Changes of factors associated with vaccine hesitancy in Chinese residents: A qualitative study

Introduction There is an urgent need to address vaccine hesitancy to achieve booster vaccination. This study aimed to reveal the factors associated with vaccine hesitancy (including COVID-19 vaccine) among Chinese residents, address modifications of the factors since the previous year, and propose vaccination rate improvement measures. Materials and methods This qualitative return visit study was performed between January and mid-February 2022, following the last interview conducted between February and March 2021. According to an outline designed in advance, 60 Chinese residents from 12 provinces participated in semi-structured interviews. Results Vaccine safety was the biggest concern raised by respondents, followed by self-immunity and vaccine effectiveness, eliciting concern since the interview last year. Notably, online media accounted for a more significant portion of suggestion sources than before, and fear of pain was a novel factor affecting vaccine hesitancy. Moreover, unlike other areas, those from provinces with a per capita gross domestic product of 3–5 (RMB 10,000) reported less concern about vaccine price and effectiveness. They tended to seek advice via online media less and were greatly influenced by vaccination policies. Conclusions Influential factors of vaccine hesitancy among Chinese residents are changing dynamically. Monitoring these trends is essential for public health measures and higher vaccination levels.

The Effectiveness of Governmental Nonpharmaceutical Interventions Against COVID-19 On The Control of Seasonal Influenza Transmission: An Ecological Study (preprint)

Background: A range of strict nonpharmaceutical interventions (NPIs) had been implemented in many countries to combat the COVID-19 pandemic. These NPIs might also be effective in controlling the seasonal influenza virus, which share the same transmission path with SARS-CoV-2. The aim of this study is to evaluate the effect of different NPIs for control of seasonal influenza. Methods: : Data on 14 NPIs implemented in 33 countries and corresponding data on influenza virologic surveillance were collected. The influenza suppression index was calculated as the difference between the influenza-positive rate during its decline period from 2019 to 2020 and that during influenza epidemic seasons in the previous 9 years. A machine learning model was developed by using extreme gradient boosting tree (XGBoost) regressor to fit NPI data and influenza suppression index. SHapley Additive exPlanations (SHAP) was used to characterize NPIs in suppressing influenza. Results: : Gathering limitation contributed the most (37.60%) among all NPIs in suppressing influenza transmission in the 2019-2020 influenza season. The top three effective NPIs were gathering limitation, international travel restriction, and school closure. Regarding the three NPIs, their intensity threshold to generate effect were restrictions on the size of gatherings less than 1000 people, travel bans on all regions or total border closure, and closing only some categories of schools, respectively. There was a strong positive interaction effect between mask wearing requirement and gathering limitation, whereas merely implementing mask wearing requirement but ignoring other NPIs would dilute mask wearing requirement’s effectiveness in suppressing influenza. Conclusions: : Gathering limitation, travel bans on all regions or total border closure, and closing some levels of schools are the most effective NPIs to suppress influenza transmission. Mask wearing requirement is advised to be combined with gathering limitation and other NPIs. Our findings could facilitate the precise control of future influenza epidemics and potential pandemics.

Multiscale Attention Guided Network for COVID-19 Diagnosis Using Chest X-ray Images (preprint)

Coronavirus disease 2019 (COVID-19) is one of the most destructive pandemic after millennium, forcing the world to tackle a health crisis. Automated lung infections classification using chest X-ray (CXR) images could strengthen diagnostic capability when handling COVID-19. However, classifying COVID-19 from pneumonia cases using CXR image is a difficult task because of shared spatial characteristics, high feature variation and contrast diversity between cases. Moreover, massive data collection is impractical for a newly emerged disease, which limited the performance of data thirsty deep learning models. To address these challenges, Multiscale Attention Guided deep network with Soft Distance regularization (MAG-SD) is proposed to automatically classify COVID-19 from pneumonia CXR images. In MAG-SD, MA-Net is used to produce prediction vector and attention from multiscale feature maps. To improve the robustness of trained model and relieve the shortage of training data, attention guided augmentations along with a soft distance regularization are posed, which aims at generating meaningful augmentations and reduce noise. Our multiscale attention model achieves better classification performance on our pneumonia CXR image dataset. Plentiful experiments are proposed for MAG-SD which demonstrates its unique advantage in pneumonia classification over cutting-edge models. The code is available at https://github.com/JasonLeeGHub/MAG-SD.

