Context-Aware Recommendations for the Prevention of Urban Arbovirus Outbreaks
Resumo
Urban arboviruses pose significant public health challenges in vulnerable urban areas, where traditional surveillance is often reactive and limited in delivering timely, localized guidance. This paper proposes a context-aware recommender system that integrates climatic data and crowdsourced environmental reports to proactively identify risk areas and deliver personalized preventive recommendations. A spatio-temporal clustering approach (ST-DBSCAN) identifies dynamic risk areas, and a multi-criteria model combines environmental and climatic indicators into a normalized risk score classified into operational levels. Based on these levels, the system generates context-sensitive recommendations, enabling proactive and localized interventions.
Palavras-chave:
Recommender System, Urban Arboviruses, Clustering
Referências
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Adomavicius, G., Mobasher, B., Ricci, F., and Tuzhilin, A. (2011). Context-aware recommender systems. AI Magazine, 32(3):67–80.
Adomavicius, G. and Tuzhilin, A. (2011). Context-Aware Recommender Systems, pages 217–253. Springer US, Boston, MA.
Afzal, M., Ali, S. I., Ali, R., Hussain, M., Ali, T., Khan, W. A., Amin, M. B., Kang, B. H., and Lee, S. (2018). Personalization of wellness recommendations using contextual interpretation. Expert Systems with Applications, 96:506–521.
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Alves, A. C., Fabbro, A. L. d., Passos, A. D. C., Carneiro, A. F. T. M., Jorge, T. M., and Martinez, E. Z. (2016). Knowledge and practices related to dengue and its vector: a community-based study from southeast brazil. Revista da Sociedade Brasileira de Medicina Tropical, 49:222–226.
Andrighetti, M. T. M., Galvani, K. C., and da Graça Macoris, M. L. (2009). Evaluation of premise condition index in the context of aedes aegypti control in marília, são paulo, brazil. pages Dengue Bulletin. 2009 Dec; 33: 167–175.
Arouca, M. G., Ribeiro, A., Amorim, A. M., Neves, I. B. d. C., Vieira, V., Barreto, M. E., Costa, F., and Brito, R. L. (2024). Gamification to support crowdsourcing and participatory mapping for signaling and spatialization of covid-19 transmission predictors. In Anais do XIX Simpósio Brasileiro de Sistemas Colaborativos. SBC.
Asad, H. and Carpenter, D. O. (2018). Effects of climate change on the spread of zika virus: a public health threat. Reviews on Environmental Health, 33(1):31–42.
Braack, L., Gouveia de Almeida, A. P., Cornel, A. J., Swanepoel, R., and De Jager, C. (2018). Mosquito-borne arboviruses of african origin: review of key viruses and vectors. Parasites & vectors, 11:1–26.
da Saúde do Brasil, M. (2025a). Educa dtn-ve: Situação epidemiológica das arboviroses no brasil.
da Saúde do Brasil, M. (2025b). Plano de contingência nacional para dengue, chikungunya e zika. Technical report, Ministério da Saúde, Brasil. Acesso em: 29 maio 2025.
de Souza, W. M., Fumagalli, M. J., de Lima, S. T., Parise, P. L., Carvalho, D. C., Hernandez, C., de Jesus, R., Delafiori, J., Candido, D. S., Carregari, V. C., et al. (2024). Pathophysiology of chikungunya virus infection associated with fatal outcomes. Cell host & microbe, 32(4):606–622.
de Tarso R Vilarinhos, P. (2005). Challenges for dengue control in brazil: overview of socioeconomic and environmental factors associated with virus circulation. Environmental Change and Malaria Risk, pages 107–111.
Dean, S. and Morgenstern, J. (2022). Preference dynamics under personalized recommendations. In Proceedings of the 23rd ACM Conference on Economics and Computation, EC ’22, page 795–816, New York, NY, USA. Association for Computing Machinery.
Figueiredo, L. T. M. (2019). Human urban arboviruses can infect wild animals and jump to sylvatic maintenance cycles in south america. Frontiers in cellular and infection microbiology, 9:259.
Fontenille, D. and Powell, J. R. (2020). From anonymous to public enemy: How does a mosquito become a feared arbovirus vector? Pathogens, 9(4).
Girard, M., Nelson, C. B., Picot, V., and Gubler, D. J. (2020). Arboviruses: A global public health threat. Vaccine, 38(24):3989–3994.
Gurgel-Gonçalves, R., Oliveira, W. K. d., and Croda, J. (2024). The greatest dengue epidemic in brazil: surveillance, prevention, and control. Revista da Sociedade Brasileira de Medicina Tropical, 57:e00203–2024.
