Nowcasting influenza‐like illness (ILI) via a deep learning approach using google search data: An empirical study on Taiwan ILI

Abstract

Influenza outbreaks have brought increasing challenges to public health systems globally. The effective and efficient tracking of influenza can help authorities make informed and proactive decisions. In this study, we focus on nowcasting influenza epidemics at regional level. To alleviate the information lag between the release of the Centers for Disease Control's influenza-like illness (ILI) reports and real-time influenza activity, we incorporate Google search data and holiday effects to facilitate the ILI nowcasting. We develop a deep learning framework by extending the spatiotemporal residual network (ST-ResNet) to nowcast ILI rates in irregular-shaped region. We investigate the effect of temporal and spatial dependencies among irregular-shaped regions as well as external influence on ILI nowcasting. Various forecasting models, including time series models, penalized regression models, and other deep learning models, are employed for evaluating the performance of the proposed framework. Moreover, based on city-level ILI data in Taiwan, we conduct extensive experiments for methods validation. The results show that the extended ST-ResNet can effectively capture the complex spatiotemporal dependencies of ILI activity and the effects of external variables. Additionally, the strategy of incorporating Google search data and holiday effects improves the prediction. The findings may provide insights into the utility of various statistical and deep learning methods for effective ILI tracking at regional level and facilitate informed decision making for public health authorities.