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BACKGROUND: Seasonal influenza activity showed a sharp decline in activity at the beginning of the Corona Virus Disease 2019 (COVID-19) emergence. Whether there is an epidemiological correlation between the dynamic of two respiratory infectious diseases and their future trends needs to be explored. OBJECTIVE: To assess the correlation between COVID-19 and influenza activity and estimate their upcoming epidemiological trends. METHODS: We retrospectively described the dynamics of COVID-19 and influenza in six World Health Organization (WHO) regions from January 2020-March 2023, and used the long short-term memory (LSTM) machine learning model to learn potential patterns of previously observed activity to predict trends for the next sixteen weeks. Finally, the past and future correlation in epidemiology between two respiratory infectious diseases was assessed by the Spearman correlation coefficients. RESULTS: With the emergence of original strain and other severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants, influenza activity kept below 10% for more than one year in the six WHO regions. Subsequently, it gradually rose as the Delta activity dropped, but still peaked below Delta. During the Omicron pandemic and the upcoming period, the two increased as each other's activity decreased, becoming interactively dominant more than once and lasting 3-4 months. Correlation analysis showed that COVID-19 and influenza activity presented a predominantly negative correlation with coefficients above -0.3 in WHO regions, especially during the Omicron pandemic and the estimated upcoming period. They had a transient positive correlation in the European Region of WHO (EURO), and the Western Pacific Region of WHO (WPRO) when multiple dominant strains were mixed pandemic. CONCLUSIONS: Influenza activity and former seasonal epidemiological patterns are shaken by the COVID-19 pandemic. Their activities are moderately and above inversely correlated, oppressing and competing with each other, showing a seesaw effect. In the post-pandemic era of COVID-19, the seesaw trends may be more prominent, prompting the possibility of using one another as early warning signals for future estimates and conducting optimized annual vaccine campaigns.
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The COVID-19 pandemic has been suggested to cause psychological problems such as anxiety, depression, panic, and insomnia. This systematic review and meta-analysis aims to assess the efficacy of psychological interventions (including CBT, DBT, and mindfulness based interventions) in reducing distressing feelings in college students during the COVID-19 pandemic. Randomized controlled trials (RCTs) on psychological interventions for reducing negative emotions among college students during the COVID-19 epidemic were searched in databases Web of Science, PubMed, Cochrane Library, Embase, Scopus, PsychInfo, CNKI, WanFang, and VIP. We utilized Cochrane risk of bias assessment technique to assess the quality of included RCTs. The data were analyzed using RevMan 5.4. Eight RCTs were finally included involving 1,496 participants. According to the meta-analysis results, psychotherapies could significantly alleviate anxiety, depression, and stress symptoms among university students, [MD = -0.98, 95%CI (-1.53, -0.43), p<0.001] [SMD = -0.47, 95%CI (-0.77, -0.16), p = 0.003] [MD = -1.53, 95%CI (-2.23, -0.83), p <0.001]. The difference in attenuation of somatization symptoms between the two groups was not statistically significant [SMD = -0.42, 95%CI (-1.41, 0.56), p = 0.40]. Psychotherapy might effectively alleviate anxiety, depression, and stress in university students caused by the COVID-19 pandemic. It could be considered the preferred strategy for reducing negative emotions and improving the quality of life of university students.
Subject(s)
COVID-19 , Mindfulness , Humans , Psychosocial Intervention , Depression/epidemiology , Depression/therapy , Depression/diagnosis , Universities , COVID-19/epidemiology , Anxiety/epidemiology , Anxiety/therapy , Anxiety/diagnosis , Emotions , StudentsABSTRACT
The adequate vaccination is a promising solution to mitigate the enormous socio-economic costs of the ongoing COVID-19 pandemic and allow us to return to normal pre-pandemic activity patterns. However, the vaccine supply shortage will be inevitable during the early stage of the vaccine rollout. Public health authorities face a crucial challenge in allocating scarce vaccines to maximize the benefits of vaccination. In this paper, we study a multi-period two-dose vaccine allocation problem when the vaccine supply is highly limited. To address this problem, we constructed a novel age-structured compartmental model to capture COVID-19 transmission and formulated as a nonlinear programming (NLP) model to minimize the total number of deaths in the population. In the NLP model, we explicitly take into account the two-dose vaccination procedure and several important epidemiologic features of COVID-19, such as pre-symptomatic and asymptomatic transmission, as well as group heterogeneity in susceptibility, symptom rates, severity, etc. We validated the applicability of the proposed model using a real case of the 2021 COVID-19 vaccination campaign in the Midlands of England. We conducted comparative studies to demonstrate the superiority of our method. Our numerical results show that prioritizing the allocation of vaccine resources to older age groups is a robust strategy to prevent more subsequent deaths. In addition, we show that releasing more vaccine doses for first-dose recipients could lead to a greater vaccination benefit than holding back second doses. We also find that it is necessary to maintain appropriate non-pharmaceutical interventions (NPIs) during the vaccination rollout, especially in low-resource settings. Furthermore, our analysis indicates that starting vaccination as soon as possible is able to markedly alleviate the epidemic impact when the vaccine resources are limited but are currently available. Our model provides an effective tool to assist policymakers in developing adaptive COVID-19 likewise vaccination strategies for better preparedness against future pandemic threats.
