Chikungunya virus infection in the southernmost state of Brazil was characterised by self-limited transmission (2017-2019) and a larger 2021 outbreak

Chikungunya is a mosquito-borne virus that has been causing large outbreaks in the Americas since 2014. In Brazil, Asian-Caribbean (AC) and East-Central-South-African (ECSA) genotypes have been detected and lead to large outbreaks in several Brazilian states. In Rio Grande do Sul (RS), the southernmost state of Brazil, the first cases were reported in 2016.

OBJECTIVES AND METHODS

We employed genome sequencing and epidemiological investigation to characterise the Chikungunya fever (CHIKF) burden in RS between 2017-2021.

FINDINGS

We detected an increasing CHIKF burden linked to travel associated introductions and communitary transmission of distinct lineages of the ECSA genotype during this period.

MAIN CONCLUSIONS

Until 2020, CHIKV introductions were most travel associated and transmission was limited. Then, in 2021, the largest outbreak occurred in the state associated with the introduction of a new ECSA lineage. CHIKV outbreaks are likely to occur in the near future due to abundant competent vectors and a susceptible population, exposing more than 11 million inhabitants to an increasing infection risk.

Key words:
arbovirus; epidemiological surveillance; disease burden; alphavirus; outbreak; genomics; climate change

Authorship

Tatiana Schäffer Gregianini

Study concept

Sample collection

Molecular analyses

Data acquisition

Analysis

Writing the draft

Manuscript

Secretaria Estadual da Saúde do Rio Grande do Sul, Centro Estadual de Vigilância em Saúde, Laboratório Central de Saúde Pública, Porto Alegre, RS, BrasilSecretaria Estadual da Saúde do Rio Grande do SulBrazilPorto Alegre, RS, BrazilSecretaria Estadual da Saúde do Rio Grande do Sul, Centro Estadual de Vigilância em Saúde, Laboratório Central de Saúde Pública, Porto Alegre, RS, Brasil

Richard Steiner Salvato

Bioinformatic analysis

Secretaria Estadual da Saúde do Rio Grande do Sul, Centro Estadual de Vigilância em Saúde, Centro de Desenvolvimento Científico e Tecnológico, Porto Alegre, RS, BrasilSecretaria Estadual da Saúde do Rio Grande do SulBrazilPorto Alegre, RS, BrazilSecretaria Estadual da Saúde do Rio Grande do Sul, Centro Estadual de Vigilância em Saúde, Centro de Desenvolvimento Científico e Tecnológico, Porto Alegre, RS, Brasil

Regina Bones Barcellos

Library preparation

Genomic sequencing

Fernanda Marques Godinho

Library preparation

Genomic sequencing

Amanda Pellenz Ruivo

Library preparation

Genomic sequencing

Viviane Horn de Melo

Library preparation

Genomic sequencing

Júlio Augusto Schroder

Bioinformatic analysis

Fernanda Letícia Martiny

Sample collection

Molecular detection

Analysis

Erica Bortoli Möllmann

Sample collection

Molecular detection

Analysis

Cátia Favreto

Epidemiological data collection

Analysis

Secretaria Estadual da Saúde do Rio Grande do Sul, Centro Estadual de Vigilância em Saúde, Divisão de Vigilância Epidemiológica, Porto Alegre, RS, BrasilSecretaria Estadual da Saúde do Rio Grande do SulBrazilPorto Alegre, RS, BrazilSecretaria Estadual da Saúde do Rio Grande do Sul, Centro Estadual de Vigilância em Saúde, Divisão de Vigilância Epidemiológica, Porto Alegre, RS, Brasil

