Forecasting new diseases in low-data settings using transfer learning (2022)
- Authors:
- USP affiliated authors: RODRIGUES, FRANCISCO APARECIDO - ICMC ; ROSTER, KIRSTIN INGRID OLIVEIRA - ICMC
- Unidade: ICMC
- DOI: 10.1016/j.chaos.2022.112306
- Subjects: APRENDIZADO COMPUTACIONAL; COVID-19; ZIKA VÍRUS; TOMADA DE DECISÃO; SURTOS DE DOENÇAS
- Keywords: Transfer learning; Epidemic forecasting
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título do periódico: Chaos, Solitons and Fractals
- ISSN: 0960-0779
- Volume/Número/Paginação/Ano: v. 161, p. 1-8, 2022
- Este periódico é de assinatura
- Este artigo é de acesso aberto
- URL de acesso aberto
- Cor do Acesso Aberto: green
-
ABNT
ROSTER, Kirstin e CONNAUGHTON, Colm e RODRIGUES, Francisco Aparecido. Forecasting new diseases in low-data settings using transfer learning. Chaos, Solitons and Fractals, v. 161, p. 1-8, 2022Tradução . . Disponível em: https://doi.org/10.1016/j.chaos.2022.112306. Acesso em: 16 maio 2024. -
APA
Roster, K., Connaughton, C., & Rodrigues, F. A. (2022). Forecasting new diseases in low-data settings using transfer learning. Chaos, Solitons and Fractals, 161, 1-8. doi:10.1016/j.chaos.2022.112306 -
NLM
Roster K, Connaughton C, Rodrigues FA. Forecasting new diseases in low-data settings using transfer learning [Internet]. Chaos, Solitons and Fractals. 2022 ; 161 1-8.[citado 2024 maio 16 ] Available from: https://doi.org/10.1016/j.chaos.2022.112306 -
Vancouver
Roster K, Connaughton C, Rodrigues FA. Forecasting new diseases in low-data settings using transfer learning [Internet]. Chaos, Solitons and Fractals. 2022 ; 161 1-8.[citado 2024 maio 16 ] Available from: https://doi.org/10.1016/j.chaos.2022.112306 - Machine-learning-based forecasting of dengue fever in Brazilian cities using epidemiologic and meteorological variables
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Informações sobre o DOI: 10.1016/j.chaos.2022.112306 (Fonte: oaDOI API)
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