Surveillance for Adverse Events Following Immunization With COVID-19 Vaccines in Dalian, China (preprint)

Background: Coronavirus disease 2019 (COVID-19) vaccines have been administered in priority populations in China since December 15, 2020. This study aims to assess the safety of the COVID-19 vaccination programme in Dalian, China.Methods: Passive surveillance for adverse events following immunization (AEFIs) with COVID-19 vaccines was performed by the Dalian Center for Disease Control and Prevention (CDC). Data were collected through June 8, 2021, from the Chinese National Adverse Events Following Immunization System (CNAEFIS) and were verified by local and upper-level CDCs.Findings: A total of 7.12 million doses of vaccine were administered from November 27, 2020, through June 8, 2021, and 623 vaccinees reported adverse events, resulting in a rate of 87.5 events per one million doses. The age-specific rates of AEFIs ranged from 74.0 per one million doses among persons aged 45 to 59 years to 102.0 per one million doses among persons aged 18 to 44 years; the manufacturer-specific rates ranged from 81.1 to 125.2 per one million doses. Among the 623 AEFIs, 544 (87.3%; rate, 76.4 per one million doses) were confirmed as common minor vaccine reactions. Very rare cases of anaphylaxis after vaccination were reported (5 cases; 0.7 per one million doses). Seven cases of AEFIs were classified as serious; however, available information indicated that there was no causal relationship with COVID-19 vaccination.Interpretation: No major safety concerns were identified during the COVID-19 vaccination campaign. There was no evidence of an increased risk of serious adverse events (SAEs).Funding Information: The study was supported by grants from the National Science Fund for Distinguished Young Scholars (No. 81525023), Key Emergency Project of Shanghai Science and Technology Committee (No. 20411950100).Declaration of Interests: H.Y. has received research funding from Sanofi Pasteur GlaxoSmithKline, Yichang HEC Changjiang Pharmaceutical Company, and Shanghai Roche Pharmaceutical Company. None of those research funding is related to development of COVID-19 vaccines. All other authors report no competing interests.

Does non-COVID-19 lung lesion help? investigating transferability in COVID-19 CT image segmentation.

BACKGROUND AND OBJECTIVE: Coronavirus disease 2019 (COVID-19) is a highly contagious virus spreading all around the world. Deep learning has been adopted as an effective technique to aid COVID-19 detection and segmentation from computed tomography (CT) images. The major challenge lies in the inadequate public COVID-19 datasets. Recently, transfer learning has become a widely used technique that leverages the knowledge gained while solving one problem and applying it to a different but related problem. However, it remains unclear whether various non-COVID19 lung lesions could contribute to segmenting COVID-19 infection areas and how to better conduct this transfer procedure. This paper provides a way to understand the transferability of non-COVID19 lung lesions and a better strategy to train a robust deep learning model for COVID-19 infection segmentation. METHODS: Based on a publicly available COVID-19 CT dataset and three public non-COVID19 datasets, we evaluate four transfer learning methods using 3D U-Net as a standard encoder-decoder method. i) We introduce the multi-task learning method to get a multi-lesion pre-trained model for COVID-19 infection. ii) We propose and compare four transfer learning strategies with various performance gains and training time costs. Our proposed Hybrid-encoder Learning strategy introduces a Dedicated-encoder and an Adapted-encoder to extract COVID-19 infection features and general lung lesion features, respectively. An attention-based Selective Fusion unit is designed for dynamic feature selection and aggregation. RESULTS: Experiments show that trained with limited data, proposed Hybrid-encoder strategy based on multi-lesion pre-trained model achieves a mean DSC, NSD, Sensitivity, F1-score, Accuracy and MCC of 0.704, 0.735, 0.682, 0.707, 0.994 and 0.716, respectively, with better genetalization and lower over-fitting risks for segmenting COVID-19 infection. CONCLUSIONS: The results reveal the benefits of transferring knowledge from non-COVID19 lung lesions, and learning from multiple lung lesion datasets can extract more general features, leading to accurate and robust pre-trained models. We further show the capability of the encoder to learn feature representations of lung lesions, which improves segmentation accuracy and facilitates training convergence. In addition, our proposed Hybrid-encoder learning method incorporates transferred lung lesion features from non-COVID19 datasets effectively and achieves significant improvement. These findings promote new insights into transfer learning for COVID-19 CT image segmentation, which can also be further generalized to other medical tasks.