ABSTRACT
BACKGROUND AND AIMS SARS-CoV-2 antibody titers after two doses of vaccination decrease over time. Hemodialysis patients are especially vulnerable to COVID-19 as they are immunocompromised, putting them at higher risk of infection and poorer response to vaccines. Therefore, administrating the third dose (‘booster’) in these patients is key to reduce COVID-19 infections and prevent severe illness. Dialysis patients were among the first group of patients who received booster vaccinations. To study the humoral response to the third injection in this group, we collected serum from 33 patients on hemodialysis and measured neutralizing antibody titers against SARS-CoV-2 before and after their booster doses. METHOD Patients were recruited from a dialysis center in New York City, NY from June to September 2021. Data on COVID-19 vaccination and demographics were collected upon enrollment. Blood samples were taken after enrollment. SARS-CoV-2 neutralization antibodies were assayed using the GenScript SARS-CoV-2 Surrogate Virus Neutralization Test Kit (Cat#L00847-A). Corresponding neutralizing antibody titers are presented as Unit/mL (U/mL). RESULTS A total of 33 in-center hemodialysis patients who had received three doses of vaccination were studied. Patients had a mean age of 61 years, 23 (70%) were male. Out of these, 31 (94%) patients received three doses of mRNA-1273 (Moderna), and two patients received the BNT162b2 (Pfizer BioNTech) vaccine. A total of 138 serum samples were analyzed (ranging from 156 days before to 85 days after the booster). Figure 1 shows the antibody titer distribution of all samples in these 33 patients. Each color indicates an individual patient. Each patient has up to 12 data points before and after the booster. The mean neutralizing antibody titers of all 48 data points pre-booster is 29.291 U/mL (range: 228–188.600). Seven days post-booster, the mean neutralizing antibody titer is 73.088 U/mL (range: 12.401–254.504). Mean titer is 169.826 U/mL (range: 17.830–375.046) at 14–28 days post-booster. After the peak time, we observe a decline of the titers. At 72–85 days, the mean titer is 72.179 (range: 33.702–204.382). We fitted a nonparametric mixed effects model with an adaptive spline and a random intercept for each subject to neutralizing antibody titers on the log10 scale. The estimate of the mean trajectory and its 95% confidence interval are shown in Fig. 2. The estimated peak time is 18.2 days with a 95% confidence interval (0–27.7). CONCLUSION Our results suggest that hemodialysis patients have a strong humoral response to booster vaccination. Neutralizing antibody titers peak at 18 days post-booster and wane to an average of 42% of peak value after 10–12 weeks.FIGURE 1: Time-course of neutralizing antibody titers before and after booster vaccination. The colors identify individual hemodialysis patients.FIGURE 2: A nonparametric mixed effects model with an adaptive spline and a random intercept for each subject to neutralizing antibody titers. The red line indicates the average titer, and the gray area indicates the 95% confidence interval. The circles are means across all data points.
ABSTRACT
Online learning has emerged as the “new norm” due to the COVID-19 crisis. Compared with institutions’ and teachers’ responses to online teaching, little is known about students’ perceived influence of online assessment practices. The present study explored the perceived effects of learning-oriented online assessment on L2 students’ feedback literacy and individual differences in feedback literacy development from an ecological perspective. We used multiple sources of data, including a survey on student feedback literacy, semi-structured interviews with two focal students, drafts produced by them and related teacher feedback, and supplementary data reflecting the online assessment practices in the course. Results demonstrated that the students held less favorable opinions of the online mode of learning in promoting feedback literacy. However, they perceived positively the development of feedback literacy in the aspects of appreciating feedback, developing judgements, and taking actions. Considerable variations were identified in the development of two focal students’ feedback literacy, especially in the aspects of managing affects and taking actions. The findings revealed the negative influence of misalignment between micro- and macro- factors on student feedback literacy and how such a misalignment interacted with learner factors to influence individual students’ feedback literacy when learning-oriented assessment (LOA) was implemented during COVID-19. The paper proposed a fine-grained model for developing student feedback literacy through learning-oriented online assessment. With a special focus on misalignment, the model provided insights into the interactional dynamics among learners, classroom, and larger contexts in using LOA to enhance student feedback literacy online. Relevant pedagogical implications for developing student feedback literacy within and beyond COVID-19 were discussed.
ABSTRACT
The COVID-19 pandemic poses a serious threat to global health, and one of the key epidemiological factors that shape the transmission of COVID-19 is its serial interval (SI). Although SI is commonly considered following a probability distribution at a population scale, slight discrepancies in SI across different transmission generations are observed from the aggregated statistics in recent studies. To explore the change in SI across transmission generations, we develop a likelihood-based statistical inference framework to examine and quantify the change in SI. The COVID-19 contact tracing surveillance data in Hong Kong are used for exemplification. We find that the individual SI of COVID-19 is likely to shrink with a rate of 0.72 per generation and 95%CI: (0.54, 0.96) as the transmission generation increases. We speculate that the shrinkage in SI is an outcome of competition among multiple candidate infectors within a cluster of cases. The shrinkage in SI may speed up the transmission process, and thus the nonpharmaceutical interventive strategies are crucially important to mitigate the COVID-19 epidemic.
