Fine-Grained Forecasting of COVID-19 Trends at the County Level in the United States (preprint)

The coronavirus (COVID-19) pandemic has profoundly impacted various aspects of daily life, society, healthcare systems, and global health policies. This pandemic has resulted in more than one hundred million people being infected and, unfortunately, the loss of life for many individuals. Although treatment for the coronavirus is now available, effective forecasting of COVID-19 infection is the most importance to aid public health officials in making critical decisions. However, forecasting COVID-19 trends through time-series analysis poses significant challenges due to the datas inherently dynamic, transient, and noise-prone nature. In this study, we have developed the Fine-Grained Infection Forecast Network (FIGI-Net) model, which provides accurate forecasts of COVID-19 trends up to two weeks in advance. FIGI-Net addresses the current limitations in COVID-19 forecasting by leveraging fine-grained county-level data and a stacked bidirectional LSTM structure. We employ a pre-trained model to capture essential global infection patterns. Subsequently, these pre-trained parameters were transferred to train localized sub-models for county clusters exhibiting comparable infection dynamics. This model adeptly handles sudden changes and rapid fluctuations in data, frequently observed across various times and locations of county-level data, ultimately improving the accuracy of COVID-19 infection forecasting at the county, state, and national levels. FIGI-Net model demonstrated significant improvement over other deep learning-based models and state-of-the-art COVID-19 forecasting models, evident in various standard evaluation metrics. Notably, FIGI-Net model excels at forecasting the direction of infection trends, especially during the initial phases of different COVID-19 outbreak waves. Our study underscores the effectiveness and superiority of our time-series deep learning-based methods in addressing dynamic and sudden changes in infection numbers over short-term time periods. These capabilities facilitate efficient public health management and the early implementation of COVID-19 transmission prevention measures.

Clinical Course and Risk Factors for Liver Injury of Severe and Critical Patients with COVID-19.

Introduction: Information regarding the clinical course of COVID-19 patients with liver injury is very limited, especially in severe and critical patients. The objective of this study was to describe the characteristics and clinical course of liver function in patients admitted with severe and/or critical SARS-CoV-2 infection, as well as explore the risk factors that affect liver function in the enrolled COVID-19 patients. Methods: Information on clinical characteristics of 63 severe and critical patients with confirmed COVID-19 was collected. Data on patients' demographics, laboratory characteristics, laboratory examination, SARS-CoV-2 RNA results and liver test parameters were acquired and analyzed. Results: The incidence of abnormal aspartate aminotransferase, alanine aminotransferase, and total bilirubin in the critical group was significantly higher than in the severe group (respectively 81.48%, 81.49%, 62.67%, and 45.71%, 63.88%, 22.86%, p < 0.05). The time for liver function parameters to reach their extremes was approximately 2-3 weeks after admission. The independent factors associated with liver injury were patients with invasive ventilators, decreased percentages of neutrophils, lymphocytes and monocytes, and sequential organ failure assessment (SOFA) score ≥2 (p < 0.05). Conclusion: Abnormal liver tests are commonly observed in severe and critical patients with COVID-19. Severe patients infected by SARS-CoV-2 should be closely observed and monitored the liver function parameters, particularly when they present with independent risk factors for liver injury.

Constructing of a scientific fitness literacy evaluation model for adults (preprint)

COVID-19-associated lockdown has been implemented worldwide, potentially causing unfavorable changes in lifestyle and psychological health. Physical literacy, healthy diets, and lifestyles play important roles in mitigating the adverse effects. Therefore, this study aimed to enable individuals to understand their fitness literacy and establish a personalized exercise plan. In this study, 903 adults aged 19－59 were surveyed based on the concept of scientific fitness literacy and the influencing factors in the context of the effective containment of COVID-19 before (2019) and during (2020) the pandemic. This study screened four factors from four dimensions–cognition, attitude, ability and skills, and behavior and habits–that can influence adults' scientific fitness literacy. Confirmatory factor analysis (CFA) and AMOS software were used to construct an evaluation index system of scientific fitness literacy for adults. The model consisting 10 items with 4 factors to evaluation index system has good overall fitness, reliability, convergent validity, differential validity, and model stability and was able to analyze the factors that affect the scientific fitness literacy of individuals from different perspectives. This allowed individuals at different stages to have a deeper understanding of scientific fitness literacy.

