A cross-sectional study on the mental health of patients with COVID-19 1 year after discharge in Huanggang, China.

OBJECTIVE: This study is aimed to investigate the mental health status of COVID-19 survivors 1 year after discharge from hospital and reveal the related risk factors. METHODS: From April 11 to May 11, 2021, 566 COVID-19 survivors in Huanggang city were recruited through their primary doctors. A total of 535 participants (94.5%) admitted to participate in the survey and completed the questionnaires. Five scales were applied including 7-Items Generalized Anxiety Disorder Scale, Patient Health Questionnaire-9, Impact of Event Scale-Revised, Pittsburgh Sleep Quality Index, and Fatigue Scale-14. The chi-square and the Fisher's exact test were used to evaluate the classification data, multivariate logistic regression was used to explore the related factors of sleep quality, fatigue, anxiety, depression, and post-traumatic stress disorder (PTSD). RESULTS: One year after being discharged, of the 535 COVID-19 survivors, 252 (47.1%) had poor sleep quality; 157 (29.3%) had the symptoms of fatigue; 84 (15.7%),112 (20.9%), and 130 (24.3%) suffered from symptoms of anxiety, depression, and PTSD, respectively. The logistic regression analysis showed that history of chronic disease was risk factor for poor sleep quality (OR 2.501; 95% CI, 1.618-3.866), fatigue (OR 3.284; 95% CI 2.143-5.033), PTSD (OR 2.323; 95% CI 1.431-3.773) and depression (OR 1.950; 95% CI 1.106-3.436) in COVID-19 survivors. Smoking contributed to the poor sleep quality (OR 2.005; 95% CI 1.044-3.850), anxiety (OR 4.491; 95% CI 2.276-8.861) and depression (OR 5.459; 95% CI 2.651-11.239) in survivors. Drinking influenced fatigue (OR 2.783; 95% CI 1.331-5.819) and PTSD (OR 4.419; 95% CI 1.990-9.814) in survivors. Compared with college-educated survivors, survivors with high school education were at higher risk for poor sleep quality (OR 1.828; 95% CI 1.050-3.181) and PTSD (OR 2.521; 95% CI 1.316-4.830), and survivors with junior high school education were at higher risk for PTSD (OR 2.078; 95% CI 1.039-4.155). Compared with overweight survivors (BMI ≥ 23.0), survivors with normal BMI (18.5-22.9) (OR 0.600; 95% CI 0.405-0.889) were at lower risk for fatigue. While being housewife (OR 0.390; 95% CI 0.189-0.803) was protective factor for fatigue and having more family members was protective factor for PTSD (OR 0.404 95% CI 0.250-0.653) in survivors. CONCLUSIONS: One year after infection, poor sleep quality, fatigue, anxiety, depression, and PTSD, still existed in a relatively high proportion of COVID-19 survivors. Chronic disease history was an independent risk factor for poor sleep quality, fatigue, depression, and PTSD. Participants with low education levels were more likely to have mental problems than the others. We should focus on the long-term psychological impact of COVID-19 on survivors, and the government should apply appropriate mental health services to offer psychiatric support.

Class-Aware Attention Network for infectious keratitis diagnosis using corneal photographs.

Infectious keratitis is one of the common ophthalmic diseases and also one of the main blinding eye diseases in China, hence rapid and accurate diagnosis and treatment for infectious keratitis are urgent to prevent the progression of the disease and limit the degree of corneal injury. Unfortunately, the traditional manual diagnosis accuracy is usually unsatisfactory due to the indistinguishable visual features. In this paper, we propose a novel end-to-end fully convolutional network, named Class-Aware Attention Network (CAA-Net), for automatically diagnosing infectious keratitis (normal, viral keratitis, fungal keratitis, and bacterial keratitis) using corneal photographs. In CAA-Net, a class-aware classification module is first trained to learn class-related discriminative features using separate branches for each class. Then, the learned class-aware discriminative features are fed into the main branch and fused with other feature maps using two attention strategies to assist the final multi-class classification performance. For the experiments, we have built a new corneal photograph dataset with 1886 images from 519 patients and conducted comprehensive experiments to verify the effectiveness of our proposed method. The code is available at https://github.com/SWF-hao/CAA-Net_Pytorch.

Asthma mortality attributable to ambient temperatures: A case-crossover study in China.

