Forecasting Dengue, Chikungunya and Zika cases in Recife, Brazil: a spatio-temporal approach based on climate conditions, health notifications and machine learning

Authors

DOI:

https://doi.org/10.33448/rsd-v10i12.20804

Keywords:

Dengue forecasting; Chikungunya forecasting; Zika forecasting; Arboviruses forecasting; Machine learning; Arboviruses prediction.

Abstract

Dengue has become a challenge for many countries. Arboviruses transmitted by Aedes aegypti spread rapidly over the last decades. The emergence chikungunya fever and zika in South America poses new challenges to vector monitoring and control. This situation got worse from 2015 and 2016, with the rapid spread of chikungunya, causing fever and muscle weakness, and Zika virus, related to cases of microcephaly in newborns and the occurrence of Guillain-Barret syndrome, an autoimmune disease that affects the nervous system. The objective of this work was to construct a tool to forecast the distribution of arboviruses transmitted by the mosquito Aedes aegypti by implementing dengue, zika and chikungunya transmission predictors based on machine learning, focused on multilayer perceptrons neural networks, support vector machines and linear regression models. As a case study, we investigated forecasting models to predict the spatio-temporal distribution of cases from primary health notification data and climate variables (wind velocity, temperature and pluviometry) from Recife, Brazil, from 2013 to 2016, including 2015’s outbreak. The use of spatio-temporal analysis over multilayer perceptrons and support vector machines results proved to be very effective in predicting the distribution of arbovirus cases. The models indicate that the southern and western regions of Recife were very susceptible to outbreaks in the period under investigation. The proposed approach could be useful to support health managers and epidemiologists to prevent outbreaks of arboviruses transmitted by Aedes aegypti and promote public policies for health promotion and sanitation.

References

Akil, L., & Ahmad, H. A. (2016). Salmonella infections modelling in Mississippi using neural network and geographical information system (GIS). BMJ Open, 6(3). Retrieved from https://bmjopen.bmj.com/content/6/3/e009255 doi: 10.1136/bmjopen­2015­009255

Baquero, O. S., Santana, L. M. R., & Chiaravalloti­Neto, F. (2018, 04). Dengue forecasting in São Paulo city with generalized additive models, artificial neural networks and seasonal autoregressive integrated moving average models. PLOS ONE, 13(4), 1­12. Retrieved from https://doi.org/10.1371/journal.pone.0195065 doi: 10.1371/journal.pone.0195065

Barbosa, V. A. d. F., Gomes, J. C., de Santana, M. A., de Lima, C. L., Calado, R. B., Bertoldo Júnior, C. R., … others (2021). Covid­19 rapid test by combining a random forest­based web system and blood tests. Journal of Biomolecular Structure and Dynamics, 65, 1–20.

Beltrán, J. D., Boscor, A., dos Santos, W. P., Massoni, T., & Kostkova, P. (2018). ZIKA: A New System to Empower Health Workers and Local Communities to Improve Surveillance Protocols by E­learning and to Forecast Zika Virus in Real Time in Brazil. In Proceedings of the 2018 international conference on digital health (pp. 90–94).

Bhatt, S., Gething, P. W., Brady, O. J., Messina, J. P., Farlow, A. W., Moyes, C. L., … others (2013). The global distribution and burden of dengue. Nature, 496(7446), 504–507.

Cao­Lormeau, V.­M., Blake, A., Mons, S., Lastère, S., Roche, C., Vanhomwegen, J., … others (2016). Guillain­Barré Syndrome outbreak associated with Zika virus infection in French Polynesia: a case­control study. The Lancet, 387(10027), 1531–1539.

Commowick, O., Istace, A., Kain, M., Laurent, B., Leray, F., Simon, M., … others (2018). Objective evaluation of multiple sclerosis lesion segmentation using a data management and processing infrastructure. Scientific Reports, 8(1), 1–17.