Huang, Y.-J. S., Higgs, S., and Vanlandingham, D. L. (2019). Arbovirus-mosquito vector-host interactions and the impact on transmission and disease pathogenesis of arboviruses. Frontiers in microbiology, 10:22.
Kajornkasirat, S., Hnusuwan, B., Puttinaovarat, S., Puangsuwan, K., and Kaewsuwan, N. (2025). Recommender system for dengue prevention using machine learning. AES International Journal of Artificial Intelligence (IJ-AI), 14(2):1106–1115.
Knijnenburg, B. P., Reijmer, N. J., and Willemsen, M. C. (2011). Each to his own: how different users call for different interaction methods in recommender systems. In Proceedings of the Fifth ACM Conference on Recommender Systems, RecSys ’11, page 141–148, New York, NY, USA. Association for Computing Machinery.
Ligsay, A., Telle, O., and Paul, R. (2021). Challenges to mitigating the urban health burden of mosquito-borne diseases in the face of climate change. International Journal of Environmental Research and Public Health, 18(9).
Lima-Camara, T. N. (2024). Dengue is a product of the environment: an approach to the impacts of the environment on the aedes aegypti mosquito and disease cases. Revista Brasileira de Epidemiologia, 27:e240048.
Madewell, Z. J. (2020). Arboviruses and their vectors. Southern Medical Journal, 113(10):520.
Martinet, J.-P., Ferté, H., Failloux, A.-B., Schaffner, F., and Depaquit, J. (2019). Mosquitoes of north-western europe as potential vectors of arboviruses: A review. Viruses, 11(11).
Mateos, P. and Bellogín, A. (2025). A systematic literature review on recent advances in context-aware recommender systems. Artificial Intelligence Review, 58:20.
Medeiros, E. A. (2024). Desafios no controle da epidemia da dengue no brasil.
Paixão, E. S., Teixeira, M. G., and Rodrigues, L. C. (2018). Zika, chikungunya and dengue: the causes and threats of new and re-emerging arboviral diseases. BMJ global health, 3(Suppl 1):e000530.
Prasad, A., Sreedharan, S., Bakthavachalu, B., and Laxman, S. (2023). Eggs of the mosquito aedes aegypti survive desiccation by rewiring their polyamine and lipid metabolism. PLOS Biology, 21(10):1–24.
Rahman, M. M. (2013). Contextual recommender systems using a multidimensional approach. International Journal of Intelligent Information Systems, 2(4):55–63.
Raza, S. and Ding, C. (2019). Progress in context-aware recommender systems — an overview. Computer Science Review, 31:84–97.
Santos, S. L. d., Parra-Henao, G., Silva, M. B. C. e., and Augusto, L. G. d. S. (2014). Dengue in brazil and colombia: a study of knowledge, attitudes, and practices. Revista da Sociedade Brasileira de Medicina Tropical, 47(6):783–787.
Scarpino, S. V., Meyers, L. A., and Johansson, M. A. (2017). Design strategies for efficient arbovirus surveillance. Emerging infectious diseases, 23(4):642.
Shaikh, S. G., Suresh Kumar, B., and Narang, G. (2023). Development of optimized ensemble classifier for dengue fever prediction and recommendation system. Biomedical Signal Processing and Control, 85:104809.
Singh, S., Singh, A., Samson, and Singh, M. (2016). Recommender system for detection of dengue using fuzzy logic. International Journal of Computer Engineering and Technology (IJCET), 7(2):44–52. Article ID: IJCET 07 02 006. Journal Impact Factor (2016): 9.3590 (Calculated by GISI).
Verbert, K., Duval, E., Lindstaedt, S. N., and Gillet, D. (2010). Context-aware recommender systems. Journal of Universal Computer Science, 16(16):2175–2178.
Weetman, D., Kamgang, B., Badolo, A., Moyes, C. L., Shearer, F. M., Coulibaly, M., Pinto, J., Lambrechts, L., and McCall, P. J. (2018). Aedes mosquitoes and aedes-borne arboviruses in africa: current and future threats. International journal of environmental research and public health, 15(2):220.
Wohlin, C. (2014). Guidelines for snowballing in systematic literature studies and a replication in software engineering. In Proceedings of the 18th International Conference on Evaluation and Assessment in Software Engineering, EASE ’14, New York, NY, USA. Association for Computing Machinery.
Young, P. R. (2018). Arboviruses: A Family on the Move, pages 1–10. Springer Singapore, Singapore.
Zhang, W. et al. (2024). Role of climate and environmental changes in mosquito population dynamics. Journal of Mosquito Research, 14.
