 191( 10), 1803–1812. doi:10.1093/aje/kwac090 -
NLM
Roster K, Connaughton C, Rodrigues FA. Machine-learning-based forecasting of dengue fever in Brazilian cities using epidemiologic and meteorological variables [Internet]. American Journal of Epidemiology. 2022 ; 191( 10): 1803–1812.[citado 2024 set. 25 ] Available from: https://doi.org/10.1093/aje/kwac090 -
Vancouver
Roster K, Connaughton C, Rodrigues FA. Machine-learning-based forecasting of dengue fever in Brazilian cities using epidemiologic and meteorological variables [Internet]. American Journal of Epidemiology. 2022 ; 191( 10): 1803–1812.[citado 2024 set. 25 ] Available from: https://doi.org/10.1093/aje/kwac090 - Forecasting new diseases in low-data settings using transfer learning
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Informações sobre o DOI: 10.1093/aje/kwac090 (Fonte: oaDOI API)
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