Novel approach to estimate transmission advantage of SARS-CoV-2 variants in real-time

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is the virus responsible for the coronavirus disease 2019 (COVID-19) pandemic, has caused over 5.2 million deaths and infected over 263 million throughout the world. Several traditional vaccines, as well as antivirals, have been developed to curb the pandemic. However, the emergence of several variants of SARS-CoV-2 having higher transmissibility requires strict control measures to mitigate the burden of this pandemic.

Study: A generic method and software to estimate the transmission advantage of pathogen variants in real-time: SARS-CoV-2 as a case-study. Image Credit: CKA / Shutterstock.com

Background

The emergence of the Alpha variant in September 2020 and the Delta variant in 2021 altered the trajectory of the COVID-19 pandemic, leading to renewed impositions of public health measures in several countries. To date, five variants have been classified as ‘variants of concern’ (VOC), including the Alpha, Beta, Gamma, Delta, and Omicron SARS-CoV-2 variants by the World Health Organization (WHO). These variants are associated with higher transmissibility, severity, and/or immune escape properties as compared to the original SARS-CoV-2 strain.

Recently, several studies have been conducted on the transmission potential of VOCs as compared to the non-VOC lineages. The researchers behind these studies have developed new approaches for estimating the transmission advantages of these VOCs. However, the time and expertise required to design and implement these approaches limit their real-time use.

A new study published on the preprint server medRxiv* retrospectively estimates the effective transmission advantage of the Alpha variant as compared to other non-VOC lineages circulating at that time in France and England using a new Bayesian inference method. The Alpha variant first originated during late summer to early autumn 2020 in England, while it was detected in early 2021 in France.

Effective transmission advantage of the Alpha SARS-CoV-2 variant over the wild-type in England (A) The daily reported incidence of cases of the wildtype (black) and Alpha (orange) in England from September 2020 to March 2021. (B) The effective reproduction number Rt estimated independently for the wildtype (x-axis) and Alpha (y-axis) on sliding weekly windows. The colour of the cells indicates the density of the draws from the respective posterior distributions of Rt. The dashed diagonal line indicates the x = y threshold. Coloured cells lying above the diagonal line suggest that Alpha is more transmissible. The orange line denotes the median effective transmission advantage estimated using MV-EpiEstim. 95% CrI were so narrow that they could not be distinguished from the line. (C) Effective transmission advantage estimated using MV-EpiEstim using data available up to the date specified on the x-axis. The dark blue line denotes the proportion of cumulative incidence of Alpha (right y-axis) counted from 1st September 2020. The black estimate corresponds to the multiplicative transmission advantage of Alpha estimated by Volz et al [33] in a report published on 31st December 2020. (D) Effective transmission advantage estimated using MV-EpiEstim for all NHS England regions together (diamond) and separately (solid circles), using data from 1st September 2020 to 14th March 2021. The NHS England regions are - East of England (EE), London (LON), Midlands (MID), North-East (NE), North-West (NW), South-East (SE), South-West (SW). In panels (C) and (D), the solid circles denote the median estimate, the vertical lines indicate the 95% CrI, and the red dashed line denotes the ϵ = 1 threshold.
Effective transmission advantage of the Alpha SARS-CoV-2 variant over the wild-type in England (A) The daily reported incidence of cases of the wildtype (black) and Alpha (orange) in England from September 2020 to March 2021. (B) The effective reproduction number Rt estimated independently for the wildtype (x-axis) and Alpha (y-axis) on sliding weekly windows. The colour of the cells indicates the density of the draws from the respective posterior distributions of Rt. The dashed diagonal line indicates the x = y threshold. Coloured cells lying above the diagonal line suggest that Alpha is more transmissible. The orange line denotes the median effective transmission advantage estimated using MV-EpiEstim. 95% CrI were so narrow that they could not be distinguished from the line. (C) Effective transmission advantage estimated using MV-EpiEstim using data available up to the date specified on the x-axis. The dark blue line denotes the proportion of cumulative incidence of Alpha (right y-axis) counted from 1st September 2020. The black estimate corresponds to the multiplicative transmission advantage of Alpha estimated by Volz et al [33] in a report published on 31st December 2020. (D) Effective transmission advantage estimated using MV-EpiEstim for all NHS England regions together (diamond) and separately (solid circles), using data from 1st September 2020 to 14th March 2021. The NHS England regions are - East of England (EE), London (LON), Midlands (MID), North-East (NE), North-West (NW), South-East (SE), South-West (SW). In panels (C) and (D), the solid circles denote the median estimate, the vertical lines indicate the 95% CrI, and the red dashed line denotes the ϵ = 1 threshold.

