Silva, Cecilia Cordeiro da 
; Moreno, Giselle Machado Magalhães ; Dutra, Livia ; Ambrizzi, Tercio ; Borges, Iuri Valério Graciano Borges
Source: Research, Society and Development. Unidade: IAG
Subjects: CLIMA, DENGUE, VÍRUS CHIKUNGUNYA, ZIKA VÍRUS, ARBOVÍRUS, SAÚDE PÚBLICA
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SILVA, Cecilia Cordeiro da et al. Forecasting Dengue, Chikungunya and Zika cases in Recife, Brazil: a spatio-temporal approach based on climate conditions, health notifications and machine learning. Research, Society and Development, v. 10, n. 12, 2021Tradução . . Disponível em: https://doi.org/10.33448/rsd-v10i12.20804. Acesso em: 18 nov. 2024.APA
Silva, C. C. da, Moreno, G. M. M., Dutra, L., Ambrizzi, T., & Borges, I. V. G. B. (2021). Forecasting Dengue, Chikungunya and Zika cases in Recife, Brazil: a spatio-temporal approach based on climate conditions, health notifications and machine learning. Research, Society and Development, 10( 12). doi:10.33448/rsd-v10i12.20804NLM
Silva CC da, Moreno GMM, Dutra L, Ambrizzi T, Borges IVGB. Forecasting Dengue, Chikungunya and Zika cases in Recife, Brazil: a spatio-temporal approach based on climate conditions, health notifications and machine learning [Internet]. Research, Society and Development. 2021 ; 10( 12):[citado 2024 nov. 18 ] Available from: https://doi.org/10.33448/rsd-v10i12.20804Vancouver
Silva CC da, Moreno GMM, Dutra L, Ambrizzi T, Borges IVGB. Forecasting Dengue, Chikungunya and Zika cases in Recife, Brazil: a spatio-temporal approach based on climate conditions, health notifications and machine learning [Internet]. Research, Society and Development. 2021 ; 10( 12):[citado 2024 nov. 18 ] Available from: https://doi.org/10.33448/rsd-v10i12.20804
