Molecular characterization of chikungunya viruses associated with outbreaks in Kenya from 2017 to 2020

Abstract

In 2014, Chikungunya virus (CHIKV) infections were reported in Kilifi, Kenya, followed by a major outbreak in Mandera County in 2016. Since then, information regarding subsequent outbreaks in Kenya has been scarce. A study on the burden and etiologies of acute febrile illnesses (AFI) in Kenya reported an increase in CHIKV cases in Mombasa between December 2017 and December 2019. In January and February 2020, another outbreak of CHIKV occurred in Dadaab-Hagadera. These recurrent outbreaks necessitated the establishment of the molecular characteristics and phylogenetic differences of the CHIKVs collected from Mombasa and Dadaab-Hagadera. The challenge of distinguishing chikungunya fever (CHIKF) from other AFIs also underscored the importance of describing predictors of laboratory-confirmed CHIKV cases versus other AFIs. Sequences generated revealed that the Indian Ocean Lineage (IOL) was associated with sporadic CHIKV outbreaks in Kenya from 2017 to 2020. When sequences were compared to the 2014 Kilifi outbreak, key mutations associated with increased CHIKV viral fitness in mosquito vectors, including E1-K211E and E2-V264A, were observed in both Dadaab-Hagadera and Mombasa samples. Time-informed Bayesian phylogenetic analysis demonstrated clear geographic structuring, with Mombasa sequences forming tightly clustered clades and Dadaab-Hagadera sequences grouping separately, indicating sustained local transmission at both sites. However, some conserved amino acid substitutions were shared between the two locations, including nsP1-A104V and E2-I94V, suggesting circulation of closely related lineages. When the association of symptoms to positive CHIKV cases was compared to other AFI symptoms, sore muscles, headache, and convulsions were significantly more common in CHIKV cases. In contrast, diarrhea was considerably less frequent in CHIKV patients. Cough, skin rash, conjunctivitis, and vomiting were not significantly associated with CHIKV infection. In regions with limited access to point-of-care diagnostics, identifying clinical symptoms that are likely to be strongly associated with CHIKV infection can be important in the differential diagnosis from other AFIs, facilitating reliable patient screening and improving diagnostic accuracy, particularly during outbreaks when timely identification of CHIKV is critical. Continuous molecular surveillance of circulating CHIKV genotypes is important as the mutations identified in these CHIKV strains continuously accumulate and may have a significant impact on control strategies.