Adomavicius, G., Mobasher, B., Ricci, F., and Tuzhilin, A. (2011). Context-aware recommender systems. AI Magazine, 32(3):67–80.
Adomavicius, G. and Tuzhilin, A. (2011). Context-Aware Recommender Systems, pages 217–253. Springer US, Boston, MA.
Afzal, M., Ali, S. I., Ali, R., Hussain, M., Ali, T., Khan, W. A., Amin, M. B., Kang, B. H., and Lee, S. (2018). Personalization of wellness recommendations using contextual interpretation. Expert Systems with Applications, 96:506–521.
Almeida, L. S., Cota, A. L. S., and Rodrigues, D. F. (2020). Saneamento, arboviroses e determinantes ambientais: impactos na saúde urbana. Ciência & Saúde Coletiva, 25:3857–3868.
Alves, A. C., Fabbro, A. L. d., Passos, A. D. C., Carneiro, A. F. T. M., Jorge, T. M., and Martinez, E. Z. (2016). Knowledge and practices related to dengue and its vector: a community-based study from southeast brazil. Revista da Sociedade Brasileira de Medicina Tropical, 49:222–226.
Andrighetti, M. T. M., Galvani, K. C., and da Graça Macoris, M. L. (2009). Evaluation of premise condition index in the context of aedes aegypti control in marília, são paulo, brazil. pages Dengue Bulletin. 2009 Dec; 33: 167–175.
Arouca, M. G., Ribeiro, A., Amorim, A. M., Neves, I. B. d. C., Vieira, V., Barreto, M. E., Costa, F., and Brito, R. L. (2024). Gamification to support crowdsourcing and participatory mapping for signaling and spatialization of covid-19 transmission predictors. In Anais do XIX Simpósio Brasileiro de Sistemas Colaborativos. SBC.
Asad, H. and Carpenter, D. O. (2018). Effects of climate change on the spread of zika virus: a public health threat. Reviews on Environmental Health, 33(1):31–42.
Braack, L., Gouveia de Almeida, A. P., Cornel, A. J., Swanepoel, R., and De Jager, C. (2018). Mosquito-borne arboviruses of african origin: review of key viruses and vectors. Parasites & vectors, 11:1–26.
da Saúde do Brasil, M. (2025a). Educa dtn-ve: Situação epidemiológica das arboviroses no brasil.
da Saúde do Brasil, M. (2025b). Plano de contingência nacional para dengue, chikungunya e zika. Technical report, Ministério da Saúde, Brasil. Acesso em: 29 maio 2025.
de Souza, W. M., Fumagalli, M. J., de Lima, S. T., Parise, P. L., Carvalho, D. C., Hernandez, C., de Jesus, R., Delafiori, J., Candido, D. S., Carregari, V. C., et al. (2024). Pathophysiology of chikungunya virus infection associated with fatal outcomes. Cell host & microbe, 32(4):606–622.
de Tarso R Vilarinhos, P. (2005). Challenges for dengue control in brazil: overview of socioeconomic and environmental factors associated with virus circulation. Environmental Change and Malaria Risk, pages 107–111.
Dean, S. and Morgenstern, J. (2022). Preference dynamics under personalized recommendations. In Proceedings of the 23rd ACM Conference on Economics and Computation, EC ’22, page 795–816, New York, NY, USA. Association for Computing Machinery.
Figueiredo, L. T. M. (2019). Human urban arboviruses can infect wild animals and jump to sylvatic maintenance cycles in south america. Frontiers in cellular and infection microbiology, 9:259.
Fontenille, D. and Powell, J. R. (2020). From anonymous to public enemy: How does a mosquito become a feared arbovirus vector? Pathogens, 9(4).
Girard, M., Nelson, C. B., Picot, V., and Gubler, D. J. (2020). Arboviruses: A global public health threat. Vaccine, 38(24):3989–3994.
Gurgel-Gonçalves, R., Oliveira, W. K. d., and Croda, J. (2024). The greatest dengue epidemic in brazil: surveillance, prevention, and control. Revista da Sociedade Brasileira de Medicina Tropical, 57:e00203–2024.
Huang, Y.-J. S., Higgs, S., and Vanlandingham, D. L. (2019). Arbovirus-mosquito vector-host interactions and the impact on transmission and disease pathogenesis of arboviruses. Frontiers in microbiology, 10:22.
Kajornkasirat, S., Hnusuwan, B., Puttinaovarat, S., Puangsuwan, K., and Kaewsuwan, N. (2025). Recommender system for dengue prevention using machine learning. AES International Journal of Artificial Intelligence (IJ-AI), 14(2):1106–1115.
