The course of the UK COVID 19 pandemic; no measurable impact of new variants
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Abstract
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 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 log 10 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 R 2 0f 0.833 for prediction of log 10 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 a newer viral genotype.
Article activity feed
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SciScore for 10.1101/2021.03.16.21253534: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Weaknesses of this study include use of temperature from the capital city which may vary widely across regions and the limitations of recorded Sars-CoV-2 data. While the use of deaths as the primary endpoint is likely to be the most accurate measure for a large population, it does increase the time between observed parameters and …
SciScore for 10.1101/2021.03.16.21253534: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Weaknesses of this study include use of temperature from the capital city which may vary widely across regions and the limitations of recorded Sars-CoV-2 data. While the use of deaths as the primary endpoint is likely to be the most accurate measure for a large population, it does increase the time between observed parameters and outcomes. The infection fatality rate is also assumed to be static, but is likely to decrease in response to improved treatment and novel therapies, such as the use of Dexamethasone.[20] Calculations using the viral reproductive rate are compound in nature, and a small variation in the number of infections at one point in time will have a large bearing on the number of infections at a later date. However, we believe any predictive model should centre on prediction of the viral reproductive rate, which though widely variable, is predicted by the variables evaluated here, in particular the degree of lockdown. As the focus of investigation turns more to emergent variants, this model could benefit the understanding of their effects in the context of the pandemic as a whole.
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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