Ludmila Fiorenzano Baethgen

Sample collection

Molecular detection

Analysis

Vithoria Pompermaier Ferreira

Sample collection

Molecular detection

Analysis

Lívia Eidt de Lima

Sample collection

Molecular detection

Analysis

Cláudia Fasolo Piazza

Sample collection

Molecular detection

Analysis

Taís Raquel Marcon Machado

Sample collection

Molecular detection

Analysis

Irina Marieta Becker

Sample collection

Molecular detection

Analysis

Raquel Rocha Ramos

Sample collection

Molecular detection

Analysis

Guilherme Carey Frölich

Sample collection

Molecular detection

Analysis

Alana Fraga Rossetti

Sample collection

Molecular detection

Analysis

Lucas da Cunha Almeida

Sample collection

Molecular detection

Analysis

Tahiana Machado Antunes Rodrigues

Sample collection

Molecular detection

Analysis

Isabella Tabelli Bragança

Sample collection

Molecular detection

Analysis

Aline Alves Scarpellini Campos

Epidemiological data collection

Analysis

Secretaria Estadual da Saúde do Rio Grande do Sul, Centro Estadual de Vigilância em Saúde, Divisão de Vigilância Ambiental, Porto Alegre, RS, BrasilSecretaria Estadual da Saúde do Rio Grande do SulBrazilPorto Alegre, RS, BrazilSecretaria Estadual da Saúde do Rio Grande do Sul, Centro Estadual de Vigilância em Saúde, Divisão de Vigilância Ambiental, Porto Alegre, RS, Brasil

Verônica Baú Manzoni

Epidemiological data collection

Analysis

Prefeitura de São Nicolau, Secretaria Municipal de Saúde, São Nicolau, RS, BrasilPrefeitura de São NicolauBrazilSão Nicolau, RS, BrazilPrefeitura de São Nicolau, Secretaria Municipal de Saúde, São Nicolau, RS, Brasil

Lais Ceschini Machado

Sample processing for sequencing

Library preparation

Genome sequence

Fundação Oswaldo Cruz-Fiocruz, Instituto Aggeu Magalhães, Departamento de Entomologia e Núcleo de Bioinformática, Recife, PE, BrasilFundação Oswaldo Cruz-FiocruzBrazilRecife, PE, BrazilFundação Oswaldo Cruz-Fiocruz, Instituto Aggeu Magalhães, Departamento de Entomologia e Núcleo de Bioinformática, Recife, PE, Brasil

Luisa Maria Inácio da Silva

Sample processing for sequencing

Library preparation

Genome sequence

André Luiz Sá de Oliveira

Map rendering

Formal analyses

Fundação Oswaldo Cruz-Fiocruz, Instituto Aggeu Magalhães, Núcleo de Estatística e Geoprocessamento, Recife, PE, BrasilFundação Oswaldo Cruz-FiocruzBrazilRecife, PE, BrazilFundação Oswaldo Cruz-Fiocruz, Instituto Aggeu Magalhães, Núcleo de Estatística e Geoprocessamento, Recife, PE, Brasil

Marcelo Henrique Santos Paiva

Map rendering

Formal analyses

Zenaida Marion Alves Nunes

Sample collection

Molecular detection

Analysis

Paula Rodrigues de Almeida

Primer design

Analysis

Universidade Feevale, Laboratório de Microbiologia Molecular, Novo Hamburgo, RS, BrasilUniversidade FeevaleBrazilNovo Hamburgo, RS, BrazilUniversidade Feevale, Laboratório de Microbiologia Molecular, Novo Hamburgo, RS, Brasil

Meriane Demoliner

Primer design

Analysis

Juliana Schons Gularte

Primer design

Analysis

Mariana Soares da Silva

Primer design

Analysis

Micheli Filippi

Primer design

Analysis

Vyctoria Malayhka de Abreu Góes Pereira

Primer design

Analysis

Fernando Rosado Spilki

Conception and design of the study

Data collection

Analysis

Interpretation

Critical revision of the article

Final approval of the version to be submitted

Ana Beatriz Gorini da Veiga

Data analysis

Funding and resources acquisition

Reviewing draft

Writing the manuscript

Universidade Federal de Ciências da Saúde de Porto Alegre, Porto Alegre, RS, BrasilUniversidade Federal de Ciências da Saúde de Porto AlegreBrazilPorto Alegre, RS, BrazilUniversidade Federal de Ciências da Saúde de Porto Alegre, Porto Alegre, RS, Brasil

