Summary of Fwin Transformer For Dengue Prediction Under Climate and Ocean Influence, by Nhat Thanh Tran et al.
FWin transformer for dengue prediction under climate and ocean influence
by Nhat Thanh Tran, Jack Xin, Guofa Zhou
First submitted to arxiv on: 10 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel approach to forecasting dengue fever cases using deep neural networks. The authors develop a Fourier mixed window attention (FWin) based transformer model that outperforms baseline models in terms of mean square error and maximum absolute error for long-range predictions up to 60 weeks. The study utilizes a dataset consisting of local climate/weather indicators from Singapore, as well as global climate indicators, from 2000 to 2019. By leveraging the relationship between these features, the authors demonstrate the potential of their approach in controlling the spread of dengue fever and informing mitigation efforts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to create a detailed forecast model for dengue fever. The goal is to help prevent the disease by predicting when it will happen and how bad it will be. To do this, scientists studied data from Singapore over 19 years, looking at both local weather and global climate patterns. They used special computer models called transformers to learn what makes the data work together. One of these models, called Fourier mixed window attention (FWin), did a great job predicting dengue fever cases up to six months in advance. |
Keywords
* Artificial intelligence * Attention * Transformer
