Transfer learning applied to the forecast of mosquito-borne diseases
Coelho, F. C.; de Holanda, N. L.; Coimbra, B. M.
Show abstract
Here we apply the concept of transfer learning to time series forecasting models for mosquito-borne diseases. Transfer learning, in this application, allows us to use knowledge obtained from modeling one disease to predict an emerging one for which extensive data is still not available. Here we discuss the performances of two families of models for predicting Chikungunya and Zika using models trained with dengue time series, in two Brazilian cities: Rio de Janeiro and Fortaleza.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- On the use of growth models for forecasting epidemic outbreaks with application to COVID-19 data 95%
- Identification of high-risk COVID-19 patients using machine learning 94%
- Interdependence between confirmed and discarded cases of dengue, chikungunya and Zika viruses in Brazil: A multivariate time-series analysis 94%
Similar papers in this journal
- Dengue forecasting and outbreak detection in Brazil using LSTM: integrating human mobility and climate factors 94%
- Estimate of the rate of unreported COVID-19 cases during the first outbreak in Rio de Janeiro 94%
- Switched forced SEIRDV compartmental models to monitor COVID-19 spread and immunization in Italy 94%
Similar papers in this journal
- A new, simple method of describing COVID-19 trajectory and dynamics in any country based on Johnson Cumulative Distribution Function fitting 94%
- Predicting dengue importation into Europe, using machine learning and model-agnostic methods. 94%
- Extended compartmental model for modeling COVID-19 epidemic in Slovenia 93%
Similar papers in this journal
- Deep Neural Networks for Human’s Fall-risk Prediction using Force-Plate Time Series Signal 92%
- Beyond Benchmarks: Towards Robust Artificial Intelligence Bone Segmentation in Socio-Technical Systems 91%
- A new local covariance matrix estimation for the classification of gene expression profiles in RNA-Seq data 90%
Similar papers in this journal
- Data-driven Discovery of Mathematical and Physical Relations in Oncology Data using Human-understandable Machine Learning 92%
- General SIR model for visible and hidden epidemic dynamics 91%
- Predicting the disease outcome in COVID-19 positive patients through Machine Learning: a retrospective cohort study with Brazilian data 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.