A cascade network for Detecting COVID-19 using chest x-rays (preprint)

The worldwide spread of pneumonia caused by a novel coronavirus poses an unprecedented challenge to the world's medical resources and prevention and control measures. Covid-19 attacks not only the lungs, making it difficult to breathe and life-threatening, but also the heart, kidneys, brain and other vital organs of the body, with possible sequela. At present, the detection of COVID-19 needs to be realized by the reverse transcription-polymerase Chain Reaction (RT-PCR). However, many countries are in the outbreak period of the epidemic, and the medical resources are very limited. They cannot provide sufficient numbers of gene sequence detection, and many patients may not be isolated and treated in time. Given this situation, we researched the analytical and diagnostic capabilities of deep learning on chest radiographs and proposed Cascade-SEMEnet which is cascaded with SEME-ResNet50 and SEME-DenseNet169. The two cascade networks of Cascade - SEMEnet both adopt large input sizes and SE-Structure and use MoEx and histogram equalization to enhance the data. We first used SEME-ResNet50 to screen chest X-ray and diagnosed three classes: normal, bacterial, and viral pneumonia. Then we used SEME-DenseNet169 for fine-grained classification of viral pneumonia and determined if it is caused by COVID-19. To exclude the influence of non-pathological features on the network, we preprocessed the data with U-Net during the training of SEME-DenseNet169. The results showed that our network achieved an accuracy of 85.6\% in determining the type of pneumonia infection and 97.1\% in the fine-grained classification of COVID-19. We used Grad-CAM to visualize the judgment based on the model and help doctors understand the chest radiograph while verifying the effectivene.

SARS-CoV-2 detection using digital PCR for COVID-19 diagnosis, treatment monitoring and criteria for discharge (preprint)

Background: SARS-CoV-2 nucleic acid detection by RT-PCR is one of the criteria approved by China FDA for diagnosis of COVID-19. However, inaccurate test results (for example, high false negative rate and some false positive rate) were reported in both China and US CDC using the RT-PCR method. Inaccurate results are caused by inadequate detection sensitivity of RT-PCR, low viral load in some patients, difficulty to collect samples from COVID-19 patients, insufficient sample loading during RT-PCR tests, and RNA degradation during sample handling process. False negative detection could subject patients to multiple tests before diagnosis can be made, which burdens health care system. Delayed diagnosis could cause infected patients to miss the best treatment time window. False negative detection could also lead to prematurely releasing infected patients who still carry residual SARS-CoV-2 virus. In this case, these patients could infect many others. A high sensitivity RNA detection method to resolve the existing issues of RT-PCR is in need for more accurate COVID-19 diagnosis. Methods: Digital PCR (dPCR) instrument DropX-2000 and assay kits were used to detect SARS-CoV-2 from 108 clinical specimens from 36 patients including pharyngeal swab, stool and blood from different days during hospitalization. Double-blinded experiment data of 108 clinical specimens by dPCR methods were compared with results from officially approved RT-PCR assay. A total of 109 samples including 108 clinical specimens and 1 negative control sample were tested in this study. All of 109 samples, 26 were from 21patients reported as positive by officially approved clinical RT-PCR detection in local CDC and then hospitalized in Nantong Third Hospital. Among the 109 samples, dPCR detected 30 positive samples on ORFA1ab gene, 47 samples with N gene positive, and 30 samples with double positive on ORFA1ab and N genes. Results: The lower limit of detection of the optimize dPCR is at least 10-fold lower than that of RT-PCR. The overall accuracy of dPCR for clinical detection is 96.3%. 4 out 4 of (100 %) negative pharyngeal swab samples checked by RT-PCR were positive judged by dPCR based on the follow-up investigation. 2 of 2 samples in the RT-PCR grey area (Ct value > 37) were confirmed by dPCR with positive results. 1 patient being tested positive by RT-PCR was confirmed to be negative by dPCR. The dPCR results show clear viral loading decrease in 12 patients as treatment proceed, which can be a useful tool for monitoring COVID-19 treatment. Conclusions: Digital PCR shows improved lower limit of detection, sensitivity and accuracy, enabling COVID-19 detection with less false negative and false positive results comparing with RT-PCR, especially for the tests with low viral load specimens. We showed evidences that dPCR is powerful in detecting asymptomatic patients and suspected patients. Digital PCR is capable of checking the negative results caused by insufficient sample loading by quantifying internal reference gene from human RNA in the PCR reactions. Multi-channel fluorescence dPCR system (FAM/HEX/CY5/ROX) is able to detect more target genes in a single multiplex assay, providing quantitative count of viral load in specimens, which is a powerful tool for monitoring COVID-19 treatment.