Comorbidity of purulent meningitis with COVID‐19: A case report

To date, only a few cases of intracranial infection related to severe acute respiratory syndrome‐coronavirus‐2 (SARS‐CoV‐2) were reported. Here we describe a case of coronavirus disease 2019 (COVID‐19) that was comorbid with purulent meningitis. A 62‐year‐old male patient was diagnosed with moderate COVID‐19 and had no fever or cough after treatment. However, he suffered from a head injury and experienced headache and fever immediately after the accident. Computed tomography (CT) of the brain showed bilateral frontal lobe contusion, subdural hematoma, and subarachnoid hemorrhage. In the following days, the patient suffered from recurrent fever, although chest CT did not show evidence of worsening of infection. Several lumbar punctures were made, confirming increased cerebrospinal fluid (CSF) pressure and karyocyte count. SARS‐CoV‐2 nucleic acid was not detected in CSF but revealed the presence of Escherichia coli. Thus, the patient was diagnosed with purulent meningitis, presumably caused by brain trauma or the immunologic dysfunction caused by COVID‐19, which was supported by the significant reduction of all kinds of immune cells. Since immunologic dysfunction is commonly presented in COVID‐19 patients, comorbidity with meningitis should be considered when a COVID‐19 patient presents with headache and fever. Lumbar punctures and CSF cultures may help in the diagnosis.

A local COVID - 19 outbreak in Dalian caused by imported cold - chain products contaminated by SARS-CoV-2

On December 15, 2020, four dock workers tested positive for severe acute respiratory syndrome-coronavirus 2 (SARS-COV-2) nucleic acids and were reported by Dalian. Up until then, Dalian City had not reported local cases for 136 consecutive days. In this coronavirus disease 2019 (COVID-19) outbreak (referred to as the "Dalian COVID-19 outbreak"), samples from all infected persons (83) and part from the ship cargoes in contact With them during December 15, 2020 to January 8, 2021 were collected. Confirmed cases accounted for 61.45% (51/83) and asymptomatic infections accounted for 38.55% (32/83). Through high-throughput sequencing, 76 SARS-CoV-2 whole-genome sequences were obtained, of which 72 (86.75%) were from clinical samples, and 4 from cold-chain food packaging surface samples on cargo ship A of country R. Refer to Wuhan reference strain (NC_045512), genome analysis revealed 12-16 nucleotide mutations in 76 whole genomes sharing 12 nucleotide mutations and belong to the SARS-CoV-2 branch of B.1.1. Viral genomics and field epidemiological investigations showed that the Dalian COVID-19 outbreak was a local epidemic caused by dock workers infected with imported cold - chain products contaminated with SARS - CoV - 2. During transmission, 3 Virus generations and three relatively independent transmission chains were formed.

Integrating Transformer and GCN for COVID-19 Forecasting

The spread of corona virus disease 2019 (COVID-19) has coincided with the rise of Transformer and graph neural networks, leading several studies to propose using them to better predict the evolution of a pandemic. The inconveniences of infectious diseases make it important to predict their spread. However, the single deep learning (DL) model has the problems of unstable prediction effect and poor convergence. When calculating the relationship between different positions within a sequence, Transformer does not consider the local context in which each position is located, which can make the prediction vulnerable to outliers, so the integration of the graph convolutional network (GCN) to capture local information is considered. In this paper, we use Transformer to encode the time sequence information of COVID-19 and GCN to decode the time sequence information with graph structure, so that Transformer and GCN are perfectly combined and spatial information is used to further study the integration of these two methods. In addition, we improve the traditional positional encoding structure and propose a dynamic positional encoding technique to extract dynamic temporal information effectively, which is proved to be the key to capture spatial and temporal patterns in data. To make our predictions more useful, we only focused on three states in the United States, covering one of the most affected states, one of the least affected states, and one intermediate state. We used mean absolute percentage error and mean square error as evaluation indexes. Experimental results show that the proposed time series model has better predictive performance than the current DL models. Moreover, the convergence of our model is also better than the current DL models, providing a more accurate reference for the prevention of epidemics.

Long-term Improvement of Air Quality Associated with Lung Function Benefits in Chinese Young Adults: A Quasi-experiment Cohort Study (preprint)

Background: Long-term exposure to air pollution is associated with lung function impairment. However, whether long-term improvements in air quality could improve lung function is unclear. Methods: : We conducted a prospective quasi-experiment cohort study with 1731 college students in Shandong, China from September 2019 to September 2020, covering COVID-19 lockdown period. Data on PM 2.5 , PM 10 , NO 2 and SO 2 concentrations were obtained from China Environmental Monitoring Station. The concentration of O3 was obtained from Tracking Air Pollution in China. Lung function indicators included forced vital capacity (FVC), forced expiratory volume in 1 second (FEV1) and forced expiratory flow at 50% of FVC (FEF50%). Linear mixed-effects model was used to examine the associations between the change of air pollutants’ concentrations and the change of lung functions. We also conducted stratified analysis by sex. Results: : Compared with 2019, the mean FVC, FEV1 and FEF50% were elevated by 414.4ml, 321.5ml, and 28.4ml respectively in 2020. Every 5μg/m 3 decrease in annual average PM 2.5 concentrations was associated with 36.0ml [95% confidence interval (CI):6.0, 66.0ml], 46.1ml (95% CI:16.7, 75.5ml), and 124.2ml/s (95% CI:69.5, 178.9ml/s) increment in the FVC, FEV1, and FEF50%, respectively. Similar associations were found for PM 10 . There was no significant effect difference between male and female. Conclusions: : Long-term improvement of air quality can improve lung function among young adults. Stricter policies on improving air quality are needed to protect human health. Funding Taishan Scholar Program

Fostering voluntary compliance in the COVID-19 pandemic: An analytical framework of information disclosure

Although there have been studies investigating the relationship between information disclosure and voluntary compliance behaviors, the terrain of such research is largely fragmented and has been rarely tested empirically in the pandemic contexts. This article reviewed the intervention and control of the pandemic from the perspective of information disclosure with reflections on the experience in China. Furthermore, the authors propose a comprehensive framework demonstrating the overall landscape of information disclosure and voluntary compliance behaviors with highlights on (a) the tensions between privacy and information transparency;(b) the trade-offs between policy rigorousness and compliance behaviors;(c) different sources of information and how they influence public behaviors differently;and most importantly, (d) how the variegated configurations and contextualization of factors result in different influencing and moderating mechanisms between information disclosure and voluntary compliance behaviors. In the end, the authors call for future research and reforms in pandemic control practice to focus on the dynamics of information disclosure, government actions, and public compliance behaviors, which has been largely neglected so far. (PsycInfo Database Record (c) 2021 APA, all rights reserved)