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Estimating the strength of selection for new SARS-CoV-2 variants.

ABSTRACT: Controlling the SARS-CoV-2 pandemic becomes increasingly challenging as the virus adapts to human hosts through the continual emergence of more transmissible variants. Simply observing that a variant is increasing in frequency is relatively straightforward, but more sophisticated methodology is needed to determine whether a new variant is a global threat and the magnitude of its selective advantage. We present three methods for quantifying the strength of selection for new and emerging variants of SARS-CoV-2 relative to the background of contemporaneous variants. These methods range from a detailed model of dynamics within one country to a broad analysis across all countries, and they include alternative explanations such as migration and drift. We find evidence for strong selection favoring the D614G spike mutation and B.1.1.7 (Alpha), weaker selection favoring B.1.351 (Beta), and no advantage of R.1 after it spreads beyond Japan. Cutting back data to earlier time horizons reveals large uncertainty very soon after emergence, but that estimates of selection stabilize after several weeks. Our results also show substantial heterogeneity among countries, demonstrating the need for a truly global perspective on the molecular epidemiology of SARS-CoV-2.

SUBMITTER: van Dorp CH

PROVIDER: S-EPMC8020992 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

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Estimating the strength of selection for new SARS-CoV-2 variants.

van Dorp Christiaan H CH Goldberg Emma E EE Hengartner Nick N Ke Ruian R Romero-Severson Ethan O EO

medRxiv : the preprint server for health sciences 20210924

Controlling the SARS-CoV-2 pandemic becomes increasingly challenging as the virus adapts to human hosts through the continual emergence of more transmissible variants. Simply observing that a variant is increasing in frequency is relatively straightforward, but more sophisticated methodology is needed to determine whether a new variant is a global threat and the magnitude of its selective advantage. We present three methods for quantifying the strength of selection for new and emerging variants ...[more]

PMID: 33821289

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Project description:BackgroundTravel restrictions and border controls were used extensively during the COVID-19 pandemic. However, the processes for making robust evidence-based risk assessments of source countries to inform border control policies was in many cases very limited.MethodsBetween April 2020 and February 2022, all international arrivals to New Zealand were required to spend 14 days in government-managed quarantine facilities and were tested at least twice. The infection rates among arrivals in the years 2020, 2021 and 2022 were respectively 6.3, 9.4 and 90.0 cases per thousand arrivals (487, 1064 and 1496 cases). Test results for all arrivals were linked with travel history, providing a large and comprehensive dataset on the number of SARS-CoV-2-positive and negative travellers from different countries over time. We developed a statistical model to predict the country-level infection risk based on infection rates among recent arrivals and reported cases in the country of origin. The model incorporates a country-level random effect to allow for the differences between the infection risk of the population of each country and that of travellers to New Zealand. A time dependent auto-regressive component of the model allows for short term correlation in infection rates.ResultsA model selection and checking exercise found that the model was robust and reliable for forecasting arrival risk for 2 weeks ahead. We used the model to forecast the number of infected arrivals in future weeks and categorised countries according to their risk level. The model was implemented in R and was used by the New Zealand Ministry of Health to help inform border control policy during 2021.ConclusionsA robust and practical forecasting tool was developed for forecasting infection risk among arriving passengers during a period of controlled borders during the COVID-19 pandemic. The model uses historical infection rates among arrivals and current infection rates in the source country to make separate risk predictions for arrivals from each country.

Dataset Information

Estimating the strength of selection for new SARS-CoV-2 variants.

Publications

Estimating the strength of selection for new SARS-CoV-2 variants.

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