ReviewRoyal Society open science2025
Application of remote sensing and geospatial technologies in predicting vector-borne disease outbreaks.
Review in Royal Society open science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Climate change and the rising threat of vector-borne diseases in the Andes.One health (Amsterdam, Netherlands) · 2026Review
- Artificial intelligence at the frontlines: Emerging infectious and parasitic diseases in the digital era.New microbes and new infections · 2026Article
- Investigation of chikungunya arbovirus in cities of Bushehr province: An ELISA-based cross-sectional study.Virus research · 2026Article
- Identification of biomedical entities from multiple repositories using a specialized metadata schema and search-augmented large language models.BMC research notes · 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
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Vector-borne diseases (VBDs) pose significant global health threats, particularly in tropical and subtropical regions. Remote sensing (RS) and geospatial technologies offer valuable tools for monitoring environmental changes and predicting disease transmission patterns, thereby supporting proactive public health interventions. This study reviews the application of RS and geospatial methods in the prediction, monitoring and control of VBDs. A systematic approach was employed to analyse existing literature, focusing on RS platforms such as Landsat, MODIS and Sentinel-2, alongside geographical information systems and machine learning models used for predictive modelling. The review reveals that these technologies play a crucial role in identifying environmental drivers of disease dynamics, including temperature, precipitation and land-use changes. However, challenges remain in terms of data resolution, model generalizability and the integration of socio-economic factors into predictive frameworks. The integration of early warning systems and participatory surveillance is highlighted as a promising avenue for improving disease forecasting. The study emphasizes the need for enhanced data accessibility, cross-sector collaboration and the inclusion of socio-economic variables in future research to improve the scalability and accuracy of disease prediction models.
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.