BACKGROUND: Whether ambient temperature exposure contributes to death from asthma remains unknown to date. We therefore conducted a case-crossover study in China to quantitatively evaluate the association and burden of ambient temperature exposure on asthma mortality. METHODS: Using data from the National Mortality Surveillance System in China, we conducted a time-stratified case-crossover study of 15 888 individuals who lived in Hubei and Jiangsu province, China and died from asthma as the underlying cause in 2015-2019. Individual-level exposures to air temperature and apparent temperature on the date of death and 21 days prior were assessed based on each subject's residential address. Distributed lag nonlinear models based on conditional logistic regression were used to quantify exposure-response associations and calculate fraction and number of deaths attributable to non-optimum ambient temperatures. RESULTS: We observed a reverse J-shaped association between air temperature and risk of asthma mortality, with a minimum mortality temperature of 21.3 °C. Non-optimum ambient temperature is responsible for substantial excess mortality from asthma. In total, 26.3% of asthma mortality were attributable to non-optimum temperatures, with moderate cold, moderate hot, extreme cold and extreme hot responsible for 21.7%, 2.4%, 2.1% and 0.9% of asthma mortality, respectively. The total attributable fraction and number was significantly higher among adults aged less than 80 years in hot temperature. CONCLUSIONS: Exposure to non-optimum ambient temperature, especially moderate cold temperature, was responsible for substantial excess mortality from asthma. These findings have important implications for planning of public-health interventions to minimize the adverse respiratory damage from non-optimum ambient temperature.

Urban Pandemic Vulnerability and COVID-19: A New Framework to Assess the Impacts of Global Pandemics in the Metropolitan Region of Amsterdam

This paper presents the concept of urban pandemic vulnerability as a crucial framework for understanding how COVID-19 affects cities and how they react to pandemics. We adapted existing social and environmental urban vulnerability frameworks to assess pandemic impacts and responses, identifying the appropriate components and spatial, environmental and socio-demographic variables of interest. Pandemic vulnerability depends on exposure, sensitivity and adaptive capacity features, which occur in different combinations in different parts of a city. The model was applied to the Metropolitan Region of Amsterdam (MRA) to create a map of pandemic vulnerability. This map differentiates between affected areas according to the types of vulnerability they experience, and it accurately identified the most vulnerable areas in line with real-world data. The findings contribute to clarifying the challenges brought by COVID-19, identifying vulnerability thresholds and guiding planning towards pandemic resilience.

The effectiveness of governmental nonpharmaceutical interventions against COVID-19 at controlling seasonal influenza transmission: an ecological study.

BACKGROUND: A range of strict nonpharmaceutical interventions (NPIs) were implemented in many countries to combat the coronavirus 2019 (COVID-19) pandemic. These NPIs may also be effective at controlling seasonal influenza virus infections, as influenza viruses have the same transmission path as severe acute respiratory syndrome coronavirus 2. The aim of this study was to evaluate the effects of different NPIs on the control of seasonal influenza. METHODS: Data for 14 NPIs implemented in 33 countries and the corresponding influenza virological surveillance data were collected. The influenza suppression index was calculated as the difference between the influenza positivity rate during its period of decline from 2019 to 2020 and during the influenza epidemic seasons in the previous 9 years. A machine learning model was developed using an extreme gradient boosting tree regressor to fit the NPI and influenza suppression index data. The SHapley Additive exPlanations tool was used to characterize the NPIs that suppressed the transmission of influenza. RESULTS: Of all NPIs tested, gathering limitations had the greatest contribution (37.60%) to suppressing influenza transmission during the 2019-2020 influenza season. The three most effective NPIs were gathering limitations, international travel restrictions, and school closures. For these three NPIs, their intensity threshold required to generate an effect were restrictions on the size of gatherings less than 1000 people, ban of travel to all regions or total border closures, and closing only some categories of schools, respectively. There was a strong positive interaction effect between mask-wearing requirements and gathering limitations, whereas merely implementing a mask-wearing requirement, and not other NPIs, diluted the effectiveness of mask-wearing requirements at suppressing influenza transmission. CONCLUSIONS: Gathering limitations, ban of travel to all regions or total border closures, and closing some levels of schools were found to be the most effective NPIs at suppressing influenza transmission. It is recommended that the mask-wearing requirement be combined with gathering limitations and other NPIs. Our findings could facilitate the precise control of future influenza epidemics and other potential pandemics.

Drivers and forecasts of multiple waves of the coronavirus disease 2019 pandemic: A systematic analysis based on an interpretable machine learning framework.

