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Molecular epidemiology of SARS-CoV-2 in Mongolia, first experience with nanopore sequencing in lower- and
Munkhtuya Erendereg1,2, Suvd Tumurbaatar3, Otgonjargal Byambaa1
1Department of Microbiology and Infection Prevention Control, School of Biomedicine, Mongolian National University of Medical Sciences, Ulaanbaatar, Mongolia.
Background:
Coronavirus disease (COVID-19) has had a significant impact globally, and extensive genomic research has been conducted on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) lineage patterns and its variants. Mongolia's effective response resulted in low prevalence until vaccinations became available. However, due to the lack of systematically collected data and absence of whole genome sequencing capabilities, we conducted a two-stepped, nationally representative molecular epidemiologic study of SARS-CoV-2 in Mongolia for 2020 and 2021.
Methods:
We used retrospective analysis of stored biological samples from November 2020 to October 2021 and a variant-specific real-time reverse transcription polymerase chain reaction (RT-PCR) test to detect SARS-CoV-2 variants, followed by whole genome sequencing by Nanopore technology. Samples were retrieved from different sites and stored at -70°C deep freezer, and tests were performed on samples with cycle threshold <30.
Results:
Out of 4879 nucleic acid tests, 799 whole genome sequencing had been carried out. Among the stored samples of earlier local transmission, we found the 20B (B.1.1.46) variant predominated in the earlier local transmission period. A slower introduction and circulation of alpha and delta variants were observed compared to global dynamics in 2020 and 2021. Beta or Gamma variants were not detected between November 2020 and September 2021 in Mongolia.
Conclusions:
SARS-CoV-2 variants of concerns including alpha and delta were delayed in circulation potentially due to public health stringencies in Mongolia. We are sharing our initial experience with whole genome sequencing of SARS-CoV-2 from Mongolia, where sequencing data is sparse.
