RESEAT: Recurrent Self-Attention Network for Multi-Regional Influenza Forecasting.

Early forecasting of influenza is an important task for public health to reduce losses due to influenza. Various deep learning-based models for multi-regional influenza forecasting have been proposed to forecast future influenza occurrences in multiple regions. While they only use historical data for forecasting, temporal and regional patterns need to be jointly considered for better accuracy. Basic deep learning models such as recurrent neural networks and graph neural networks have limited ability to model both patterns together. A more recent approach uses an attention mechanism or its variant, self-attention. Although these mechanisms can model regional interrelationships, in state-of-the-art models, they consider accumulated regional interrelationships based on attention values that are calculated only once for all of the input data. This limitation makes it difficult to effectively model the regional interrelationships that change dynamically during that period. Therefore, in this article, we propose a recurrent self-attention network (RESEAT) for various multi-regional forecasting tasks such as influenza and electrical load forecasting. The model can learn regional interrelationships over the entire period of the input data using self-attention, and it recurrently connects the attention weights using message passing. We demonstrate through extensive experiments that the proposed model outperforms other state-of-the-art forecasting models in terms of the forecasting accuracy for influenza and COVID-19. We also describe how to visualize regional interrelationships and analyze the sensitivity of hyperparameters to forecasting accuracy.

Model-Agnostic Meta-Learning-based Region-Adaptive Parameter Adjustment Scheme for Influenza Forecasting

Deep learning models perform well when there is enough data available for training, but otherwise the performance deteriorates rapidly owing to the so-called data shortage problem. Recently, model-agnostic meta-learning (MAML) was proposed to alleviate this problem by embedding common prior knowledge from different tasks into the initial parameters of the target model. Data shortages are very common in regional influenza predictions, and MAML also often struggles with regional influenza forecasting, especially when region-specific knowledge, such as peak timing or intensity, varies. In this paper, we propose a novel MAML-based parameter adjustment scheme for influenza forecasting, called MARAPAS. The fundamental idea of our scheme is to adjust the initial parameters obtained from common knowledge to a target region by using adjustment variables. We experimentally show that MARAPAS outperforms other MAML-based methods, in terms of root mean square error and Pearson correlation coefficient. Particularly, this scheme improves the forecasting performance by up to 34 % compared with that of the state-of-the-art schemes. We also show the robust forecasting accuracy of our scheme and demonstrate its applicability by performing zero-shot COVID-19 forecasting.

Cluster-Based Analysis of Infectious Disease Occurrences Using Tensor Decomposition: A Case Study of South Korea.

For a long time, various epidemics, such as lower respiratory infections and diarrheal diseases, have caused serious social losses and costs. Various methods for analyzing infectious disease occurrences have been proposed for effective prevention and proactive response to reduce such losses and costs. However, the results of the occurrence analyses were limited because numerous factors affect the outbreak of infectious diseases and there are complex interactions between these factors. To alleviate this limitation, we propose a cluster-based analysis scheme of infectious disease occurrences that can discover commonalities or differences between clusters by grouping elements with similar occurrence patterns. To do this, we collect and preprocess infectious disease occurrence data according to time, region, and disease. Then, we construct a tensor for the data and apply Tucker decomposition to extract latent features in the dimensions of time, region, and disease. Based on these latent features, we conduct k-means clustering and analyze the results for each dimension. To demonstrate the effectiveness of this scheme, we conduct a case study on data from South Korea and report some of the results.