Coronavirus disease 2019 (COVID-19) has become a global pandemic and continues to prevail with multiple rebound waves in many countries. The driving factors for the spread of COVID-19 and their quantitative contributions, especially to rebound waves, are not well studied. Multidimensional time-series data, including policy, travel, medical, socioeconomic, environmental, mutant and vaccine-related data, were collected from 39 countries up to 30 June 2021, and an interpretable machine learning framework (XGBoost model with Shapley Additive explanation interpretation) was used to systematically analyze the effect of multiple factors on the spread of COVID-19, using the daily effective reproduction number as an indicator. Based on a model of the pre-vaccine era, policy-related factors were shown to be the main drivers of the spread of COVID-19, with a contribution of 60.81%. In the post-vaccine era, the contribution of policy-related factors decreased to 28.34%, accompanied by an increase in the contribution of travel-related factors, such as domestic flights, and contributions emerged for mutant-related (16.49%) and vaccine-related (7.06%) factors. For single-peak countries, the dominant ones were policy-related factors during both the rising and fading stages, with overall contributions of 33.7% and 37.7%, respectively. For double-peak countries, factors from the rebound stage contributed 45.8% and policy-related factors showed the greatest contribution in both the rebound (32.6%) and fading (25.0%) stages. For multiple-peak countries, the Delta variant, domestic flights (current month) and the daily vaccination population are the three greatest contributors (8.12%, 7.59% and 7.26%, respectively). Forecasting models to predict the rebound risk were built based on these findings, with accuracies of 0.78 and 0.81 for the pre- and post-vaccine eras, respectively. These findings quantitatively demonstrate the systematic drivers of the spread of COVID-19, and the framework proposed in this study will facilitate the targeted prevention and control of the ongoing COVID-19 pandemic.

Retrospective quantitative detection of SARS-CoV-2 by digital PCR showing high accuracy for low viral load specimens.

INTRODUCTION: Accurate detection of severe acute respiratory syndrome coronavirus 2 is critical for diagnosis and disease status evaluation of Coronavirus disease 2019. We retrospectively evaluated the infection status and viral load of severe acute respiratory syndrome coronavirus 2 in Nantong city, China, using a quantitative digital polymerase chain reaction and reverse-transcription PCR. METHODOLOGY: A total of 103 clinical specimens from 31 patients were collected and tested by digital PCR and reverse-transcription PCR. RESULTS: The overall accuracy of digital PCR was 96.8%, which was higher than the overall accuracy of 87.1% for reverse-transcription PCR. 4 (3.88%) specimens for ORF1ab and 22 (21.36%) specimens for N gene were negative by reverse-transcription PCR but positive by digital PCR. 3 (2.91%, 3/103) specimens of ORF1ab were positive by reverse-transcription PCR but negative by digital PCR. The digital PCR assay exhibited higher sensitivity to measure the N gene than the ORF1ab gene (p < 0.01). CONCLUSIONS: Our results showed that digital PCR assay provides more reliable detection of Coronavirus disease 2019 than reverse-transcription PCR, especially for low viral load specimens.

iCOVID: interpretable deep learning framework for early recovery-time prediction of COVID-19 patients.

Most prior studies focused on developing models for the severity or mortality prediction of COVID-19 patients. However, effective models for recovery-time prediction are still lacking. Here, we present a deep learning solution named iCOVID that can successfully predict the recovery-time of COVID-19 patients based on predefined treatment schemes and heterogeneous multimodal patient information collected within 48 hours after admission. Meanwhile, an interpretable mechanism termed FSR is integrated into iCOVID to reveal the features greatly affecting the prediction of each patient. Data from a total of 3008 patients were collected from three hospitals in Wuhan, China, for large-scale verification. The experiments demonstrate that iCOVID can achieve a time-dependent concordance index of 74.9% (95% CI: 73.6-76.3%) and an average day error of 4.4 days (95% CI: 4.2-4.6 days). Our study reveals that treatment schemes, age, symptoms, comorbidities, and biomarkers are highly related to recovery-time predictions.

A Cascade-SEME network for COVID-19 detection in chest x-ray images.

