COVID-19 Vaccine-Associated Pneumonitis in the Republic of Korea: A Nationwide Multicenter Survey.

BACKGROUND: Recent reports have suggested that pneumonitis is a rare complication following vaccination against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, its clinical features and outcomes are not well known. The aim of this study was to identify the clinical characteristics and outcomes of patients with vaccine-associated pneumonitis following vaccination against SARS-CoV-2. METHODS: In this nationwide multicenter survey study, questionnaires were distributed to pulmonary physicians in referral hospitals. They were asked to report cases of development or exacerbation of interstitial lung disease (ILD) associated with the coronavirus disease 2019 vaccine. Vaccine-associated pneumonitis was defined as new pulmonary infiltrates documented on chest computed tomography within 4 weeks of vaccination and exclusion of other possible etiologies. RESULTS: From the survey, 49 cases of vaccine-associated pneumonitis were identified between February 27 and October 30, 2021. After multidisciplinary discussion, 46 cases were analyzed. The median age was 66 years and 28 (61%) were male. The median interval between vaccination and respiratory symptoms was 5 days. There were 20 (43%), 17 (37%), and nine (19%) patients with newly identified pneumonitis, exacerbation of pre-diagnosed ILD, and undetermined pre-existing ILD, respectively. The administered vaccines were BNT162b2 and ChAdOx1 nCov-19/AZD1222 each in 21 patients followed by mRNA-1273 in three, and Ad26.COV2.S in one patient. Except for five patients with mild disease, 41 (89%) patients were treated with corticosteroid. Significant improvement was observed in 26 (57%) patients including four patients who did not receive treatment. However, ILD aggravated in 9 (20%) patients despite treatment. Mortality was observed in eight (17%) patients. CONCLUSION: These results suggest pneumonitis as a potentially significant safety concern for vaccines against SARS-CoV-2. Clinical awareness and patient education are necessary for early recognition and prompt management. Additional research is warranted to identify the epidemiology and characterize the pathophysiology of vaccine-associated pneumonitis.

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.