Comparative therapeutic efficacy of interferon alfa-2b and combination lopinavir/ritonavir plus interferon alfa-2b against SARS-CoV-2.

BACKGROUND: The outbreak of coronavirus disease 2019 (COVID-19) posed an enormous threat to public health. The use of antiviral drugs in patients with this disease have triggered people's attentions. Whether interferon alfa-2b or lopinavir/ritonavir (LPV/r) plus interferon alfa-2b treatment can against SARS-CoV-2 was unknown. The objectives of this study was to evaluate the efficacy and safety of interferon alfa-2b and LPV/r plus interferon alfa-2b for SARS-CoV-2 infection in adult patients hospitalized with COVID-19. METHODS: This is a retrospective cohort study of 123 patients confirmed SARS-CoV-2 infection by PCR on nasopharyngeal swab and symptoms between Jan. 13 and Apr. 23, 2020. All patients received standard supportive care and regular clinical monitoring. Patients were assigned to standard care group (n = 12), interferon alfa-2b group (n = 44), and combination LPV/r plus interferon alfa-2b group (n = 67). The primary endpoints were duration of required oxygen support and virus clearance time. Associations between therapies and these outcomes were assessed by Cox proportional hazards regression. RESULTS: Baseline clinical characteristics were not significantly different among the three groups (P > 0.05). No significant associations were observed between LPV/r/interferon alfa-2b and faster SARS-CoV-2 RNA clearance (HR, 0.85 [95% confidence interval (CI) 0.45-1.61]; P = 0.61 in interferon alfa-2b group vs HR, 0.59 [95% CI 0.32-1.11]; P = 0.10 in LPV/r plus interferon alfa-2b group). Individual therapy groups also showed no significant association with duration of required oxygen support. There were no significant differences among the three groups in the incidence of adverse events (P > 0.05). CONCLUSIONS: In patients with confirmed SARS-CoV-2 infection, no benefit was observed from interferon alfa-2b or LPV/r plus interferon alfa-2b treatment. The findings may provide references for treatment guidelines of patients with SARS-CoV-2 infection.

COVID-19 Cases from the First Local Outbreak of the SARS-CoV-2 B.1.1.7 Variant in China May Present More Serious Clinical Features: A Prospective, Comparative Cohort Study.

The SARS-CoV-2 B.1.1.7 variant has increased sharply in numbers worldwide and is reported to be more contagious than the nonvariant. Little is known regarding the detailed clinical features of B.1.1.7 variant infection. Data on 74 COVID-19 cases from two outbreaks in two districts of Beijing, China were extracted from a cloud database, including 41 cases from Shunyi District (Shunyi B.1.470 group) and 33 from Daxing (Daxing B.1.1.7 group) from December 25, 2020 to January 17, 2021. We conducted a comparison of the clinical characteristics. Seven clinical indicators of the Daxing B.1.1.7 group were significantly higher than those of the Shunyi group, including the proportion with fever over 38°C, the levels of C-reactive protein (CRP), serum amyloid A (SAA), creatine kinase (CK), d-dimer (DD), and CD4+ T lymphocytes (CD4+ T), and the proportion with ground-glass opacity (GGO) in the lung (P values of ≤0.05). After adjusting for age, B.1.1.7 variant infection was a risk factor for elevated CRP (P = 0·045), SAA (P = 0·011), CK (P = 0·034), and CD4+ T (P = 0.029) and for the presence of GGO (P = 0.005). The median threshold cycle (CT) value of reverse transcriptase quantitative PCR (RT-qPCR) tests of the N gene target in the Daxing B.1.1.7 group was significantly lower (P = 0.036) than that in the Shunyi B.1.470 group. Clinical features, including a more serious inflammatory response, pneumonia, and a possibly higher viral load, were detected in the cases infected with B.1.1.7 SARS-CoV-2. The B.1.1.7 variant may have increased pathogenicity. IMPORTANCE The SARS-CoV-2 B.1.1.7 variant, which was first identified in the United Kingdom, has increased sharply in numbers worldwide and was reported to be more contagious than the nonvariant. To our knowledge, no studies investigating the detailed clinical features of COVID-19 cases infected with the B.1.1.7 variant have been published. Local epidemics have rarely occurred in China, but occasionally, a small clustered outbreak triggered by an imported SARS-CoV-2 strain with only one chain of transmission could happen. From late 2020 to early 2021, two clustered COVID-19 outbreaks occurred in Beijing, one of which was caused by the B.1.1.7 variant. The COVID-19 patients from the two outbreaks received similar clinical tests, diagnoses, and treatments. We found that the B.1.1.7 variant infection could lead to a more serious inflammatory response, acute response process, more severe pneumonia, and probably higher viral loads. This therefore implies that the B.1.1.7 variant may have increased pathogenicity.

