ArticlePloS one2025
Oropouche fever outbreak in Brazil: Key factors behind the largest epidemic in history.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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
3 citing papers in PubMed.
- Oropouche Virus (OROV) Vaccine Development for Vulnerable Populations: Epidemiological Context, Challenges and Future Directions.Vaccines · 2026Review
- Arrival of Oropouche Virus in a Nonendemic Area in Northeastern Brazil, 2024.Journal of medical virology · 2026Article
- Oropouche Virus: An Emerging Arboviral Threat and Its Implications for Europe.Life (Basel, Switzerland) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Oropouche virus (OROV) is an arthropod-borne virus responsible for outbreaks of Oropouche fever (ORO) in Central and South America since the 1950s. Herein, we investigated the climatic and socioenvironmental factors contributing to the reemergence of ORO in Brazil in 2024, culminating in the largest epidemic in the country's history. Accordingly, we conducted a modeling study to identify areas with the highest incidence of OROV in Brazil based on confirmed human cases between the 2020 and 2024 outbreaks and socioenvironmental variables. Our analysis utilized Maxent software, a machine learning algorithm for species distribution modeling, along with SatScan software to identify high- and low-risk spatial clusters. A total of 8,258 ORO cases were serologically confirmed in Brazil in 2024 and 108 in 2020/2021. The distribution of OROV differed markedly in 2020 and 2024: in 2020, cases were primarily confined to the Amazon region, while in 2024, cases expanded across nearly the entire country. High-risk areas showed higher temperatures and precipitation, and land-cover and land-use change (LCLUC) appeared to be key factors in ORO distribution. Upon comparing deforestation rates between 2020 and 2023, we noted a sharper increase in the expansion of pasture cover and soybean plantations in high-risk regions. Moreover, municipalities in high-risk clusters tended to face greater socioeconomic challenges, including poverty and restricted access to healthcare. Our study identified areas vulnerable to OROV circulation, providing valuable insights to support the establishment of robust public health policies that must be prioritized and strengthened in the context of climate change.
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.