Cordeiro, F. R., Santos, W. P., & Silva­Filho, A. G. (2016). A semi­supervised fuzzy growcut algorithm to segment and classify regions of interest of mammographic images. Expert Systems with Applications, 65, 116–126.

da Silva, C. C., de Lima, C. L., da Silva, A. C. G., Silva, E. L., Marques, G. S., de Araújo, L. J. B., … others (2021). Covid­19 dynamic monitoring and real­time spatio­temporal forecasting. Frontiers in Public Health, 9.

de Lima, C. L., da Silva, C. C., da Silva, A. C. G., Luiz Silva, E., Marques, G. S., de Araújo, L. J. B., … others (2020). COVID­SGIS: a smart tool for dynamic monitoring and temporal forecasting of Covid­19. Frontiers in Public Health, 8, 761.

de Lima, S. M., da Silva­Filho, A. G., & dos Santos, W. P. (2016). Detection and classification of masses in mammographic images in a multi­kernel approach. Computer methods and programs in biomedicine, 134, 11–29.

de Lima, T. F. M., Lana, R. M., de Senna Carneiro, T. G., Codeço, C. T., Machado, G. S., Ferreira, L. S., … Davis Junior, C. A. (2016). DengueME: A Tool for the Modeling and Simulation of Dengue Spatiotemporal Dynamics. International Journal of Environmental Research and Public Health, 13(9), 920.

de Souza, R. G., dos Santos Lucas e Silva, G., dos Santos, W. P., & de Lima, M. E. (2021). Computer­aided diagnosis of Alzheimer’s disease by MRI analysis and evolutionary computing. Research on Biomedical Engineering, 37, 455–­483.

Drucker, H., Burges, C. J., Kaufman, L., Smola, A., Vapnik, V., et al. (1997). Support vector regression machines. Advances in Neural Information Processing Systems, 9, 155–161.

Frank, E., Hall, M., Trigg, L., Holmes, G., & Witten, I. H. (2004). Data mining in bioinformatics using Weka. Bioinformatics, 20(15), 2479–2481.

Gubler, D. J. (2011). Dengue, urbanization and globalization: the unholy trinity of the 21st century. Tropical Medicine and Health, 39(4SUPPLEMENT), S3–S11.

Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009). The WEKA data mining software: an update. ACM SIGKDD Explorations Newsletter, 11(1), 10–18.

Haykin, S. (2001). Redes Neurais: Princípios e Prática. Porto Alegre: Bookman.

Jindal, A., & Rao, S. (2017). Agent­based modeling and simulation of mosquito­borne disease transmission. In Proceedings of the 16th conference on autonomous agents and multiagent systems (pp. 426–435).

Koche, J. C. (2011). Fundamentos de metodologia científica: teoria da ciência e iniciação à pesquisa. Petrópolis, Rio de Janeiro, Brasil: Vozes.

Kostkova, P., dos Santos, W. P., & Massoni, T. L. (2019). ZIKA: improved surveillance and forecast of Zika virus in Brazil. European Journal of Public Health, 29(Supplement 4), 414­415, ckz186.085.

LaDeau, S. L., Allan, B. F., Leisnham, P. T., & Levy, M. Z. (2015). The ecological foundations of transmission potential and vector­borne disease in urban landscapes. Functional Ecology, 29(7), 889–901.

Laureano­Rosario, A. E., Duncan, A. P., Mendez­Lazaro, P. A., Garcia­Rejon, J. E., Gomez­Carro, S., Farfan­Ale, J., … MullerKarger, F. E. (2018). Application of Artificial Neural Networks for Dengue Fever Outbreak Predictions in the Northwest Coast of Yucatan, Mexico and San Juan, Puerto Rico. Tropical Medicine and Infectious Disease, 3(1), 5.

Ludke, M., & André, M. E. D. A. (2013). Pesquisas em educação: uma abordagem qualitativa. São Paulo, Brasil: EPU Editora Pedagógica e Universitária.

Mohammed, A., & Chadee, D. D. (2011). Effects of different temperature regimens on the development of aedes aegypti (l.)(diptera: Culicidae) mosquitoes. Acta Tropica, 119(1), 38–43.