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COVID-19 , Vaccines , Humans , Aged , Pandemics , COVID-19 Vaccines , COVID-19/epidemiology , COVID-19/prevention & control , Resource AllocationABSTRACT
The aim of this research was to develop a quantitative method for clinicians to predict the probability of improved prognosis in patients with coronavirus disease 2019 (COVID-19). Data on 104 patients admitted to hospital with laboratory-confirmed COVID-19 infection from 10 January 2020 to 26 February 2020 were collected. Clinical information and laboratory findings were collected and compared between the outcomes of improved patients and non-improved patients. The least absolute shrinkage and selection operator (LASSO) logistics regression model and two-way stepwise strategy in the multivariate logistics regression model were used to select prognostic factors for predicting clinical outcomes in COVID-19 patients. The concordance index (C-index) was used to assess the discrimination of the model, and internal validation was performed through bootstrap resampling. A novel predictive nomogram was constructed by incorporating these features. Of the 104 patients included in the study (median age 55 years), 75 (72.1%) had improved short-term outcomes, while 29 (27.9%) showed no signs of improvement. There were numerous differences in clinical characteristics and laboratory findings between patients with improved outcomes and patients without improved outcomes. After a multi-step screening process, prognostic factors were selected and incorporated into the nomogram construction, including immunoglobulin A (IgA), C-reactive protein (CRP), creatine kinase (CK), acute physiology and chronic health evaluation II (APACHE II), and interaction between CK and APACHE II. The C-index of our model was 0.962 (95% confidence interval (CI), 0.931-0.993) and still reached a high value of 0.948 through bootstrapping validation. A predictive nomogram we further established showed close performance compared with the ideal model on the calibration plot and was clinically practical according to the decision curve and clinical impact curve. The nomogram we constructed is useful for clinicians to predict improved clinical outcome probability for each COVID-19 patient, which may facilitate personalized counselling and treatment.
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OBJECTIVES: To study the clinical features of children with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant infection. METHODS: A retrospective analysis was performed on the medical data of 201 children with coronavirus disease 2019 (COVID-19) who were hospitalized and diagnosed with SARS-CoV-2 Omicron variant infection in Quanzhou First Hospital from March 14 to April 7, 2022. Among the 201 children, there were 34 children with asymptomatic infection and 167 with symptomatic infection. The two groups were compared in terms of clinical features, results of experimental examinations, and outcome. RESULTS: Of all the 201 children, 161 (80.1%) had a history of exposure to COVID-19 patients and 132 (65.7%) had a history of COVID-19 vaccination. Among the 167 children with symptomatic infections, 151 had mild COVID-19 and 16 had common COVID-19, with no severe infection or death. Among the 101 children who underwent chest CT examination, 16 had ground glass changes and 20 had nodular or linear opacities. The mean time to nucleic acid clearance was (14±4) days for the 201 children with Omicron variant infection, and the symptomatic infection group had a significantly longer time than the asymptomatic infection group [(15±4) days vs (11±4) days, P<0.05]. The group vaccinated with one or two doses of COVID-19 vaccine had a significantly higher positive rate of IgG than the group without vaccination (P<0.05). The proportions of children with increased blood lymphocyte count in the symptomatic infection group was significantly lower than that in the asymptomatic infection group (P<0.05). Compared with the asymptomatic infection group, the symptomatic infection group had significantly higher proportions of children with increased interleukin-6, increased fibrinogen, and increased D-dimer (P<0.05). CONCLUSIONS: Most of the children with Omicron variant infection have clinical symptoms, which are generally mild. The children with symptomatic infection are often accompanied by decreased or normal blood lymphocyte count and increased levels of interleukin-6, fibrinogen, and D-dimer, with a relatively long time to nucleic acid clearance. Some of them had ground glass changes on chest CT.
