DengueNet: Dengue Prediction using Spatiotemporal Satellite Imagery for Resource-Limited Countries

Anonymous

Keywords: Dengue, satellite imagery, health equity

TL;DR: The study aims to advance health equity in low-resource nations by developing a predictive model for dengue outbreaks that effectively utilizes publicly accessible high-resolution satellite imagery.

Abstract: 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 epidemiological-week basis. To evaluate the effectiveness of our proposed method, we conducted experiments in five municipalities in Colombia. We utilized a dataset comprising 780 high-resolution Sentinel-2 satellite images for training and evaluation purposes. 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±42.19. Notably, the highest MAE was recorded in Cali at 113.65±0.08, whereas the lowest MAE was observed in Ibagué, amounting to 5.67±0.18. Our findings strongly support the efficacy of satellite imagery as a valuable resource for dengue prediction, particularly in informing public health policies within low- and middle-income countries. In these countries, where manually collected data of high quality is scarce and dengue virus prevalence is severe, satellite imagery can play a crucial role in improving dengue prevention and control strategies.