Impact of public health and social measures on contact dynamics during a SARS-CoV-2 Omicron variant outbreak in Quanzhou, China, March to April 2022.

OBJECTIVES: To evaluate the impact of early implementation of public health and social measures (PHSMs) on contact rates over time and explore contact behavior of asymptomatic versus symptomatic cases. METHODS: We used the largest contact tracing data in China thus far to estimate the mean contacts over time by age groups and contact settings. We used bootstrap with replacement to quantify the uncertainty of contact matrixes. The Pearson correlation was performed to demonstrate the number of contacts over time in relation to the evolution of restrictions. In addition, we analyzed the index cases with a high number of contacts and index cases that produced a high number of secondary cases. RESULTS: Rapidly adapted PHSMs can reduce the mean contact rates in public places while increasing the mean contact rates within households. The mean contact rates were 11.81 (95% confidence interval, 11.61-12.01) for asymptomatic (at the time of investigation) cases and 6.70 (95% confidence interval, 6.54-6.87) for symptomatic cases. The percentage of asymptomatic cases (at the time of investigation) meeting >50 close contacts make up more than 65% of the overall cases. The percentage of asymptomatic cases producing >10 secondary cases account for more than 80% of the overall cases. CONCLUSION: PHSMs may increase the contacts within the household, necessitating the need for pertinent prevention strategies at home. Asymptomatic cases can contribute significantly to Omicron transmission. By making asymptomatic people aware that they are already contagious, hence limiting their social contacts, it is possible to lower the transmission risk.

Simulating potential outbreaks of Delta and Omicron variants based on contact-tracing data: A modelling study in Fujian Province, China.

Although studies have compared the relative severity of Omicron and Delta variants by assessing the relative risks, there are still gaps in the knowledge of the potential COVID-19 burden these variations may cause. And the contact patterns in Fujian Province, China, have not been described. We identified 8969 transmission pairs in Fujian, China, by analyzing a contact-tracing database that recorded a SARS-CoV-2 outbreak in September 2021. We estimated the waning vaccine effectiveness against Delta variant infection, contact patterns, and epidemiology distributions, then simulated potential outbreaks of Delta and Omicron variants using a multi-group mathematical model. For instance, in the contact setting without stringent lockdowns, we estimated that in a potential Omicron wave, only 4.7% of infections would occur in Fujian Province among individuals aged >60 years. In comparison, 58.75% of the death toll would occur in unvaccinated individuals aged >60 years. Compared with no strict lockdowns, combining school or factory closure alone reduced cumulative deaths of Delta and Omicron by 28.5% and 6.1%, respectively. In conclusion, this study validates the need for continuous mass immunization, especially among elderly aged over 60 years old. And it confirms that the effect of lockdowns alone in reducing infections or deaths is minimal. However, these measurements will still contribute to lowering peak daily incidence and delaying the epidemic, easing the healthcare system's burden.

Nomogram Model for Prediction of SARS-CoV-2 Breakthrough Infection in Fujian: A Case-Control Real-World Study.

SARS-CoV-2 breakthrough infections have been reported because of the reduced efficacy of vaccines against the emerging variants globally. However, an accurate model to predict SARS-CoV-2 breakthrough infection is still lacking. In this retrospective study, 6,189 vaccinated individuals, consisting of SARS-CoV-2 test-positive cases (n = 219) and test-negative controls (n = 5970) during the outbreak of the Delta variant in September 2021 in Xiamen and Putian cities, Fujian province of China, were included. The vaccinated individuals were randomly split into a training (70%) cohort and a validation (30%) cohort. In the training cohort, a visualized nomogram was built based on the stepwise multivariate logistic regression. The area under the curve (AUC) of the nomogram in the training and validation cohorts was 0.819 (95% CI, 0.780-0.858) and 0.838 (95% CI, 0.778-0.897). The calibration curves for the probability of SARS-CoV-2 breakthrough infection showed optimal agreement between prediction by nomogram and actual observation. Decision curves indicated that nomogram conferred high clinical net benefit. In conclusion, a nomogram model for predicting SARS-CoV-2 breakthrough infection based on the real-world setting was successfully constructed, which will be helpful in the management of SARS-CoV-2 breakthrough infection.

Modelling the Emerging COVID-19 Epidemic and Estimating Intervention Effectiveness - Taiwan, China, 2021.

INTRODUCTION: The coronavirus disease 2019 (COVID-19) pandemic recently affected Taiwan, China. This study aimed to calculate the transmissibility of COVID-19 to predict trends and evaluate the effects of interventions. METHODS: The data of reported COVID-19 cases was collected from April 20 to May 26, 2021, which included daily reported data (Scenario I) and reported data after adjustment (Scenario II). A susceptible-exposed-symptomatic-asymptomatic-recovered model was developed to fit the data. The effective reproductive number (Reff ) was used to estimate the transmissibility of COVID-19. RESULTS: A total of 4,854 cases were collected for the modelling. In Scenario I, the intervention has already taken some effects from May 17 to May 26 (the Reff reduced to 2.1). When the Reff was set as 0.1, the epidemic was projected to end on July 4, and a total of 1,997 cases and 855 asymptomatic individuals would have been reported. In Scenario II, the interventions were projected as having been effective from May 24 to May 26 (the Reff reduced to 0.4). When the Reff was set as 0.1, the epidemic was projected to end on July 1, and a total of 1,482 cases and 635 asymptomatic individuals would have been reported. CONCLUSION: The epidemic of COVID-19 was projected to end after at least one month, even if the most effective interventions were applied in Taiwan, China. Although there were some positive effects of intervention in Taiwan, China.

