FWin transformer for dengue prediction under climate and ocean influence
This work addresses the need for accurate long-range dengue prediction to aid in disease control and mitigation efforts, representing an incremental improvement over existing transformer-based methods.
The study tackled long-range dengue fever forecasting by developing a Fourier mixed window attention (FWin) transformer, which achieved the best performance in terms of mean square error and maximum absolute error for predictions up to 60 weeks.
Dengue fever is one of the most deadly mosquito-born tropical infectious diseases. Detailed long range forecast model is vital in controlling the spread of disease and making mitigation efforts. In this study, we examine methods used to forecast dengue cases for long range predictions. The dataset consists of local climate/weather in addition to global climate indicators of Singapore from 2000 to 2019. We utilize newly developed deep neural networks to learn the intricate relationship between the features. The baseline models in this study are in the class of recent transformers for long sequence forecasting tasks. We found that a Fourier mixed window attention (FWin) based transformer performed the best in terms of both the mean square error and the maximum absolute error on the long range dengue forecast up to 60 weeks.