Seroprevalence of dengue, Zika, and chikungunya in São Sebastião, Brazil (2020–2021): a population-based survey

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BackgroundUrban arboviruses pose a significant global burden, particularly in tropical regions like Brazil. São Sebastião, a lower-middle-class urban area just 26 km from the Brazilian capital, is an endemic area for dengue. However, asymptomatic cases may obscure the actual extent of the disease. In this study, we measured the seroprevalence of dengue, Zika virus, and chikungunya, and compared these findings with surveillance data.MethodsA cross-sectional study was conducted involving 1,535 households. ELISA serological tests were performed to detect IgM and IgG antibodies against dengue, Zika virus, and chikungunya. History of previous exposure to arboviruses, data on age, gender, and education level were collected through a questionnaire. Participants who tested positive for IgM and/or IgG were classified as soropositive. Statistical analyses included tests for normality, associations, mean comparisons, and correlations. Positive serological results were compared with cases captured by local epidemiological surveillance.ResultsThe study included 1,405 individuals, divided into two groups related to pre-pandemic and pandemic COVID-19 phases. Among participants, 0.7% to 28.8% self-reported history of dengue, Zika, or chikungunya. However, the estimated overall seroprevalence was 64.3% (95% CI: 61.8–66.7) for dengue virus, 51.4% (95% CI: 48.8–53.9) for Zika virus, and 5.4% (95% CI: 4.4–6.7) for chikungunya virus. Multiple arboviruses were noted at 4.0% (95% CI: 3.1–5.1). Advancing age and lower education were associated with higher exposure to arboviruses (p < 0.05). The estimated number of urban arboviral infections was 84 times higher than reported cases.ConclusionsThe large gap between seroprevalence estimates and cases captured by epidemiological surveillance suggests a silent circulation of arboviruses, highlighting the need for comprehensive serological surveys in endemic regions. Addressing these discrepancies is crucial for effective resource allocation and implementation of public health interventions.