Chikungunya Virus serological cross-reactivity in co-endemic settings: Mechanisms, Diagnostic implications, and epidemiological consequences

Chikungunya virus (CHIKV) causes an acute febrile illness with debilitating arthralgia, and serological assays remain central to its diagnosis and surveillance. However, antibody-based results are complicated by cross-reactivity in settings where multiple arboviruses co-circulate. This review distinguishes three related but distinct phenomena affecting CHIKV serology: biological cross-reactivity, arising from genuine antigenic overlap between related viruses; assay-associated non-target reactivity, in which a diagnostic platform generates a positive signal without an established biological relationship between the viruses; and diagnostic misclassification, the downstream epidemiological consequence of conflating the two. Applying this framework to five virus pairs, we find that CHIKV–O'nyong-nyong virus (ONNV), CHIKV–Mayaro virus (MAYV) and CHIKV–Ross River virus (RRV) relationships are supported by shared alphavirus antigenic architecture and, in several studies, by functional cross-neutralisation. By contrast, CHIKV reactivity reported in dengue virus (DENV)- and Zika virus (ZIKV)-associated sera lacks an established biological basis and is better classified as assay-associated non-target reactivity of unresolved mechanism. These distinctions matter: unadjusted seropositivity can inflate or obscure CHIKV transmission estimates, and no single diagnostic platform resolves the problem for every virus pair. We argue that CHIKV diagnosis in co-endemic settings requires a layered strategy: molecular detection during the acute phase, antigenically informed and multiplex serology thereafter, and neutralisation-based confirmation where attribution is critical, combined with cross-reactivity-aware statistical modelling for surveillance. Strengthening assay validation and confirmatory testing, particularly in African settings such as Nigeria where CHIKV, DENV and related alphaviruses circulate together, is essential for improving diagnostic accuracy and epidemiological inference.