Arbovirus‐Associated Guillain–Barré Syndrome: A Systematic Review and Meta‐Analysis of Clinical Characteristics, Subtypes, and Vaccine Associations

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BACKGROUND: Arboviral infections are increasingly recognized as triggers of Guillain-Barré syndrome (GBS), yet the clinical spectrum, subtype distribution, and strength of association across different arboviruses remain incompletely characterized. This systematic review and meta-analysis evaluated the epidemiology, clinical features, and outcomes of arbovirus-associated GBS. METHODS: PubMed, Scopus, Web of Science, and Google Scholar were searched through early 2025 following PRISMA 2020 guidelines. Observational studies were included in the quantitative meta-analysis, while case reports and case series were synthesized qualitatively. Random-effects models were used to estimate pooled prevalence, odds ratios, and clinical outcomes. RESULTS: One hundred studies were included, comprising 74 case reports/case series, 19 prevalence studies, and seven case-control studies. The pooled prevalence of GBS among individuals with arboviral infection was 1% (95% CI: 0.3%-3.3%), whereas 37% (95% CI: 22%-54%) of patients with GBS had laboratory evidence of recent arboviral infection. Arboviral co-infections occurred in 16% (95% CI: 8%-31%) of confirmed cases. Case-control studies demonstrated a significant association between Zika virus infection and GBS (OR = 8, 95% CI: 2-34). Qualitative synthesis showed frequent intensive care admission, mechanical ventilation, disability, and mortality. Demyelinating subtypes predominated in Zika virus-associated GBS, whereas axonal variants were more common following Japanese encephalitis virus infection. CONCLUSIONS: Multiple arboviruses are associated with GBS, with the strongest evidence for Zika virus. Arbovirus-associated GBS frequently results in severe neurological outcomes, highlighting the need for standardized diagnostics, enhanced surveillance, and prospective multicenter studies to improve understanding of disease mechanisms and optimize patient management.

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