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English Information

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Authors
# Name
1 Ramon Garcia(ramon.garcia@aluno.cefet-rj.br)
2 Eduardo Ogasawara(eogasawara@ieee.org)
3 Jorge Soares(jorge@eic.cefet-rj.br)
4 Amaury de Souza(amaury.souza@ufms.br)
5 Rejane Sobrino(rejane@iesc.ufrj.br)
6 Eduardo Bezerra(ebezerra@cefet-rj.br)

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Reference
# Reference
1 Alves, L., Lana, R., and Coelho, F. (2021). A framework for weather-driven dengue virus transmission dynamics in different brazilian regions. Int. J. Environ. Res. Public Health, 18:9493.
2 Carrington, L. B., Armijos, M. V., Lambrechts, L., Barker, C. M., and Scott, T. W. (2013). Effects of fluctuating daily temperatures at critical thermal extremes on aedes aegypti life-history traits. PLoS ONE, 8(3):e58824.
3 Edillo, F., Ymbong, R. R., Navarro, A. O., Cabahug, M. M., and Saavedra, K. (2024). Detecting the impacts of humidity, rainfall, temperature, and season on chikungunya, dengue and zika viruses in aedes albopictus mosquitoes from selected sites in cebu city, philippines. Virology Journal, 21:42.
4 Hochreiter, S. and Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8):1735–1780.
5 Machado, C. J. S., Miagostovich, M. P., Leite, J. P. G., and Vilani, R. M. (2013). Promoção da relação saúde-saneamento-cidade por meio da virologia ambiental. Revista de Informação Legislativa, 50(199):321–345.
6 Ministério da Saúde (2024a). Indicadores de dengue.
7 Ministério da Saúde (2024b). Sistema Único de saúde (sus).
8 Reinhold, J. M., Lazzari, C. R., and Lahondère, C. (2018). Effects of the environmental temperature on aedes aegypti and aedes albopictus mosquitoes: A review. Insects, 9(4):158.
9 Salim, N. A. M., Samsudin, N. A., Ismail, R., et al. (2021). Prediction of dengue outbreak in selangor malaysia using machine learning techniques. Sci. Rep., 11:79193.
10 Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61:85–117.
11 Zhao, N., Charland, K., Carabali, M., Nsoesie, E. O., Maheu-Giroux, M., Rees, E., et al. (2020). Machine learning and dengue forecasting: Comparing random forests and artificial neural networks for predicting dengue burden at national and sub-national scales in colombia. PLOS Neglected Tropical Diseases, 14(9).