Serological and viral prevalence of Oropouche virus (OROV): A systematic review and meta-analysis from 2000-2024 including human, animal, and vector surveillance studies
Bibliographic record
Abstract
Abstract Background Oropouche virus (OROV) is an emerging arbovirus primarily transmitted by biting midges and is increasingly recognized as a public health threat in Central and South America. With over 11,000 confirmed cases reported in 2024, a ten-fold increase from the previous year, its transmission dynamics and true burden remain poorly understood due to diagnostic challenges and fragmented surveillance systems. Objective This systematic review and meta-analysis (SRMA) synthesizes OROV prevalence data in humans and summarizes the available data for vectors and non-human hosts sampled between 2000 and 2024 to provide updated estimates and identify key surveillance gaps. Methods We systematically searched Web of Science, PubMed, Embase, Medline, and LILACS for OROV seroprevalence and viral prevalence studies in human, insect, and animal populations, published up to September 12, 2024. The review protocol was registered with PROSPERO (CRD42024551000). Studies were extracted in duplicate, and data were meta-analyzed using generalized linear mixed-effects models. Risk of bias was appraised using a modified Joanna Briggs Institute checklist. Results We included 71 articles reporting serological or viral prevalence of OROV across nine countries. Between 2000–2024, pooled human seroprevalence among individuals with febrile illness or suspected of Oropouche infection was 12.6% [95% CI 5.3-26.9%] across four South American countries and seroprevalence of 1.1% [95% CI 0.5-2.3%] was observed in asymptomatic groups. Viral prevalence among individuals with febrile illness or suspected of Oropouche infection was 1.5% [0.8-3.0%] across seven South American countries and Haiti. Most studies used convenience sampling and RT-PCR or hemagglutination assays. In vector populations, positive OROV prevalence in Aedes aegypti and Culex quinquefasciatus was reported in two of 18 sources, while 10.0% and 7.5% animal host prevalence was reported in dogs and cattle, respectively. We found high risk of bias in 11.3% of studies in our critical appraisal, with most animal, human, and vector studies falling in the moderate risk of bias range. Conclusions Despite rising numbers of OROV reported cases, prevalence estimates remain limited by sparse surveillance and variable methodology. This review highlights the urgent need for standardized serological assays, community-based studies, and expanded surveillance in animal and vector reservoirs. A One Health approach is essential to monitor OROV transmission and inform regional preparedness efforts. Author summary Oropouche virus is a pathogen carried by biting midges and some other insect vectors that causes fever and flu-like illness in people, and it can also be carried by animals. Cases of Oropouche virus have been rising in Central and South America, with approximately 11,000 confirmed cases in 2024 – a ten-times increase from the previous year. Despite the growing outbreaks, we still know little about how many people and animals have been infected; studies testing at-risk populations for viral infection or the presence of antibodies are valuable tools in filling these knowledge gaps. To address this, we reviewed such studies published from 2000 to 2024 that tested people, animals, and insects for Oropouche virus, and produced estimates of the virus’ prevalence in these populations to better understand its spread and detection. We found that in humans with suspected infection in four South American countries, i.e. with fever and related symptoms, 12.6% of them had antibodies for Oropouche – indicating a history of infection. The prevalence of active viral infections in similar populations was 1.5%. In asymptomatic people, the prevalence of antibodies was lower, at 1.1%. We also found small presence of the virus in insect vectors and animals, namely dogs and cattle. Overall, the studies had varying methods especially with regards to diagnostic tests, and the number of studies is still very limited, especially in animals and insects. Our findings highlight the urgent need for better tools, standardized research methods, and an increase in community surveillance studies across species to better understand Oropouche, prevent future outbreaks, and best respond to the health needs of affected communities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.037 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".