First Whole-Genome Sequences of Chikungunya Virus Isolates from 2023 Outbreak in Burkina Faso, West Africa

Chikungunya disease has been spreading rapidly in recent years. Burkina Faso, which had never recorded any cases before, reported its first confirmed cases of chikungunya in September 2023, following the implementation of integrated surveillance of arboviruses (dengue, Zika, chikungunya) since 2017 through sentinel sites. Chikungunya cases then spread to several health areas. Between September and December 2023, a total of 7,020 suspected cases of chikungunya were analyzed by quantitative reverse transcriptase polymerase chain reaction, of which 342 were confirmed. However, no genomic data was previously available to track the evolution of the virus. The objective of this study is to identify the genotypes of the chikungunya virus (CHIKV) involved in the 2023 epidemic in Burkina Faso. A sample of 14 CHIKV-positive samples, selected mainly based on the origin of the index case and with cycle threshold values below 30, was sequenced. DNA libraries were prepared using Twist Bioscience technology, and sequencing was performed on the Illumina MiSeq platform. Bioinformatic analyses, including genome assembly, variant identification, and lineage assignment, were performed using the GeVarLi pipeline. Phylogenetic analysis was performed using reference sequences from the GISAID platform, with alignment performed using MAFFT and the phylogenetic tree generated using MEGA12. All 14 samples yielded complete genomic sequences, representing a 100% success rate. The average genomic coverage was 99.9% (±0.018), with an average depth of 16,193. Phylogenetic reconstruction showed that all sequences clustered within a single West African genotype clade, closely related to strains from Senegal and Côte d'Ivoire. This first genomic study of CHIKV in Burkina Faso confirms the circulation of a single clade and highlights regional transmission, underscoring the importance of genomic surveillance for outbreak response to epidemics and monitoring viral evolution.

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The authors declare no conflict of interest.

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The datasets used in this study are openly available at [repository link] and the source code is available on GitHub at [GitHub link].

This work did not receive any external funding.