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Genomic tracking of SARS-COV-2 variants in Myanmar (preprint)
biorxiv; 2022.
Preprint
in English
| bioRxiv | ID: ppzbmed-10.1101.2022.10.10.511623
ABSTRACT
BackgroundIn December 2019, the COVID-19 disease started in Wuhan, China. WHO declared a pandemic on March 12, 2020, and the disease started in Myanmar on March 23, 2020. December brought variants around the world, threatening the healthcare systems. To counter those threats, Myanmar started the COVID-19 variant surveillance program in late 2020. MethodsWhole genome sequencing was done six times between January 2021 and March 2022. We chose 83 samples with a PCR threshold cycle of less than 25. Then, we used MiSeq FGx for sequencing and Illumina DRAGEN COVIDSeq pipeline, command line interface, GISAID, and MEGA version 7 for data analysis. Result and DiscussionJanuary 2021 results showed no variant. The second run during the rise of cases in June 2021 showed multiple variants like Alpha, Delta, and Kappa. There is only Delta in the third run at the height of mortality in August, and Delta alone continued until the fourth run in December. After the world reported the Omicron variant in November, Myanmar started a surveillance program. The fifth run in January 2022 showed both Omicron and Delta variants. The sixth run in March 2022 showed only Omicron BA.2. Amino acid mutation at receptor binding domain (RBD) of Spike glycoprotein started since the second run coupling with high transmission, recurrence, and vaccine escape. We also found the mutation at the primer targets used in current RT-PCR platforms. ConclusionThe occurrence of multiple variants and mutations claimed vigilance at ports of entry and preparedness for effective control measures. Genomic surveillance with the observation of evolutionary data is required to predict imminent threats of the current disease and diagnose emerging infectious diseases.
Full text:
Available
Collection:
Preprints
Database:
bioRxiv
Main subject:
Communicable Diseases, Emerging
/
COVID-19
Language:
English
Year:
2022
Document Type:
Preprint
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