About the study

The current study involved a new Bayesian inference method, MV-EpiEstim (for Multi-Variant EpiEstim) that estimates the real-time transmission advantage of a new variant of a pathogen compared to a reference variant. The validity of the method was assessed under several scenarios with different values for the transmissibility of each variant and relies on the daily incidence data of the reference and the variant.

Data were collected from a total of seven National Health Service (NHS) regions in England between September 1, 2020, and March 14, 2021, and from 18 ADM2 regions in France between February 18 and May 30, 2021.

MV-EpiEstim can accurately estimate the transmission advantage when variants differed in natural history. However, a scenario similar to the real-time outbreak analysis, where the natural history of the new variant is different but is assumed to be the same as the reference, was also considered.

The transmission advantage was found to be unbiased, even in the presence of substantial super spreading or poor case reporting. Furthermore, the full posterior distribution of estimated transmission advantage was used to classify the variant as more or less transmissible as compared to the reference variant.

The current study involved several assumptions. First, it was assumed that the epidemic in each location is independent and closed when data from more than one location was used. Second, it is assumed that the reproduction number of a new variant is proportional to the reference variant at all times and all locations. Third, the number of secondary infections that are generated by each case is Poisson distributed.

Study findings

The results of the current study indicate that the SARS-CoV-2 Alpha variant was more transmissible as compared to the then circulating non-VOC lineages; however, the magnitude of transmission advantage varied over time and across regions.

The transmission advantage of the Alpha variant as compared to the wildtype strain of SARS-CoV-2 was found to be 1.46 in England, while the transmission advantage was 1.29 in France. Region-specific analysis revealed that the transmission advantage varies from 1.36 to 1.54 in England, while it varies from 1.21 to 1.41 in France. Alpha cases were also found to account for less than 10% of all cases between mid-September and early December by this method.

Therefore, the current study was quite accurate in analyzing the effective transmission advantage of the SARS-CoV-2 Alpha variant a few weeks before the earliest published estimate. This method does not require whole-genome sequence data if specific biomarkers to distinguish variants are available.

This approach not only provides information on the transmissibility of a new variant but also the ability of the variant to escape immunity. MV-EpiEstim reduces uncertainty in estimates, as it combines information across time and locations, assuming the transmission advantage remains constant across these factors.

Conclusions

The incidence of SARS-CoV-2 across the globe is still high, with nearly three million cases reported each week as of November 2021. The high level of transmission and low vaccine coverage can cause the emergence of newer variants.

The method used in this study can help in rapidly identifying new SARS-CoV-2 VOCs, as well as monitoring their transmissibility. This method can also be used for other pathogens such as influenza or Streptococcus pneumoniae.

*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:
Suchandrima Bhowmik

Written by

Suchandrima Bhowmik

Suchandrima has a Bachelor of Science (B.Sc.) degree in Microbiology and a Master of Science (M.Sc.) degree in Microbiology from the University of Calcutta, India. The study of health and diseases was always very important to her. In addition to Microbiology, she also gained extensive knowledge in Biochemistry, Immunology, Medical Microbiology, Metabolism, and Biotechnology as part of her master's degree.

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