A study conducted by researchers at the University of Maryland, USA, has highlighted the importance of coronavirus disease 2019 (COVID-19) vaccination in reducing the frequency of mutations in the delta variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The study also presents an evolutionary algorithm that can accurately predict new COVID-19 outbreaks. A detailed description of the study is currently available on the medRxiv* preprint server.
Currently, the best possible way to end the COVID-19 pandemic is mass vaccination. However, public distrust and hesitancy to accept COVID-19 vaccines have added an extra level of complicacy in combating the global spread of SARS-CoV-2. Despite proven efficacy against SARS-CoV-2 infections, a large proportion of the global population is still uncertain about the risk-benefit ratio of COVID-19 vaccines.
In addition to increasing the risk of viral transmission, under-vaccination may affect the rate of viral mutations. On average, the mutation rate of SARS-CoV-2 is 7.23 mutations per viral sample. Mutations that emerged under positive selection pressure, such as vaccine/therapy-induced immunity, are the main driving force of viral evolution. Thus, novel viral variants evolving during the pandemic are likely to develop resistance against vaccines and therapeutics.
In the current study, the scientists have explored the association between vaccine coverage rate and mutation frequency of the SARS-CoV-2 delta variant (B.1.617.2).
For the analysis, they have collected complete genome sequences of SARS-CoV-2 from the Global Initiative on Sharing All Influenza Data (GISAID) database. In total, viral sequences from 20 countries have been included in the analysis.
(A) Correlation between full vaccinated rate  and mutation frequency (Mf) from June 20 to July 3 2021 in 20 countries: Australia (AUS), France (FRA), Germany (GER), Indonesia (IDA), India (IND), Ireland (IRL), Israel (ISR), Italy (ITA), Japan (JPN), Mexico (MEX), Netherland (NED), Norway (NOR), Portugal (POR), Singapore (SGP), Spain (ESP), Switzerland (SUI), Sweden (SWE), Turkey (TUR), United States (USA), and UK. Logarithmic regression (solid) line was draw based on 16 countries (pink dots) with a calculated 95% confidence interval (dashed lines). Japan, Switzerland, USA, and Australia are labeled in different colors as outliers. (B and C) Chronology of nucleotide diversity (π) (B) and Tajima D’ value (C) of SARS CoV-2 delta variants in UK (N=27,344, blue), Indian (N=4,451, red), and Australian (N=305, green). Data were plotted every two weeks, and the data only represent the effective population size with more than 3 high quality sequences. The arrows label the epidemiological events of COVID-19 delta variants announced by the World Health Organization (WHO). WHO classified the delta variant as a global variant of interest (VOI) on 4 April 2021, and variants of concern (VOC) on 11 May 2021 . The dashed line in (C) labels the cut-off threshold -2.50 in Tajima D’ test.
The analysis revealed that with an increase in vaccination rate, there is a reduction in the frequency of viral mutations. This inverse correlation between vaccination rate and mutation frequency was observed in 16 out of 20 countries.
As an exception, Australia exhibited a very low mutation frequency with a vaccination rate of around 10%. In contrast, a high mutation frequency was observed in the United States, Japan, and Switzerland, despite higher vaccination rates than in Australia. These observations indicate more successful implementation of control measures in Australia than in these countries.
Prediction of new outbreaks
To determine whether vaccine-induced immunity acts as a positive selection pressure to initiate viral evolution, the scientists analyzed genome sequences of the delta variant in the UK, India, and Australia. They performed the Tajima D test to determine whether mutations emerge neutrally or via non-random processes, including directional selection or demographic expansion. Tajima’s D is a statistical test used in population genetics to compare pair-wise genetic diversity and total polymorphism to deduce selection and demographic events.
The findings of the Tajima D test revealed that the delta variants in the UK emerged with rapid clonal expansion. In contrast, the variants in India and Australia mainly emerged with singleton mutations (single nucleotide variants). The values obtained from the Tajima D test were between -2.68 and -2.84 for all the delta variants. These D’ values were equivalent to that calculated from the sequences of B.1.1.7 variant in the UK during the study period. Negative D’ values observed in both the UK and Indian variants throughout the study period indicate more substantial demographic expansion or positive selection.
With further analysis, the scientists observed that new COVID-19 outbreaks occurred in the UK and India 1 – 3 weeks after the reduction of D’ values below -2.50. Based on these findings, they proposed that a D’ value of -2.50 could be used as a threshold to predict new outbreaks.
The study reveals that the frequency of viral mutations can be reduced by increasing the rate of full vaccination. In other words, countries with high vaccine coverage are less likely to experience new COVID-19 outbreaks. Thus, public hesitancy to COVID-19 vaccination could potentially lead to the emergence of more pathogenic viral variants and failure to achieve herd immunity.
As recommended by the scientists, mass vaccination, control measure implementation, and continuous genomic surveillance are the most vital strategies to combat the COVID-19 pandemic.
medRxiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.