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COVID-19 , Nucleic Acids , Child , Humans , Asymptomatic Infections , COVID-19/diagnosis , COVID-19/immunology , COVID-19/virology , COVID-19 Vaccines , Fibrinogen , Interleukin-6 , Retrospective Studies , SARS-CoV-2ABSTRACT
BACKGROUND: The impact of coronavirus diseases 2019 (COVID-19) related non-pharmaceutical interventions (NPIs) on influenza activity in the presence of other known seasonal driving factors is unclear, especially at the municipal scale. This study aimed to assess the impact of NPIs on outpatient influenza-like illness (ILI) consultations in Beijing and the Hong Kong Special Administrative Region (SAR) of China. METHODS: We descriptively analyzed the temporal characteristics of the weekly ILI counts, nine NPI indicators, mean temperature, relative humidity, and absolute humidity from 2011 to 2021. Generalized additive models (GAM) using data in 2011-2019 were established to predict the weekly ILI counts under a counterfactual scenario of no COVID-19 interventions in Beijing and the Hong Kong SAR in 2020-2021, respectively. GAM models were further built to evaluate the potential impact of each individual or combined NPIs on weekly ILI counts in the presence of other seasonal driving factors in the above settings in 2020-2021. RESULTS: The weekly ILI counts in Beijing and the Hong Kong SAR fluctuated across years and months in 2011-2019, with an obvious winter-spring seasonality in Beijing. During the 2020-2021 season, the observed weekly ILI counts in both Beijing and the Hong Kong SAR were much lower than those of the past 9 flu seasons, with a 47.5% [95% confidence interval (CI): 42.3%, 52.2%) and 60.0% (95% CI: 58.6%, 61.1%) reduction, respectively. The observed numbers for these two cities also accounted for only 40.2% (95% CI: 35.4%, 45.3%) and 58.0% (95% CI: 54.1%, 61.5%) of the GAM model estimates in the absence of COVID-19 NPIs, respectively. Our study revealed that, "Cancelling public events" and "Restrictions on internal travel" measures played an important role in the reduction of ILI in Beijing, while the "restrictions on international travel" was statistically most associated with ILI reductions in the Hong Kong SAR. CONCLUSIONS: Our study suggests that COVID-19 NPIs had been reducing outpatient ILI consultations in the presence of other seasonal driving factors in Beijing and the Hong Kong SAR from 2020 to 2021. In cities with varying local circumstances, some NPIs with appropriate stringency may be tailored to reduce the burden of ILI caused by severe influenza strains or other respiratory infections in future.
Subject(s)
COVID-19 , Influenza, Human , Humans , Hong Kong/epidemiology , Influenza, Human/epidemiology , Influenza, Human/prevention & control , COVID-19/prevention & control , COVID-19/complications , Beijing , China/epidemiology , SeasonsABSTRACT
The spike protein of SARS-CoV-2 has been a promising target for developing vaccines and therapeutics due to its crucial role in the viral entry process. Previously reported cryogenic electron microscopy (cryo-EM) structures have revealed that free fatty acids (FFA) bind with SARS-CoV-2 spike protein, stabilizing its closed conformation and reducing its interaction with the host cell target in vitro. Inspired by these, we utilized a structure-based virtual screening approach against the conserved FFA-binding pocket to identify small molecule modulators of SARS-CoV-2 spike protein, which helped us identify six hits with micromolar binding affinities. Further evaluation of their commercially available and synthesized analogs enabled us to discover a series of compounds with better binding affinities and solubilities. Notably, our identified compounds exhibited similar binding affinities against the spike proteins of the prototypic SARS-CoV-2 and a currently circulating Omicron BA.4 variant. Furthermore, the cryo-EM structure of the compound SPC-14 bound spike revealed that SPC-14 could shift the conformational equilibrium of the spike protein toward the closed conformation, which is human ACE2 (hACE2) inaccessible. Our identified small molecule modulators targeting the conserved FFA-binding pocket could serve as the starting point for the future development of broad-spectrum COVID-19 intervention treatments.
