FWin transformer for dengue prediction under climate and ocean influence

TL;DR

This paper introduces a Fourier mixed window attention transformer model that effectively forecasts dengue cases up to 60 weeks ahead by leveraging climate and ocean data, outperforming existing models.

Contribution

The study develops a novel FWin transformer model that improves long-range dengue prediction accuracy using climate and ocean indicators.

Findings

FWin transformer outperforms baseline models in forecasting accuracy.

The model effectively captures complex relationships in climate data.

Long-range predictions up to 60 weeks are feasible with this approach.

Abstract

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.