SARS-CoV-2 transmission dynamics in Belarus in 2020 revealed by genomic and incidence data analysis

  1. Alina Nemira
  2. Ayotomiwa Ezekiel Adeniyi
  3. Elena L. Gasich
  4. Kirill Y. Bulda
  5. Leonid N. Valentovich
  6. Anatoly G. Krasko
  7. Olga Glebova
  8. Alexander Kirpich
  9. Pavel Skums

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Abstract

Background

Non-pharmaceutical interventions (NPIs) have been implemented worldwide to curb COVID-19 spread. Belarus is a rare case of a country with a relatively modern healthcare system, where highly limited NPIs have been enacted. Thus, investigation of Belarusian COVID-19 dynamics is essential for the local and global assessment of the impact of NPI strategies.

Methods

We integrate genomic epidemiology and surveillance methods to investigate the spread of SARS-CoV-2 in Belarus in 2020. We utilize phylodynamics, phylogeography, and probabilistic bias inference to study the virus import and export routes, the dynamics of the effective reproduction number, and the incidence of SARS-CoV-2 infection.

Results

Here we show that the estimated cumulative number of infections by June 2020 exceeds the confirmed case number by a factor of ~4 (95% confidence interval (2; 9)). Intra-country SARS-CoV-2 genomic diversity originates from at least 18 introductions from different regions, with a high proportion of regional transmissions. Phylodynamic analysis indicates a moderate reduction of the effective reproductive number after the introduction of limited NPIs, but its magnitude is lower than for developed countries with large-scale NPIs. On the other hand, the effective reproduction number estimate is comparable with that for the neighboring Ukraine, where NPIs were broader.

Conclusions

The example of Belarus demonstrates how countries with relatively low outward population mobility continue to be integral parts of the global epidemiological environment. Comparison of the effective reproduction number dynamics for Belarus and other countries reveals the effect of different NPI strategies but also emphasizes the role of regional Eastern European sociodemographic factors in the virus spread.

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  1. Version published to 10.1038/s43856-021-00031-1
  2. ScreenIT

    SciScore for 10.1101/2021.04.13.21255404: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The sequences were aligned using MAFFT [34], and a maximum likelihood (ML) phylogenetic tree was constructed using IQ-TREE [39] under Hasegawa-Kishino-Yano (HKY)+Γ nucleotide substitution model with a gamma-distributed site rate variation [30].
    MAFFT
    suggested: (MAFFT, RRID:SCR_011811)
    IQ-TREE
    suggested: (IQ-TREE, RRID:SCR_017254)
    This trait inference algorithm has been implemented in Matlab (v. R2019b).
    Matlab
    suggested: (MATLAB, RRID:SCR_001622)
    At first, temporal signal was evaluated by constructing an ML phylogeny under HKY+Γ nucleotide substitution model and by regressing root-to-tip genetic divergence against sampling dates using TempEst (v.1.5.3) [43].
    TempEst
    suggested: (TempEst, RRID:SCR_017304)
    The MCMC sampling quality was assessed using Tracer (v.1.7.1) [41] and accepted if all parameters had effective sampling sizes (ESS) higher than 200.
    Tracer
    suggested: (Tracer, RRID:SCR_019121)
    In the first approach, trees and BDSKY parameters sampled by BEAST were used to reconstruct cumulative case count trajectories using the particle filter algorithm implemented in EpiInf (v.7.3.0) [51].
    BEAST
    suggested: (BEAST, RRID:SCR_010228)

    Results from OddPub: Thank you for sharing your code.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    It is important to highlight that the presented study has several limitations. The first of them is the scarcity of currently available genomic data, especially in comparison with most of other European countries. Our approach strives to compensates for it by utilizing informative priors and linked models for phylogenetic and phylodynamics inference. BD-SKY models are also sensitive enough and suitable for inference even for smaller genomic datasets. For example, the numbers of sequences and/or density of branching events in this study is similar to those in other studies [49, 38, 26], where meaningful estimates of ℛe have been produced for several epidemics, including SARS-CoV-2. Nevertheless, the inference precision could have been higher, if more SARS-CoV-2 genomes have been available. For Belarus, however, significant expansion of the available genomic dataset in the near future is unlikely, and in our opinion the lack of other studies justifies the need to fill the knowledge gap and to report the results based on the existing data. We also hope that this study will serve as a trigger for further SARS-CoV-2 genomic epidemiology studies in Belarus and will encourage funding increase and the corresponding development and expansion of sequencing facilities for molecular surveillance. The second limitation is that phylogeographic inference of introduction sources can be sensitive to sampling bias and can be affected by relatively slow accumulation of mutations in SARS-CoV-2 g...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

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  3. Version published to 10.1101/2021.04.13.21255404v1 on medRxiv