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Dengue forecasting in São Paulo city with generalized additive models, artificial neural networks and seasonal autoregressive integrated moving average models

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  • Oswaldo Santos Baquero
  • Lidia Maria Reis Santana
  • Francisco Chiaravalloti-Neto

Abstract

Globally, the number of dengue cases has been on the increase since 1990 and this trend has also been found in Brazil and its most populated city—São Paulo. Surveillance systems based on predictions allow for timely decision making processes, and in turn, timely and efficient interventions to reduce the burden of the disease. We conducted a comparative study of dengue predictions in São Paulo city to test the performance of trained seasonal autoregressive integrated moving average models, generalized additive models and artificial neural networks. We also used a naïve model as a benchmark. A generalized additive model with lags of the number of cases and meteorological variables had the best performance, predicted epidemics of unprecedented magnitude and its performance was 3.16 times higher than the benchmark and 1.47 higher that the next best performing model. The predictive models captured the seasonal patterns but differed in their capacity to anticipate large epidemics and all outperformed the benchmark. In addition to be able to predict epidemics of unprecedented magnitude, the best model had computational advantages, since its training and tuning was straightforward and required seconds or at most few minutes. These are desired characteristics to provide timely results for decision makers. However, it should be noted that predictions are made just one month ahead and this is a limitation that future studies could try to reduce.

Suggested Citation

  • Oswaldo Santos Baquero & Lidia Maria Reis Santana & Francisco Chiaravalloti-Neto, 2018. "Dengue forecasting in São Paulo city with generalized additive models, artificial neural networks and seasonal autoregressive integrated moving average models," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-12, April.
  • Handle: RePEc:plo:pone00:0195065
    DOI: 10.1371/journal.pone.0195065
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    References listed on IDEAS

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    1. Samir Bhatt & Peter W. Gething & Oliver J. Brady & Jane P. Messina & Andrew W. Farlow & Catherine L. Moyes & John M. Drake & John S. Brownstein & Anne G. Hoen & Osman Sankoh & Monica F. Myers & Dylan , 2013. "The global distribution and burden of dengue," Nature, Nature, vol. 496(7446), pages 504-507, April.
    2. Aditya Lia Ramadona & Lutfan Lazuardi & Yien Ling Hii & Åsa Holmner & Hari Kusnanto & Joacim Rocklöv, 2016. "Prediction of Dengue Outbreaks Based on Disease Surveillance and Meteorological Data," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-18, March.
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    Cited by:

    1. Villi Dane M. Go, 2023. "Communicable disease surveillance through predictive analysis: A comparative analysis of prediction models," HO CHI MINH CITY OPEN UNIVERSITY JOURNAL OF SCIENCE - ENGINEERING AND TECHNOLOGY, HO CHI MINH CITY OPEN UNIVERSITY JOURNAL OF SCIENCE, HO CHI MINH CITY OPEN UNIVERSITY, vol. 13(2), pages 45-54.
    2. Zhichao Li, 2022. "Forecasting Weekly Dengue Cases by Integrating Google Earth Engine-Based Risk Predictor Generation and Google Colab-Based Deep Learning Modeling in Fortaleza and the Federal District, Brazil," IJERPH, MDPI, vol. 19(20), pages 1-16, October.
    3. Panja, Madhurima & Chakraborty, Tanujit & Nadim, Sk Shahid & Ghosh, Indrajit & Kumar, Uttam & Liu, Nan, 2023. "An ensemble neural network approach to forecast Dengue outbreak based on climatic condition," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).

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