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The number of coronavirus disease 2019 (COVID-19) cases continues to surge, overwhelming healthcare systems and causing excess mortality in many countries. Testing of infectious populations remains a key strategy to contain the COVID-19 outbreak, delay the exponential spread of the disease, and flatten the epidemic curve. Using the Omicron variant outbreak as a background, this study aimed to evaluate the effectiveness of testing strategies with different test combinations and frequencies, analyze the factors associated with testing effectiveness, and optimize testing strategies based on these influencing factors. We developed a stochastic, agent-based, discrete-time susceptible-latent-infectious-recovered model simulating a community to estimate the association between three levels of testing strategies and COVID-19 transmission. Antigen testing and its combination strategies were more efficient than polymerase chain reaction (PCR)-related strategies. Antigen testing also showed better performance in reducing the demand for hospital beds and intensive care unit beds. The delay in the turnaround time of test results had a more significant impact on the efficiency of the testing strategy compared to the detection limit of viral load and detection-related contacts. The main advantage of antigen testing strategies is the short turnaround time, which is also a critical factor to be optimized to improve PCR strategies. After modifying the turnaround time, the strategies with less frequent testing were comparable to daily testing. The choice of testing strategy requires consideration of containment goals, test efficacy, community prevalence, and economic factors. This study provides evidence for the selection and optimization of testing strategies in the post-pandemic era and provides guidance for optimizing healthcare resources.
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Aerosols are an important route for the transmission of antibiotic resistance genes (ARGs). Since the 2019 (COVID-19) pandemic, the large-scale use of disinfectants has effectively prevented the spread of environmental microorganisms, but studies regarding the antibiotic resistance of airborne bacteria remain limited. This study focused on four functional urban areas (commercial areas, educational areas, residential areas and wastewater treatment plant) to study the variations in ARG abundances, bacterial community structures and risks to human health during the COVID-19 pandemic in aerosol. The results indicated the abundance of ARGs during the COVID-19 period were up to approximately 13-fold greater than before the COVID-19 period. Large-scale disinfection resulted in a decrease in total bacterial abundance. However, chlorine-resistant bacteria tended to be survived. Among the four functional areas, the diversity and abundance of aerosol bacteria were highest in commercial aera. Antibiotic susceptibility assays suggested elevated resistance of isolated bacteria to several tested antibiotics due to disinfection exposure. The potential exposure risks of ARGs to human health were 2 times higher than before the COVID-19 pandemic, and respiratory intake was the main exposure route. The results highlighted the elevated antibiotic resistance of bacteria in aerosols that were exposed to disinfectants after the COVID-19 pandemic. This study provides theoretical guidance for the rational use of disinfectants and control of antimicrobial resistance.
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COVID-19 , Disinfectants , Humans , Pandemics , Genes, Bacterial , Respiratory Aerosols and Droplets , Drug Resistance, Microbial/genetics , Bacteria/genetics , Anti-Bacterial Agents/pharmacology , Disinfectants/pharmacologyABSTRACT
Meplazumab, a humanized CD147 antibody, has shown favourable safety and efficacy in our previous clinical studies. In DEFLECT (NCT04586153), 167 patients with severe COVID-19 were enroled and randomized to receive three dosages of meplazumab and a placebo. Meplazumab at 0.12 mg/kg, compared to the placebo group, showed clinical benefits in significantly reducing mortality by 83.6% (2.4% vs. 14.6%, p = 0.0150), increasing the proportion of patients alive and discharged without supplemental oxygen (82.9% vs. 70.7%, p = 0.0337) and increasing the proportion of patients who achieved sustained clinical improvement (41.5% vs. 31.7%). The response rate in the 0.2 mg/kg group was relatively increased by 16.0% compared with the placebo group (53.7% vs. 46.3%). Meplazumab also reduced the viral loads and multiple cytokine levels. Compare with the placebo group, the 0.3 mg/kg significantly increased the virus negative rate by 40.6% (p = 0.0363) and reduced IL-8 level (p = 0.0460); the 0.2 mg/kg increased the negative conversion rate by 36.9%, and reduced IL-4 (p = 0.0365) and IL-8 levels (p = 0.0484). In this study, the adverse events occurred at a comparable rate across the four groups, with no unexpected safety findings observed. In conclusion, meplazumab promoted COVID-19 convalescence and reduced mortality, viral load, and cytokine levels in severe COVID-19 population with good safety profile.