Gabriel Luz Wallau ⁺+ Corresponding author: gabriel.wallau@fiocruz.br

Conceptualisation of the study

Formal analysis

Investigation

Resources

Visualisation

Funding and resources acquisition

Writing draft

Reviewing

Writing and final manuscript

National Reference Centre for Tropical Infectious Diseases, Bernhard Nocht Institute for Tropical Medicine, Department of Arbovirology, WHO Collaborating Centre for Arbovirus and Haemorrhagic Fever Reference and Research, Hamburg, GermanyNational Reference Centre for Tropical Infectious DiseasesGermanyHamburg, GermanyNational Reference Centre for Tropical Infectious Diseases, Bernhard Nocht Institute for Tropical Medicine, Department of Arbovirology, WHO Collaborating Centre for Arbovirus and Haemorrhagic Fever Reference and Research, Hamburg, Germany

http://orcid.org/0000-0002-1419-5713

+ Corresponding author: gabriel.wallau@fiocruz.br

The authors declare no conflict of interest

SCIMAGO INSTITUTIONS RANKINGS

Figures | Tables

Figures (3)
Tables (1)

Thumbnail

Fig. 1:
spatial interpolation of Chikungunya virus (CHIKV) cases in the State of Rio Grande do Sul, Brazil, between 2017 and 2021 transformed to logarithms to the base 10 (A). Graphs show the number of CHIKV cases notified [suspected based on typical Chikungunya fever (CHIKF) symptoms], confirmed by molecular assays [real-time quantitative polymerase chain reaction (RT-qPCR) and/or immuno assays], showing autochthonous transmission (no travel-associated) and confirmed cases with travel to/from endemic Latin American or Brazilian regions prior to symptoms onset in Rio Grande do Sul State (B) and São Nicolau municipality (C).

Thumbnail

Fig. 2:
symptoms and age distribution of cases with suspected, confirmed and discarded Chikungunya virus (CHIKV) infection. (A) Number of CHIKV cases notified (suspected based on typical Chikungunya fever (CHIKF) symptoms), confirmed by molecular assays [real-time quantitative polymerase chain reaction (RT-qPCR) or immuno assays] and discarded from São Nicolau state in 2021 stratified by reported symptoms; and (B) age stratification of cases in São Nicolau municipality.

Thumbnail

Fig. 3:
Bayesian phylogenetic and ancestral state reconstruction of the East-Central-South-African (ECSA) lineage circulating in Brazil, other South American countries and Caribbean islands. (A) Coloured map showing the countries and Brazilian states with Chikungunya virus (CHIKV) genomic information used in the Bayesian phylogenetic reconstruction of the ECSA lineage circulating in Brazil, other South American countries and Caribbean islands. (B) Time resolved phylogenetic tree zoom on the São Nicolau outbreak. Key nodes and clades are highlighted with associated posterior probabilities, tMRCA HPD 95% and ancestral reconstruction state probabilities recovered. Horizontal bars are HPD 95% tMRCA dating. Arrows indicate samples from Rio Grande do Sul State patients, municipality of sampling and associated travel history between parenthesis. BSPP: Bayesian support posterior probability; ASRPP: ancestral state reconstruction posterior probability.

Thumbnail

TABLE
Information of samples and genomes sequenced from Rio Grande do Sul and São Nicolau outbreak between 2017-2021

Fig. 1: spatial interpolation of Chikungunya virus (CHIKV) cases in the State of Rio Grande do Sul, Brazil, between 2017 and 2021 transformed to logarithms to the base 10 (A). Graphs show the number of CHIKV cases notified [suspected based on typical Chikungunya fever (CHIKF) symptoms], confirmed by molecular assays [real-time quantitative polymerase chain reaction (RT-qPCR) and/or immuno assays], showing autochthonous transmission (no travel-associated) and confirmed cases with travel to/from endemic Latin American or Brazilian regions prior to symptoms onset in Rio Grande do Sul State (B) and São Nicolau municipality (C).