PURPOSE: The worldwide spread of the SARS-CoV-2 virus poses unprecedented challenges to medical resources and infection prevention and control measures around the world. In this case, a rapid and effective detection method for COVID-19 can not only relieve the pressure of the medical system but find and isolate patients in time, to a certain extent, slow down the development of the epidemic. In this paper, we propose a method that can quickly and accurately diagnose whether pneumonia is viral pneumonia, and classify viral pneumonia in a fine-grained way to diagnose COVID-19. METHODS: We proposed a Cascade Squeeze-Excitation and Moment Exchange (Cascade-SEME) framework that can effectively detect COVID-19 cases by evaluating the chest x-ray images, where SE is the structure we designed in the network which has attention mechanism, and ME is a method for image enhancement from feature dimension. The framework integrates a model for a coarse level detection of virus cases among other forms of lung infection, and a model for fine-grained categorisation of pneumonia types identifying COVID-19 cases. In addition, a Regional Learning approach is proposed to mitigate the impact of non-lesion features on network training. The network output is also visualised, highlighting the likely areas of lesion, to assist experts' assessment and diagnosis of COVID-19. RESULTS: Three datasets were used: a set of Chest x-ray Images for Classification with bacterial pneumonia, viral pneumonia and normal chest x-rays, a COVID chest x-ray dataset with COVID-19, and a Lung Segmentation dataset containing 1000 chest x-rays with masks in the lung region. We evaluated all the models on the test set. The results shows the proposed SEME structure significantly improves the performance of the models: in the task of pneumonia infection type diagnosis, the sensitivity, specificity, accuracy and F1 score of ResNet50 with SEME structure are significantly improved in each category, and the accuracy and AUC of the whole test set are also enhanced; in the detection task of COVID-19, the evaluation results shows that when SEME structure was added to the task, the sensitivities, specificities, accuracy and F1 scores of ResNet50 and DenseNet169 are improved. Although the sensitivities and specificities are not significantly promoted, SEME well balanced these two significant indicators. Regional learning also plays an important role. Experiments show that Regional Learning can effectively correct the impact of non-lesion features on the network, which can be seen in the Grad-CAM method. CONCLUSIONS: Experiments show that after the application of SEME structure in the network, the performance of SEME-ResNet50 and SEME-DenseNet169 in both two datasets show a clear enhancement. And the proposed regional learning method effectively directs the network's attention to focus on relevant pathological regions in the lung radiograph, ensuring the performance of the proposed framework even when a small training set is used. The visual interpretation step using Grad-CAM finds that the region of attention on radiographs of different types of pneumonia are located in different regions of the lungs.

Multiscale Attention Guided Network for COVID-19 Diagnosis Using Chest X-Ray Images.

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.

Genomic variations of SARS-CoV-2 and molecular diagnosis

Rapid and accurate detection of contagium virus is a key tool for controlling the outbreak. As the number of patients infected with 2019 novel coronavirus (SARS-CoV-2) pneumonia is increasing and the epidemic is spreading, many hospitals, laboratories and pharmaceutical companies have developed diagnostic reagents that can detect SARS-CoV-2. Currently, genome sequencing or real-time PCR is a method for detecting SARS-CoV-2. However, there have been reported cases of negative results by nucleic acid detection but confirmed on the radiological CT scan. How to improve the diagnostic efficiency and the sensitivity of molecular detection remains to be solved. To analyze the variations in viral genomic sequence may be helpful in guiding the prevention and treatment of diseases infected by SARS-CoV-2. At present, controversy still exists over the source of SARS-CoV-2 even though the probable origin is bat in nature. This article summarizes the recent findings of SARS-CoV-2 from genetic, virological and evolutionary biological perspectives. By comparing the genomic variations among SARS-CoV-2 infected patients, we hope the findings can be used in the viral detection and potential antiviral therapy. Also, it will be great if we can trace the spread of the virus from one person to another, which will be an much effective way to predict and control the spread from the virus-carrier without symptoms at the incubation period to the others.

Modifiable areal unit problem and environmental factors of COVID-19 outbreak.

Several recent studies have explored the association between environmental factors, such as temperature, humidity, and air pollution, and the severity of the COVID-19 outbreak by analyzing the statistical association at the district level. However, we argue that the modifiable areal unit problem (MAUP) arises when aggregating disease and environmental data into districts, leading to bias in such studies. Therefore, in this study, we analyzed the association between environmental factors and the number of COVID-19 death cases under different aggregation strategies to illustrate the presence of MAUP. We used real-world COVID-19 outbreak data from the Hubei and Henan Provinces and studied their association with atmospheric NO2 levels. By fitting linear regression models with penalized splines on NO2, we found that the association between COVID-19 mortality and NO2 varies when data were aggregated (1) at the city level, (2) under two different aggregation strategies, and (3) at the provincial level, indicating the presence of MAUP. Therefore, this study reminds researchers of the presence of MAUP and the necessity to minimize this problem while exploring the environmental determinants of the COVID-19 outbreak.