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COVID-19 , Humans , Adult , SARS-CoV-2 , Interleukin-8 , CytokinesABSTRACT
OBJECTIVE: To evaluate the safety of primary immunization using CoronaVac® among population aged 3 years and above in a large-scale use. METHOD: A multi-center open-label study was carried out in 11 provinces of China. Individuals aged 3 years and older who had no history of COVID-19 vaccination or had received only one dose of CoronaVac® were enrolled in this study. Adults and elderly with or without underlying medical conditions(UMCs) were also recruited. Eligible participants received one or two doses of CoronaVac® with an interval of 28 days. Demographic information, vaccination and the occurrence of adverse events were recorded by participants or guardians using data collection system designed for this study. All adverse events occurred within 6 months after the second dose of vaccination were collected. The incidence of adverse events that cannot be ruled out as being caused by the vaccine were calculated to assess the safety of CoronaVac®. This study is registered with ClinicalTrials. Gov (NCT04911790 and NCT04992208). RESULTS: A total of 162,691 participants have been included in this study and 89.50 % had finished primary immunization. Among adults and elderly, people with UMCs accounted for 25.85 %, with the top five disease being hypertension, diabetes, chronic gastritis, coronary heart disease(CHD) and kidney stone. The overall incidence of adverse reactions (ARs) within 6 months after the second vaccination was 2.70 %, with incidence for children and adolescents, adults, and elderly being 2.03 %, 3.46 %, and 1.90 %, respectively. Most ARs were mild (grade 1). Pain at the injection sites, fatigue, induration/swelling, and headache were the most common symptoms, occurring in 1.64 %, 0.46 %, 0.31 % and 0.24 %, respectively. No serious adverse events related to vaccines were reported. No adverse events of special interest (AESIs) were identified. For children and adolescents, children aged 3-5 years had the highest incidence of ARs of 3.29 %. The incidence of ARs among those aged 18 years and older with and without UMCs were 2.81 % and 2.99 %, respectively, with no statistical significance between two groups(P = 0.089). And people with coronary heart disease had higher AR incidence compared to those with other UMCs, but the most common symptoms was pain at the injection site. CONCLUSION: CoronaVac® is safe in a large-scale use and shows well-tolerance for children and adolescents and people with underlying medical conditions. Further studies need to be conducted to explore the relation of ARs incidence to age or different kinds of UMCs.
Subject(s)
COVID-19 Vaccines , COVID-19 , Adult , Aged , Adolescent , Child , Humans , COVID-19 Vaccines/adverse effects , SARS-CoV-2 , COVID-19/prevention & control , Vaccination/adverse effects , Pain , Antibodies, ViralABSTRACT
Diabetic retinopathy (DR) is a common retinal vascular disease, which can cause severe visual impairment. It is of great clinical significance to use fundus images for intelligent diagnosis of DR. In this paper, an intelligent DR classification model of fundus images is proposed. This method can detect all the five stages of DR, including of no DR, mild, moderate, severe, and proliferative. This model is composed of two key modules. FEB, feature extraction block, is mainly used for feature extraction of fundus images, and GPB, grading prediction block, is used to classify the five stages of DR. The transformer in the FEB has more fine-grained attention that can pay more attention to retinal hemorrhage and exudate areas. The residual attention in the GPB can effectively capture different spatial regions occupied by different classes of objects. Comprehensive experiments on DDR datasets well demonstrate the superiority of our method, and compared with the benchmark method, our method has achieved competitive performance.
Subject(s)
Diabetes Mellitus , Diabetic Retinopathy , Humans , Diabetic Retinopathy/diagnostic imaging , Fundus Oculi , Image Interpretation, Computer-Assisted/methods , BenchmarkingABSTRACT
BACKGROUND: Herpes zoster increases the burden on the elderly in an aging society. Although an effective vaccine licensed by China Food and Drug Administration in 2019 was introduced into the market in June 2020, the willingness and influencing factors of herpes zoster vaccines in Chinese adults ≥ 50-years-old during coronavirus disease-2019 pandemic are yet to be elucidated. METHODS: An online questionnaire survey was conducted using a simple random sampling method in October 2021 for viewers of the broadcast program. A binary logistic regression and multiple response analysis were conducted for herpes zoster vaccine and vaccination willingness. Pareto's graphs were plotted to present the multiple-choice questions of influencing factors. RESULTS: A total of 3838 eligible participants were included in this study. Among them, 43.02% intended to be vaccinated, including 10.34% self-reported about receiving at least one shot of shingles vaccine, 30.22% declined, and 26.76% were hesitant. This population comprised a large proportion of middle-aged and older people (≥ 50-years-old) who have not experienced an episode of herpes zoster (54.98%) or are unaware of the virus (33.22%). The strongest determinants of vaccine hesitancy among older people were education background of Master's degree or above compared to senior high or equivalent and below, personal monthly income < 3000 RMB compared to 3000-5999 RMB, and living in a rural area. CONCLUSIONS: The willingness to get shingles vaccines can be improved further. Professional education and credible recommendation might prompt the elderly to improve their willingness and reassure them of the safety and efficacy of the vaccine. Also, accessibility and affordability should also be improved in the future.