Introduction In November 2020, a new SARS-COV-2 variant or the Kent variant emerged in the UK, and became the dominant UK SARS-COV-2 variant, demonstrating faster transmission than the original variant, which rapidly died out. However, it is unknown if this actually altered the overall course of the pandemic as genomic analysis was not common place at the outset and other factors such as the climate could alter the viral transmission rate over time. We aimed to test the hypothesis that the overall observed viral transmission was not altered by the emergence of the new variant, by testing a model generated earlier in the pandemic based on lockdown stringency, temperature and humidity. Methods From 1/1/20 to 4/2/21, the daily incidence of SARS-COV-2 deaths and the overall stringency of National Lockdown policy on each day was extracted from the Oxford University Government response tracker. The daily average temperature and humidity for London was extracted from Wunderground.com. The viral reproductive rate was calculated on a daily basis from the daily mortality data for each day. The correlation between log10 of viral reproductive rate and lockdown stringency and weather parameters were compared by Pearson correlation to determine the time lag associated with the greatest correlation. A multivariate model for the log10 of viral reproductive rate was constructed using lockdown stringency, temperature and humidity for the period 1/1/20 to 30/9/20. This model was extrapolated forward from 1/10/20 to 4/2/21 and the predicted viral reproductive rate, daily mortality and cumulative mortality were compared with official data. Results On multivariate linear regression, the optimal model had and R2 0f 0.833 for prediction of log10 viral reproductive rate 13 days later in the model construction period, with (coefficient, probability) lockdown stringency (-0.0109, p=0.0000), humidity (0.0038, p=0.0041) and temperature (-0.0035, p=0.0008). When extrapolated to the validation period (1/10/20 to 4/2/21), the model was highly correlated with daily (Pearson coefficient 0.88, p=0.0000) and cumulated SARS-COV-2 mortality (Pearson coefficient 0.99, p=0.0000). Conclusion The course of the SARS-COV-2 pandemic in the UK seems highly predicted by an earlier model based on the lockdown stringency, humidity and temperature and unaltered by the emergence of newer viral genotype.
The Coronavirus outbreak that began in December, 2019 became a worldwide pandemic by March, 2020. The education industry had to adapt to this new change in a speedy and most feasible way by shifting to virtual classes over the internet and other available resources. WhatsApp has turned out to be a boon in academic’s section where in all the teachers have their own subject-wise groups on the said application. This not only helps them to share the necessary information resources but also to have discussion regarding the subject matter. On the other side, this application allows easy access for the students to all the resources shared by the teacher, and they can share their assignments without much difficulty with their subject teachers. Therefore, it has become a primary tool for conducting teaching, learning and evaluation online. The researchers have tried to analyze impact of online classes and the efforts required by the students to learn online with the help of survey conducted with the sample size of 514. The survey questionnaire was grouped into 4 categories namely; Opinion on Information and Communications Technology (ICT) based Teaching, Changes in communication behavior due to ICT using WhatsApp, the main hindrance for integration of ICT (Information Communication Technology) in the teaching-learning process and disadvantages of using ICT for teaching and learning.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.