Modeling shows Omicron SARS-CoV-2 will place a heavy burden on UK healthcare

In a recent study published on the medRxiv * preprint server, the researchers are using data from the UK and a mathematical model to determine the impact of the Omicron variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on future risk of infection.

To study: Short-term projections based on the dynamics of early Omicron variants in England. Image Credit: CKA / Shutterstock.com

The Omicron SARS-CoV-2 variant

Since the start of the 2019 coronavirus disease (COVID-19) pandemic, several different variants of SARS-CoV-2 with improved immunological suitability have appeared around the world. On November 24, 2021, the SARS-CoV-2 Omicron variant (B.1.1.529) was first reported in South Africa. Shortly thereafter, the World Health Organization (WHO) designated Omicron as a variant of concern (VOC).

Coverage of tests in England capable of identifying the failure of the S-Gene (SGFT) target. Southwestern regions that have lower coverage are less likely to be able to quickly discriminate between the Omicron and Delta variants.

Currently, there is a massive increase in COVID-19 cases around the world following the emergence of Omicron. This is probably due to the increased transmissibility of this variant compared to other strains of SARS-CoV-2 due to the more than 30 mutations in its spike protein region (S). In addition to increasing its transmissibility, these mutations also help the Omicron variant escape the immunity acquired through existing vaccines and previous SARS-CoV-2 infections.

About the study

In the present study, researchers conducted sensitivity analysis and mathematical modeling using SARS-CoV-2 data from the UK to predict Omicron variant transmissibility, hospitalizations, death rate and l effect of non-pharmaceutical interventions in the future.

The first signs of Omicron transmission were analyzed using data from the UK Health Security Agency (UKHSA) case list. Subsequently, the UKHSA case list and reinfection data, as well as a mathematical model previously developed by the researchers, were used to simulate the dynamics of SARS-CoV-2 transmission in seven regions of the National. Health Service (NHS) of England. The authors included Omicron in their previous model along with the wild-type, Alpha, and Delta variants of SARS-CoV-2.

While the BA.1 and BA.3 Omicron sublines failed to amplify in the S gene target (SGTF), the BA.2 sublines did not display SGTF. The SARS-CoV-2 polymerase chain reaction (PCR) assay at UK Lighthouse laboratories using the TaqPath S gene target was used to track Omicron’s progress.

Model participants were divided into 21 age groups, with five-year age differences. The COVID-19 cases in the model were classified as the first infection in a household (F), subsequent infection by symptomatic and asymptomatic household members (SI, SA), and the first case subsequently detected in a household quarantined (QF, QS). The degree of vaccine protection against Omicron was determined in the vaccinees Pfizer, Moderna or AstraZeneca.

Study results

The results indicated that at the start of December 2021, the identified SGTF was highest in the North East of England and lowest in the South West of England, while the lab’s coverage was not was not consistent in London.

From November 1, 2021 to December 13, 2021, cases positive for the Delta variant S gene showed a slow decline, while Omicron SGTF cases showed a rapid increase. However, from early December 2021, there was a slight decrease in the growth rate of SGTF cases due to delays in reporting and variations in the age distribution of SARS-CoV-2 cases.

With the emergence of Omicron in early December 2021, there was an increased rate of reinfection in all age groups, especially adults 20 to 29 years old. After stratification by age group and region in England, a rapid increase in reinfection of young adults was observed in all regions, including those with low SGTF coverage. In contrast, the modeled infection showed an even distribution of Omicron cases among individuals aged 25 to 44 years.

Estimate of the daily percentage of reinfection cases for England, stratified by age group, showing substantial increases in December in many age groups above a previously relatively constant reinfection rate for each group of age, indicating omicron invasion. Observations are shown as points, fitted splines as lines, and 95% confidence intervals as shaded regions.

Comparison between SGTF cases from UKHSA data and Omicron cases in the study transmission model indicated that the projected proportion of new infections was due to the Omicron variant.

According to the immune distribution model estimated against Omicron, young adults were the most susceptible to infection, convalescent immunity was mainly seen in those under 60 years of age, and the vast majority of people 60 years or older were protected by a booster vaccination.

Estimated distribution of immune status by age based on the model at the start of December 2021. Susceptible individuals (who have no protection, in blue) are concentrated in the youngest age groups, while those with low immunity. against infection (retrieved in green) are usually under 60 years of age. For those over 60, the vast majority are protected by booster vaccines (yellow).

Conclusion

In the present study, the authors extended their existing SARS-CoV-2 predictive model to include the dynamics of the Omicron variant from England and demonstrated rapid relative growth of the Omicron variant compared to the Delta variant. In addition, there has been a sharp increase in infections, hospitalizations and deaths following the increase in SGTF induced by Omicron. The results of the study help to predict future scenarios and the impact of various interventions associated with this new variant.

While TaqPath PCR testing provided a clear picture of the relative growth of the Omicron variant, other measures used in this study are significantly less certain and strongly influenced by system latency and behavior changes. Overall, the study’s projections indicate that Omicron may generate high levels of infections and hospitalizations that could disrupt healthcare services.

*Important Notice

medRxiv publishes preliminary scientific reports that are not peer reviewed and, therefore, should not be considered conclusive, guide clinical practice / health-related behavior, or treated as established information.