ArticleScientific data2026
Epidemiological and digital syndromic surveillance data on dengue, chikungunya, and SARI in Brazil.
Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- The role of health institutions in the epidemiological surveillance of arboviral diseases in Rio Grande do Sul, Brazil: notification, hospitalization, and regional differences.BMC public health · 2026Article
- Epidemiological and digital syndromic surveillance data on dengue, chikungunya, and SARI in Brazil.Scientific data · 2026Article
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
Vector-borne diseases, such as dengue and chikungunya, along with air-borne diseases like influenza and COVID-19, are prone to epidemics, which increases the demand for real-time outbreak monitoring. Developing such systems requires harmonized datasets for calibration and validation. In this study, we created a dataset containing official disease notifications for dengue, chikungunya, and severe acute respiratory infections (SARI) across Brazilian states for over a decade. The dataset integrates Google Trends search data for each disease and all associated symptoms, organized by the corresponding epidemiological week. By providing this integrated resource, we aim to encourage the use of alternative online data to explore associations between digital search behavior and official disease incidence, thereby supporting the development of nowcasting models for early outbreak detection to inform timely public health responses and decision-making.
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.