Identification of COVID-19 Clinical Phenotypes by Principal Component Analysis-Based Cluster Analysis.

Background: COVID-19 has been quickly spreading, making it a serious public health threat. It is important to identify phenotypes to predict the severity of disease and design an individualized treatment. Methods: We collected data from 213 COVID-19 patients in Wuhan Pulmonary Hospital from January 1 to March 30, 2020. Principal component analysis (PCA) and cluster analysis were used to classify patients. Results: We identified three distinct subgroups of COVID-19. Cluster 1 was the largest group (52.6%) and characterized by oldest age, lowest cellular immune function, and albumin levels. 38.5% of subjects were grouped into Cluster 2. Most of the lab results in Cluster 2 fell between those of Clusters 1 and 3. Cluster 3 was the smallest cluster (8.9%), characterized by youngest age and highest cellular immune function. The incidence of respiratory failure, acute respiratory distress syndrome (ARDS), heart failure, and usage of non-invasive mechanical ventilation in Cluster 1 was significantly higher than others (P < 0.05). Cluster 1 had the highest death rate of 30.4% (P = 0.005). Although there were significant differences in age between Clusters 2 and 3 (P < 0.001), we found that there was no difference in demand for medical resources. Conclusions: We identified three distinct clusters of the COVID-19 patients. The results show that age alone could not be used to assess a patient's condition. Specifically, management of albumin, and immune function are important in reducing the severity of disease.

Dynamic changes of D-dimer and neutrophil-lymphocyte count ratio as prognostic biomarkers in COVID-19.

BACKGROUND: Since December 2019, the outbreak of COVID-19 caused a large number of hospital admissions in China. Many patients with COVID-19 have symptoms of acute respiratory distress syndrome, even are in danger of death. This is the first study to evaluate dynamic changes of D-Dimer and Neutrophil-Lymphocyte Count Ratio (NLR) as a prognostic utility in patients with COVID-19 for clinical use. METHODS: In a retrospective study, we collected data from 349 hospitalized patients who diagnosed as the infection of the COVID-19 in Wuhan Pulmonary Hospital. We used ROC curves and Cox regression analysis to explore critical value (optimal cut-off point associated with Youden index) and prognostic role of dynamic changes of D-Dimer and NLR. RESULTS: Three hundred forty-nine participants were enrolled in this study and the mortality rate of the patients with laboratory diagnosed COVID-19 was 14.9%. The initial and peak value of D-Dimer and NLR in deceased patients were higher statistically compared with survivors (P < 0.001). There was a more significant upward trend of D-Dimer and NLR during hospitalization in the deceased patients, initial D-Dimer and NLR were lower than the peak tests (MD) -25.23, 95% CI: - 31.81- -18.64, P < 0.001; (MD) -43.73, 95% CI:-59.28- -31.17, P < 0.001. The test showed a stronger correlation between hospitalization days, PCT and peak D-Dimer than initial D-Dimer. The areas under the ROC curves of peak D-Dimer and peak NLR tests were higher than the initial tests (0.94(95%CI: 0.90-0.98) vs. 0.80 (95% CI: 0.73-0.87); 0.93 (95%CI:0.90-0.96) vs. 0.86 (95%CI:0.82-0.91). The critical value of initial D-Dimer, peak D-Dimer, initial NLR and peak NLR was 0.73 mg/L, 3.78 mg/L,7.13 and 14.31 respectively. 35 (10.03%) patients were intubated. In the intubated patients, initial and peak D-Dimer and NLR were much higher than non-intubated patients (P < 0.001). The critical value of initial D-Dimer, peak D-Dimer, initial NLR and peak NLR in prognosticate of intubation was 0.73 mg/L, 12.75 mg/L,7.28 and 27.55. The multivariable Cox regression analysis showed that age (HR 1.04, 95% CI 1.00-1.07, P = 0.01), the peak D-Dimer (HR 1.03, 95% CI 1.01-1.04, P < 0.001) were prognostic factors for COVID-19 patients' death. CONCLUSIONS: To dynamically observe the ratio of D-Dimer and NLR was more valuable during the prognosis of COVID-19. The rising trend in D-Dimer and NLR, or the test results higher than the critical values may indicate a risk of death for participants with COVID-19.

The potential role of TNFα in 2019 novel coronavirus pneumonia.

The outbreak of 2019 novel coronavirus has spread rapidly in multiple countries. We report the first case of 2019-nCoV infection in a patient with Ankylosing Spondylitis (AS), who was a biological agent (anti-TNFα) user in Wenzhou, China, and describe the clinical course and management of the case.