Back

MOLECULAR EPIDEMIOLOGY TO UNDERSTAND THE SARS-CoV-2 EMERGENCE IN THE BRAZILIAN AMAZON REGION

dos Santos, M. C.; Sousa, E. C.; Ferreira, J. A.; Silva, S. P.; Souza, M. P.; Cardoso, J. F.; Silva, A. M.; Barbagelata, L. S.; Chagas, W. D.; Ferreira, J. L.; Souza, E. M.; Vilaca, P. L.; Alves, J. C.; Abreu, M. C.; Lobo, P. S.; Santos, F. S.; Lima, A. A.; Bragagnolo, C. M.; Soares, L. S.; Almeida, P. S.; Oliveira, D. S.; Amorim, C. K.; Costa, I. B.; Teixeira, D. M.; Penha, E. T.; Bezerra, D. A.; Siqueira, J. A.; Tavares, F. N.; Freitas, F. B.; Rodrigues, J. T.; Mazaro, J.; Costa, A. S.; Cavalcante, M. S.; Silva, M. S.; Silva, I. A.; Borges, G. A.; Lima, L. G.; Ferreira, H. L.; Livorati, M. T

2020-09-07 infectious diseases
10.1101/2020.09.04.20184523 medRxiv
Show abstract

The COVID-19 pandemic in Brazil has demonstrated an important public health impact, as has been observed in the world. In Brazil, the Amazon Region contributed with a large number of cases of COVID-19, especially in the beginning of the circulation of SARS-CoV-2 in the country. Thus, we describe the epidemiological profile of COVID-19 and the genetic diversity of SARS-CoV-2 strains circulating in the Amazon Region. We observe an extensive spread of virus in this Brazilian site. The data on sex, age and symptoms presented by the investigated individuals were similar to what has been observed worldwide. The genomic analysis of the viruses revealed important amino acid changes, including the D614G and the I33T in Spike and ORF6 proteins, respectively. The latter found in strains originating in Brazil. The phylogenetic analyzes demonstrated the circulation of the lineages B.1 and B.1.1, whose circulation in Brazil has already been previous reported. Our data reveals molecular epidemiology of SARS-CoV-2 in the Amazon Region. These findings also reinforce the importance of continuous genomic surveillance this virus with the aim of providing accurate and updated data to understand and map the transmission network of this agent in order to subsidize operational decisions in public health.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.