FWin transformer for dengue prediction under climate and ocean influence
Nhat Thanh Tran Jack Xin Guofa Zhou
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.