Subject(s)
COVID-19ABSTRACT
Background: Deep throat saliva (DTS) and pooled nasopharyngeal swab and throat swab (NPSTS) are utilized for viral detection. DTS is challenging for children. Swabbing the respiratory mucosa requires trained personnel and may trigger sneezing and coughing, which generate droplets. A reliable, simple and safe sampling method applicable to a wide age range is required for community-based surveillance. Methods: We introduced nasal strip as an easy and low-risk collection method. Asymptomatic and symptomatic SARS-CoV-2 infected patients (n = 38) were recruited. Nasal epithelial lining fluid (NELF) (n = 43) strip paired with nasal swab (n = 13) were collected by a healthcare worker to compare with NPSTS (n = 21) or DTS (n =22) collected within 24 hours as reference. All samples were subjected to viral RNA quantitation by real-time PCR targeting the nucleoprotein gene. Results: Comparable Ct values were observed between paired nasal strip and nasal swab samples. The agreement between nasal strip samples and NPSTS was 94.44% and 100% for NPSTS positive and negative samples. Higher viral RNA concentration was detected in nasal strips than DTS samples. False-negative results were recorded in six DTS specimens, of which four were from children. Storage at room temperature up to 72 (n = 3) hours did not affect diagnostic yield of nasal strips. Conclusions: Nasal strip is a reliable and non-invasive sampling method for SARS-CoV-2 detection, and viral detection remains stable for at least 72 hours. It can be used as an alternative tool for community-based surveillance.
Subject(s)
Severe Acute Respiratory SyndromeABSTRACT
Background: Since the first case of coronavirus disease 2019 (COVID-19) was detected on February 14, 2020, the cumulative confirmations reached 15207 including 831 deaths by April 13, 2020. Methods: We analyzed the initial phase of the epidemic of COVID-19 in Africa between 1 March and 13 April 2020, by using the simple exponential growth model. Results: We estimated the exponential growth rate as 0.22 per day (95%CI: 0.20 – 0.24), and the basic reproduction number, R0, to be 2.37 (95%CI: 2.22-2.51) based on the assumption that the exponential growth starting from 1 March 2020. Conclusion: The initial growth of COVID-19 cases in Africa was rapid and showed large variations across countries. Our estimates should be useful in preparedness planning. Trial registration: NA
Subject(s)
COVID-19 , Growth DisordersABSTRACT
BackgroundMonitoring the time-varying reproduction number (Rt) of the disease is useful in determining whether there is sustained transmission in a population. In this study, we examined Rt of COVID-19 and compared its transmissibility between different intervention periods in Hangzhou and Shenzhen. MethodsDaily aggregated counts of confirmed imported and local cases between January 1, 2020 and March 13, 2020 were analysed. A likelihood function was constructed to estimate Rt, accounting for imported cases. ResultsAlthough Hangzhou had fewer number of cases than Shenzhen, Shenzhen had higher proportion of imported cases than Hangzhou (83% vs 29%). Since the epidemic of COVID-19 in Shenzhen was dominated by imported cases, Rt was kept below unity through time. On the contrary, Rt was greater than unity in Hangzhou from 16 January to 7 February due to the surge in local cases. Credits to the Wuhan lockdown and outbreak response measures following the local lockdown, Rt decreased steadily and dropped below unity in mid-February. ConclusionThe lockdown measures and local outbreak responses helped reduce the potential of local transmission in Hangzhou and Shenzhen. Meanwhile, cities with similar epidemic trend could have different transmission dynamics given the variation in imported cases.
Subject(s)
COVID-19ABSTRACT
Backgrounds: The emerging virus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused a large outbreak of novel coronavirus disease (COVID-19) in Wuhan, China since December 2019. Based on the publicly available surveillance data, we identified 21 transmission chains in Hong Kong and estimated the serial interval (SI) of COVID-19. Methods: Index cases were identified and reported after symptoms onset, and contact tracing was conducted to collect the data of the associated secondary cases. An interval censored likelihood framework is adopted to fit a Gamma distribution function to govern the SI of COVID-19. Findings: Assuming a Gamma distributed model, we estimated the mean of SI at 4.4 days (95%CI: 2.9-6.7) and SD of SI at 3.0 days (95%CI: 1.8-5.8) by using the information of all 21 transmission chains in Hong Kong. Conclusion: The SI of COVID-19 may be shorter than the preliminary estimates in previous works. Given the likelihood that SI could be shorter than the incubation period, pre-symptomatic transmission may occur, and extra efforts on timely contact tracing and quarantine are recommended in combating the COVID-19 outbreak.