Evaluating transfer learning for forecasting chikungunya cases


  • Eduardo C. Araujo
  • Flávio C. Coelho


In the last decade, we have seen a spread of chikungunya cases all over the country. Each year, new cities are reporting cases. To minimize the impact of this new disease on public health is necessary to understand how the disease spreads over time. It can be done using mathematical models[1]. Another approach is through statistical models, which can learn from historical data and can help us forecast the number of cases in the short to medium term. When it comes to forecasting, another class of model that is growing more common is deep learning models (DL)[2], which are quite robust at capturing the nonlinear associations between predictor variables and the response variable. [...]


Não há dados estatísticos.

Biografia do Autor

Eduardo C. Araujo

UTFPR, Curitiba, PR

Flávio C. Coelho

FGV-EMAp, Rio de Janeiro, RJ


Nicholas C Grassly and Christophe Fraser. “Mathematical models of infectious disease transmission”. In: Nature Reviews Microbiology 6.6 (2008), pp. 477–487.

Elisa Mussumeci and Flávio Codeço Coelho. “Large-scale multivariate forecasting models for Dengue-LSTM versus random forest regression”. In: Spatial and Spatio-temporal Epidemiology 35 (2020), p. 100372.

Karl Weiss, Taghi M Khoshgoftaar, and DingDing Wang. “A survey of transfer learning”. In: Journal of Big data 3.1 (2016), p. 9.

Rusul L Abduljabbar, Hussein Dia, and Pei-Wei Tsai. “Development and evaluation of bidirectional LSTM freeway traffic forecasting models using simulation data”. In: Scientific reports 11.1 (2021), pp. 1–16.

Sima Siami-Namini, Neda Tavakoli, and Akbar Siami Namin. “The performance of LSTM and BiLSTM in forecasting time series”. In: 2019 IEEE International Conference on Big Data (Big Data). IEEE. 2019, pp. 3285–3292.

Yarin Gal and Zoubin Ghahramani. “Dropout as a bayesian approximation: Representing model uncertainty in deep learning”. In: international conference on machine learning. 2016, pp. 1050–1059.

Aicha Dridi, Hossam Afifi, Hassine Moungla, and Chérifa Boucetta. “Transfer learning for classification and prediction of time series for next generation networks”. In: ICC 2021-IEEE International Conference on Communications. IEEE. 2021, pp. 1–6.