Fig. 2: symptoms and age distribution of cases with suspected, confirmed and discarded Chikungunya virus (CHIKV) infection. (A) Number of CHIKV cases notified (suspected based on typical Chikungunya fever (CHIKF) symptoms), confirmed by molecular assays [real-time quantitative polymerase chain reaction (RT-qPCR) or immuno assays] and discarded from São Nicolau state in 2021 stratified by reported symptoms; and (B) age stratification of cases in São Nicolau municipality.

Fig. 3: Bayesian phylogenetic and ancestral state reconstruction of the East-Central-South-African (ECSA) lineage circulating in Brazil, other South American countries and Caribbean islands. (A) Coloured map showing the countries and Brazilian states with Chikungunya virus (CHIKV) genomic information used in the Bayesian phylogenetic reconstruction of the ECSA lineage circulating in Brazil, other South American countries and Caribbean islands. (B) Time resolved phylogenetic tree zoom on the São Nicolau outbreak. Key nodes and clades are highlighted with associated posterior probabilities, tMRCA HPD 95% and ancestral reconstruction state probabilities recovered. Horizontal bars are HPD 95% tMRCA dating. Arrows indicate samples from Rio Grande do Sul State patients, municipality of sampling and associated travel history between parenthesis. BSPP: Bayesian support posterior probability; ASRPP: ancestral state reconstruction posterior probability.

TABLE Information of samples and genomes sequenced from Rio Grande do Sul and São Nicolau outbreak between 2017-2021

Sample identification number	Municipality	Symptoms started (dd-mm-yyy)	Sampling date (dd-mm-yyy)	Travel association	Ct value	Average depth	Coverage breadth
1115	São Nicolau	3/9/2021	3/17/2021		24	4741.98	97.55
1124	São Nicolau	3/13/2021	3/17/2021		31	3629.10	99.22
1499	São Nicolau	3/18/2021	3/24/2021		24	5444.54	99.26
1502	São Nicolau	3/18/2021	3/24/2021		30	2557.28	100.00
1504	São Nicolau	3/15/2021	3/24/2021		30	4454.54	99.46
1509	São Nicolau	3/21/2021	3/24/2021		21	5623.90	99.91
1892	São Nicolau	3/29/2021	3/30/2021		20	3773.15	99.75
2405	São Nicolau	3/26/2021	3/31/2021		30	2053.71	96.86
4079	São Nicolau	4/13/2021	4/15/2021		25	3383.81	99.89
4080	São Nicolau	4/9/2021	4/16/2021		29	1273.39	98.97
4081	São Nicolau	4/15/2021	4/15/2021		28	5643.75	99.99
5166	São Nicolau	4/19/2021	4/23/2021		26	2599.88	99.89
5171	São Nicolau	4/16/2021	4/23/2021		28	3711.22	99.91
5180	São Nicolau	4/19/2021	4/26/2021		28	4601.83	98.54
5183	São Nicolau	4/21/2021	4/27/2021		30	2162.92	97.09
6165	Espumoso	5/4/2021	5/7/2021		28	2214.65	97.21
6701	Espumoso	5/8/2021	5/12/2021		28	1289.84	97.04
7302	São Nicolau	5/10/2021	5/13/2021		30	1258.46	95.76
2849	Canoas	6/19/2019	6/19/2019		21	2900.56	97.84
1982	Rio Grande	5/16/2019	5/18/2019	Rio de Janeiro	35	1955.66	98.58
158	Porto Alegre	1/28/2019	2/2/2019	Cuba	35	2072.16	98.56
336	Porto Alegre	3/6/2019	3/9/2019		23	198.69	97.41
1328	Caxias do Sul	4/26/2017	5/3/2017		18	3088.81	98.70

Ct: cycle threshold.

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Métricas

Chikungunya virus infection in the southernmost state of Brazil was characterised by self-limited transmission (2017-2019) and a larger 2021 outbreak

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[1] + Corresponding author: gabriel.wallau@fiocruz.br