ArticleBMC infectious diseases2026
Seroprevalence of endemic and emergent coronaviruses among SARS-COV-2 patients and healthcare workers in Abidjan, Côte d'Ivoire.
Article in BMC infectious diseases, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundUnderstanding the serological landscape of endemic and emergent coronaviruses is critical to interpreting early-pandemic immune responses and evaluating hypotheses of cross-reactivity. It was proposed that prior exposure to endemic coronaviruses could affect susceptibility or shape symptom severity through cross-reactive antibody responses. However, little was known about baseline coronavirus seroprevalence in many global regions, including Abidjan, Côte d’Ivoire. Characterizing this landscape provides key insights into early pandemic immunity and the potential influence of prior coronavirus exposures on SARS-CoV-2 immune response.
methodsHere, we probe this using data from syndromic surveillance in Abidjan, Côte d’Ivoire, collected between September 2020 and July 2021. We quantified IgG antibody levels to both spike and nucleocapsid proteins for emergent coronaviruses (SARS-CoV-1, SARS-CoV-2, and MERS-CoV) and endemic coronaviruses (HKU1, OC43, NL63 and 229E) using high-throughput multiplex bead assay. Samples were collected from SARS-CoV-2 negative healthcare workers (N = 202) and SARS-CoV-2 positive patients (N = 207). SARS-CoV-2 positive patients returned for repeat sampling at day 28 (N = 131).
resultsSARS-CoV-2 negative healthcare workers had higher SARS-CoV-1 seropositivity [0.27 (CI: 0.21–0.33) vs. 0.18 (CI: 0.13–0.24)] and SARS-CoV-2 seropositivity [0.52 (CI: 0.46–0.59) vs. 0.37 (CI: 0.31–0.44)] than SARS-CoV-2 positive patients. There were no significant differences among endemic coronaviruses between the healthcare workers and patients. Among the endemic coronaviruses, seropositivity was highest for 229E at 0.96 (95% CI: 0.94–0.98) and lowest for HKU1 at 0.56 (95% CI: 0.51–0.61) We found no significant sex difference in seropositivity to any coronavirus.
conclusionsThese findings provide a snapshot of endemic and emergent coronavirus seroprevalences during the beginning of the COVID-19 pandemic in Abidjan. We observed high seroprevalence to endemic alphacoronaviruses (229E and NL63), slightly lower levels for betacoronaviruses (HKU1 and OC43), and cross-reactive antibody signals to SARS-CoV-1. Among SARS-CoV-2 positive patients sampled again after 28 days, we did not observe evidence of boosting antibody levels to endemic coronaviruses, suggesting no cross-reactive responses. However, studies incorporating conserved S2 regions are needed to more fully assess cross-reactivity.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.