Back

Epidemiological and genomic investigation of chikungunya virus in Rio de Janeiro state, Brazil, between 2015 and 2018

Moreira, F. R. R.; Menezes, M. T.; Salgado-Benvindo, C.; Whittaker, C.; Cox, V.; Chandradeva, N.; Paula, H. H. S.; Martins, A. F.; Chagas, R. R.; Brasil, R. D. V.; Candido, D. S.; Herlinger, A. L.; Ribeiro, M. O.; Arruda, M. B.; Alvarez, P.; Torres, M. C. P.; Dorigatti, I.; Brady, O.; Voloch, C. M.; Tanuri, A.; Iani, F.; Souza, W. M.; Cardozo, S. V.; Faria, N. R.; Aguiar, R. S.

2023-04-20 epidemiology
10.1101/2023.04.12.23288482 medRxiv
Show abstract

Since 2014, Brazil has experienced an unprecedented epidemic caused by chikungunya virus (CHIKV), with several waves of East-Central-South-African (ECSA) lineage transmission reported across the country. In 2018, Rio de Janeiro state, the third most populous state in Brazil, reported 41% of all chikungunya cases in the country. Here we use evolutionary and epidemiological analysis to estimate the timescale of CHIKV-ECSA-American lineage and its epidemiological patterns in Rio de Janeiro. We show that the CHIKV-ECSA outbreak in Rio de Janeiro derived from two distinct clades introduced from the Northeast region in mid-2015 (clade RJ1, n = 63/67 genomes from Rio de Janeiro) and mid-2017 (clade RJ2, n = 4/67). We detected evidence for positive selection in non-structural proteins linked with viral replication in the RJ1 clade (clade-defining: nsP4-A481D) and the RJ2 clade (nsP1-D351G). Finally, we estimate the CHIKV-ECSAs basic reproduction number (R0) to be between 1.2 to 1.6 and show that its instantaneous reproduction number (Rt) displays a strong seasonal pattern with peaks in transmission coinciding with periods of high Aedes aegypti transmission potential. Our results highlight the need for continued genomic and epidemiological surveillance of CHIKV in Brazil, particularly during periods of high ecological suitability, and show that selective pressures underline the emergence and evolution of the large urban CHIKV-ECSA outbreak in Rio de Janeiro.

Matching journals

The top 2 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.