The dynamics of the Omicron wave in England

In a recent study posted to the medRxiv* preprint server, researchers investigated the transmission dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant of concern (VOC) Omicron in England using polymerase chain reaction (PCR) testing and genomic sequencing data from the REal-time Assessment of Community Transmission-1 (REACT-1) study.

Study: The new normal? Dynamics and scale of the SARS-CoV-2 variant Omicron epidemic in England. Image Credit: DOERS/Shutterstock
Study: The new normal? Dynamics and scale of the SARS-CoV-2 variant Omicron epidemic in England. Image Credit: DOERS/Shutterstock

Background

The Omicron VOC rapidly disseminated in over 170 countries by January 2022. However, due to saturation in coronavirus disease 2019 (COVID-19) testing capacity and variable test-seeking behavior of the population of each country, estimates of Omicron’s transmission dynamics were biased in several countries. Thus, the magnitude of the Omicron wave is not yet apparent in most countries.

During the REACT-1 study, investigators tested random samples of the population of England approximately every month since May 2020. Being a representative community survey, REACT-1 accurately measured the prevalence of SARS-CoV-2 with fewer tests and avoided biases.

About the study

In the present study, researchers used the REACT-1 study data to describe the dynamics of the Omicron wave in England and the role of Omicron sub-lineages BA.1, BA.2, BA.1.1 in the overall dynamics.

The researchers used swab-positivity and genomic sequencing data from the REACT-1 rounds 14 to 18, starting September 9th, 2021, ending March 1st, 2022, and rounds 16-18. The REACT-1 rounds 16, 17, and 18 were conducted between November 23rd and December 14th, 2021, January 5th and January 20th, 2022, and February 8th and March 1st, 2022, respectively.

The team performed genomic sequencing on swab samples with a nucleocapsid (N)-gene cycle threshold (Ct) value of less than 34, given the sample volume was sufficient. Next, they used a machine-learning-based algorithm PangoLEARN, for lineage designation.

Further, to estimate the prevalence of Omicron and Delta infections over time, the team used a mixed-effects Bayesian P-spline model. Assuming a binomial likelihood, they fitted the proportion of the total prevalence attributed to Omicron to the daily number of Omicron lineages vs. the total number of samples with known lineage.

Additionally, the researchers fitted an analogous model to rounds 17 and 18, assuming that >99% prevalence was attributable to Omicron. The modeled prevalence time series estimated the instantaneous growth rate of Omicron, Delta and their difference over time.

Study findings

Omicron has an approximately 28% shorter generation time compared to Delta. Therefore, its growth advantage was not constant over time and ranged from 0.37 on December 3rd, 2021 (when Omicron was first detected in the REACT-1 study), declining steadily to 0.11 on January 8th, 2022.

Similar to the decrease in the growth advantage for the Alpha VOC in England in 2020, the decline in the growth rate of Omicron reflected that it initially achieved higher average transmission rates among younger and socially active groups than in the general population.

As Omicron prevalence increased,  the prevalence of Delta rapidly dropped below 0.1% on January 3rd, 2022, across all regions and age groups, which halved its time-varying reproduction number (Rt) from 1.0 to 0.5 in three weeks (9th to 30th December 2021).

On December 30th, 2021, Omicron’s prevalence reached its peak, and at this time, the proportion of BA.1, BA.1.1, and  BA.2-infected cases were 84.6%, 15.2%, and 0.2%, respectively. However, by March 1st, 2022, the share of BA.1 cases declined to 9.6%, whereas that of BA.1.1 and BA.2 cases spiked to 21.6% and 68.7%, respectively.

In February 2022, the prevalence of BA.2 steadily increased in England, whereas that of non-BA.2 cases decreased. Accordingly, the estimated Rt for BA.2 was greater than the Rt for non-BA.2 Omicron on March 1st, 2022 (1.17 vs. 0.77), signifying a multiplicative advantage of approximately 1.5 for the BA.2 sub-variant.

The difference in dynamics between Omicron sub-lineages rationalizes the trends in Omicron prevalence in England, with a greater proportion of BA.2-infected cases. Moreover, the BA.2 cases exhibited the most common COVID-19 symptoms, including loss of smell or taste, fever, and persistent cough, compared to those infected with BA.1. Furthermore, the emergence of BA.2 prolonged the Omicron wave in England; accordingly, during the first three weeks of March 2022, all regions of England witnessed a resurgence in cases and hospitalizations across age groups.

The age-group-specific Rt showed a high degree of concomitance. Omicron prevalence peaked at 10.74% in the age group of 5 to 17 years on January 28, 2022, much higher than the maximum prevalence of 7.65% in the age group of 18 to 34 years attained four weeks earlier on January 1st, 2022. The maximum Omicron prevalence was 3.67%, at its lowest prevalence was observed in those aged 55 years and over initially. However, it increased at the end of the study period and attained an Rt of 1.14 on March 1, 2022.

Conclusions

The emergence of Omicron and its sublineages suggested that the evolutionary dynamics of SARS-CoV-2 will be dominated by immune evasion. In this new public health paradigm, continued surveillance, booster vaccinations, and vaccine equity worldwide will be crucial in mitigating the harmful effects of the COVID-19 pandemic and reducing the rate of emergence of the deadly SARS-CoV-2 VOCs.

*Important notice

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.

Journal reference:
Neha Mathur

Written by

Neha Mathur

Neha is a digital marketing professional based in Gurugram, India. She has a Master’s degree from the University of Rajasthan with a specialization in Biotechnology in 2008. She has experience in pre-clinical research as part of her research project in The Department of Toxicology at the prestigious Central Drug Research Institute (CDRI), Lucknow, India. She also holds a certification in C++ programming.

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