Morin, C. W., Comrie, A. C., & Ernst, K. (2013). Climate and dengue transmission: evidence and implications. Environmental health perspectives, 121(11­12), 1264–1272.

Musah, A., Rubio­Solis, A., Birjovanu, G., dos Santos, W. P., Massoni, T., & Kostkova, P. (2019). Assessing the Relationship between various Climatic Risk Factors & Mosquito Abundance in Recife, Brazil. In Proceedings of the 9th international conference on digital public health (pp. 97–100).

Padmanabhan, P., Seshaiyer, P., & Castillo­Chavez, C. (2017). Mathematical modeling, analysis and simulation of the spread of zika with influence of sexual transmission and preventive measures. Letters in Biomathematics, 4(1), 148–166.

Pereira, A. S., Shitsuka, D. M., Parreira, F. J., & Shitsuka, R. (2018). Metodologia da pesquisa científica. Santa Maria, Rio Grande do Sul, Brasil: Universidade Federal de Santa Maria.

Pereira, J., Santana, M. A., Gomes, J. C., de Freitas Barbosa, V. A., Valença, M. J. S., de Lima, S. M. L., & dos Santos, W. P. (2021). Feature selection based on dialectics to support breast cancer diagnosis using thermographic images. Research on Biomedical Engineering, 37, 485–­506.

Pessanha, J. E. M., Caiaffa, W. T., César, C. C., & Proietti, F. A. (2009). Avaliação do plano nacional de controle da dengue. Cad. Saúde Pública, 25(7), 1637–1641.

Rubio­Solis, A., Musah, A., dos Santos, W. P., Massoni, T., Birjovanu, G., & Kostkova, P. (2019). ZIKA Virus: Prediction of Aedes Mosquito Larvae Occurrence in Recife (Brazil) using Online Extreme Learning Machine and Neural Networks. In Proceedings of the 9th international conference on digital public health (pp. 101–110).

Salathe, M., Bengtsson, L., Bodnar, T. J., Brewer, D. D., Brownstein, J. S., Buckee, C., … others (2012). Digital epidemiology. PLoS Computational Biology, 8(7), e1002616.

Santana, M. A. d., Pereira, J. M. S., Silva, F. L. d., Lima, N. M. d., Sousa, F. N. d., Arruda, G. M. S. d., … Santos, W. P. d. (2018). Breast cancer diagnosis based on mammary thermography and extreme learning machines. Research on Biomedical Engineering, 34, 45–53.

Siriyasatien, P., Chadsuthi, S., Jampachaisri, K., & Kesorn, K. (2018). Dengue epidemics prediction: A survey of the state­ofthe­art based on data science processes. IEEE Access, 6, 53757­53795.

Smola, A. J., & Schölkopf, B. (2004). A tutorial on support vector regression. Statistics and Computing, 14(3), 199–222.

Witten, I. H., & Frank, E. (2005). Data mining: Pratical machine learning tools and technique. San Francisco, CA, USA: Morgan Kaufmann Publishers.

Yin, R. K. (2015). Estudo de caso: Planejamento e métodos. Porto Alegre, Brasil: Bookman.

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Published

26/09/2021

How to Cite

SILVA, C. C. da; LIMA, C. L. de; SILVA, A. C. G. da; MORENO, G. M. M.; MUSAH, A.; ALDOSERY, A.; DUTRA, L.; AMBRIZZI, T.; BORGES, I. V. G.; TUNALI, M.; BASIBUYUK, S.; YENIGÜN, O.; JONES, K.; CAMPOS, L.; MASSONI, T. L.; SILVA FILHO, A. G. da; KOSTKOVA, P.; SANTOS, W. P. dos. 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, [S. l.], v. 10, n. 12, p. e452101220804, 2021. DOI: 10.33448/rsd-v10i12.20804. Disponível em: https://rsdjournal.org/index.php/rsd/article/view/20804. Acesso em: 2 mar. 2024.

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Section

Health Sciences