  Dengue fever presents a substantial challenge in developing countries where
sanitation infrastructure is inadequate. The absence of comprehensive
healthcare systems exacerbates the severity of dengue infections, potentially
leading to life-threatening circumstances. Rapid response to dengue outbreaks
is also challenging due to limited information exchange and integration. While
timely dengue outbreak forecasts have the potential to prevent such outbreaks,
the majority of dengue prediction studies have predominantly relied on data
that impose significant burdens on individual countries for collection. In this
study, our aim is to improve health equity in resource-constrained countries by
exploring the effectiveness of high-resolution satellite imagery as a
nontraditional and readily accessible data source. By leveraging the wealth of
publicly available and easily obtainable satellite imagery, we present a
scalable satellite extraction framework based on Sentinel Hub, a cloud-based
computing platform. Furthermore, we introduce DengueNet, an innovative
architecture that combines Vision Transformer, Radiomics, and Long Short-term
Memory to extract and integrate spatiotemporal features from satellite images.
This enables dengue predictions on an epi-week basis. To evaluate the
effectiveness of our proposed method, we conducted experiments on five
municipalities in Colombia. We utilized a dataset comprising 780
high-resolution Sentinel-2 satellite images for training and evaluation. The
performance of DengueNet was assessed using the mean absolute error (MAE)
metric. Across the five municipalities, DengueNet achieved an average MAE of
43.92. Our findings strongly support the efficacy of satellite imagery as a
valuable resource for dengue prediction, particularly in informing public
health policies within countries where manually collected data is scarce and
dengue virus prevalence is severe.