Case Report: Identification of SARS-CoV-2 in Cerebrospinal Fluid by Ultrahigh-Depth Sequencing in a Patient With Coronavirus Disease 2019 and Neurological Dysfunction.

We reported that the complete genome sequence of SARS-Coronavirus-2 (SARS-CoV-2) was obtained from a cerebrospinal fluid (CSF) sample by ultrahigh-depth sequencing. Fourteen days after onset, seizures, maxillofacial convulsions, intractable hiccups and a significant increase in intracranial pressure developed in an adult coronavirus disease 2019 patient. The complete genome sequence of SARS-CoV-2 obtained from the cerebrospinal fluid indicates that SARS-CoV-2 can invade the central nervous system. In future, along with nervous system assessment, the pathogen genome detection and other indicators are needed for studying possible nervous system infection of SARS-CoV-2.

Bacterial and fungal co-infections among COVID-19 patients in intensive care unit.

This study aimed to investigate the frequency and characteristics of respiratory co-infections in COVID-19 patients in the intensive care unit (ICU). In this retrospective observational study, pathogens responsible for potential co-infections were detected by the bacterial culture, real-time polymerase chain reaction (RT-PCR), or serological fungal antigen tests. Demographic and clinical characteristics, as well as microbial results, were analyzed. Bacterial culture identified 56 (58.3%) positive samples for respiratory pathogens, with the most common bacteria being Burkholderia cepacia (18, 18.8%). RT-PCR detected 38 (76.0%) and 58 (87.9%) positive results in the severe and critical groups, respectively. Most common pathogens detected were Stenotrophomonas maltophilia (28.0%) and Pseudomonas aeruginosa (28.0%) in the severe group and S. maltophilia (45.5%) in the critical group. P. aeruginosa was detected more during the early stage after ICU admission. Acinetobacter baumannii and Staphylococcus aureus were more frequently identified during late ICU admission. Fungal serum antigens were more frequently positive in the critical group than in the severe group, and the positive rate of fungal serum antigens frequency increased with prolonged ICU stay. A high frequency of respiratory co-infections presented in ICU COVID-19 patients. Careful examinations and necessary tests should be performed to exclude these co-infections.

Differentiating Between 2019 Novel Coronavirus Pneumonia and Influenza Using a Nonspecific Laboratory Marker-Based Dynamic Nomogram.

BACKGROUND: There is currently a lack of nonspecific laboratory indicators as a quantitative standard to distinguish between the 2019 coronavirus disease (COVID-19) and an influenza A or B virus infection. Thus, the aim of this study was to establish a nomogram to detect COVID-19. METHODS: A nomogram was established using data collected from 457 patients (181 with COVID-19 and 276 with influenza A or B infection) in China. The nomogram used age, lymphocyte percentage, and monocyte count to differentiate COVID-19 from influenza. RESULTS: Our nomogram predicted probabilities of COVID-19 with an area under the receiver operating characteristic curve of 0.913 (95% confidence interval [CI], 0.883-0.937), greater than that of the lymphocyte:monocyte ratio (0.849; 95% CI, 0.812-0.880; P = .0007), lymphocyte percentage (0.808; 95% CI, 0.768-0.843; P < .0001), monocyte count (0.780; 95% CI, 0.739-0.817; P < .0001), or age (0.656; 95% CI, 0.610-0.699; P < .0001). The predicted probability conformed to the real observation outcomes of COVID-19, according to the calibration curves. CONCLUSIONS: We found that age, lymphocyte percentage, and monocyte count are risk factors for the early-stage prediction of patients infected with the 2019 novel coronavirus. As such, our research provides a useful test for doctors to differentiate COVID-19 from influenza.