Knijnenburg, B. P., Reijmer, N. J., and Willemsen, M. C. (2011). Each to his own: how different users call for different interaction methods in recommender systems. In Proceedings of the Fifth ACM Conference on Recommender Systems, RecSys ’11, page 141–148, New York, NY, USA. Association for Computing Machinery.
Ligsay, A., Telle, O., and Paul, R. (2021). Challenges to mitigating the urban health burden of mosquito-borne diseases in the face of climate change. International Journal of Environmental Research and Public Health, 18(9).
Lima-Camara, T. N. (2024). Dengue is a product of the environment: an approach to the impacts of the environment on the aedes aegypti mosquito and disease cases. Revista Brasileira de Epidemiologia, 27:e240048.
Madewell, Z. J. (2020). Arboviruses and their vectors. Southern Medical Journal, 113(10):520.
Martinet, J.-P., Ferté, H., Failloux, A.-B., Schaffner, F., and Depaquit, J. (2019). Mosquitoes of north-western europe as potential vectors of arboviruses: A review. Viruses, 11(11).
Mateos, P. and Bellogín, A. (2025). A systematic literature review on recent advances in context-aware recommender systems. Artificial Intelligence Review, 58:20.
Medeiros, E. A. (2024). Desafios no controle da epidemia da dengue no brasil.
Paixão, E. S., Teixeira, M. G., and Rodrigues, L. C. (2018). Zika, chikungunya and dengue: the causes and threats of new and re-emerging arboviral diseases. BMJ global health, 3(Suppl 1):e000530.
Prasad, A., Sreedharan, S., Bakthavachalu, B., and Laxman, S. (2023). Eggs of the mosquito aedes aegypti survive desiccation by rewiring their polyamine and lipid metabolism. PLOS Biology, 21(10):1–24.
Rahman, M. M. (2013). Contextual recommender systems using a multidimensional approach. International Journal of Intelligent Information Systems, 2(4):55–63.
Raza, S. and Ding, C. (2019). Progress in context-aware recommender systems — an overview. Computer Science Review, 31:84–97.
Santos, S. L. d., Parra-Henao, G., Silva, M. B. C. e., and Augusto, L. G. d. S. (2014). Dengue in brazil and colombia: a study of knowledge, attitudes, and practices. Revista da Sociedade Brasileira de Medicina Tropical, 47(6):783–787.
Scarpino, S. V., Meyers, L. A., and Johansson, M. A. (2017). Design strategies for efficient arbovirus surveillance. Emerging infectious diseases, 23(4):642.
Shaikh, S. G., Suresh Kumar, B., and Narang, G. (2023). Development of optimized ensemble classifier for dengue fever prediction and recommendation system. Biomedical Signal Processing and Control, 85:104809.
Singh, S., Singh, A., Samson, and Singh, M. (2016). Recommender system for detection of dengue using fuzzy logic. International Journal of Computer Engineering and Technology (IJCET), 7(2):44–52. Article ID: IJCET 07 02 006. Journal Impact Factor (2016): 9.3590 (Calculated by GISI).
Verbert, K., Duval, E., Lindstaedt, S. N., and Gillet, D. (2010). Context-aware recommender systems. Journal of Universal Computer Science, 16(16):2175–2178.
Weetman, D., Kamgang, B., Badolo, A., Moyes, C. L., Shearer, F. M., Coulibaly, M., Pinto, J., Lambrechts, L., and McCall, P. J. (2018). Aedes mosquitoes and aedes-borne arboviruses in africa: current and future threats. International journal of environmental research and public health, 15(2):220.
Wohlin, C. (2014). Guidelines for snowballing in systematic literature studies and a replication in software engineering. In Proceedings of the 18th International Conference on Evaluation and Assessment in Software Engineering, EASE ’14, New York, NY, USA. Association for Computing Machinery.
Young, P. R. (2018). Arboviruses: A Family on the Move, pages 1–10. Springer Singapore, Singapore.
Zhang, W. et al. (2024). Role of climate and environmental changes in mosquito population dynamics. Journal of Mosquito Research, 14.
Publicado
08/06/2026
Como Citar
AROUCA, Murilo Guerreiro; DURÃO, Frederico Aráujo.
Context-Aware Recommendations for the Prevention of Urban Arbovirus Outbreaks. In: SIMPÓSIO BRASILEIRO DE SISTEMAS COLABORATIVOS (SBSC), 21. , 2026, Porto Alegre/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 444-454.
ISSN 2326-2842.
DOI: https://doi.org/10.5753/sbsc.2026.20270.
