ArticleScientific reports2023
Predicting Kyasanur forest disease in resource-limited settings using event-based surveillance and transfer learning.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed.
- From Reactive to Predictive One Health: AI-Enabled Frameworks for Integrated Zoonotic Surveillance and Governance.International journal of environmental research and public health · 2026Article
- Biosurveillance and early outbreak detection of rabies in settings with limited laboratory capacity using spatiotemporal clustering and a machine learning framework.Scientific reports · 2026Article
- Challenges in combating tick-borne disease transmissions in India: Emphasizing the importance of a one-health approach.One health (Amsterdam, Netherlands) · 2025Review
- Pathogenicity and virulence of Kyasanur Forest disease: A comprehensive review of an expanding zoonotic threat in southwestern India.Virulence · 2025Review
- Meeting Global Health Needs via Infectious Disease Forecasting: Development of a Reliable Data-Driven Framework.JMIR public health and surveillance · 2025Article
- First evidence of transovarial transmission of Kyasanur Forest disease virus in Haemaphysalis and Rhipicephalus ticks in the wild.Parasites & vectors · 2025Article
- Article
- Machine learning to improve the understanding of rabies epidemiology in low surveillance settings.Scientific reports · 2024Article
- From COVID-19 to monkeypox: a novel predictive model for emerging infectious diseases.BioData mining · 2024Article
- Review
- Computational Exploration of Potential Pharmacological Inhibitors Targeting the Envelope Protein of the Kyasanur Forest Disease Virus.Pharmaceuticals (Basel, Switzerland) · 2024Article
- Kyasanur Forest Disease: A Comprehensive Review.Cureus · 2024Review
- Sociodemographic factors associated with Kyasanur forest disease in India - a retrospective study.IJID regions · 2024Article
- Kyasanur Forest Disease: An Epidemiological Investigation and Case-Control Study in Shivamogga, Karnataka, India-2022.International journal of public health · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, the reports of Kyasanur forest disease (KFD) breaking endemic barriers by spreading to new regions and crossing state boundaries is alarming. Effective disease surveillance and reporting systems are lacking for this emerging zoonosis, hence hindering control and prevention efforts. We compared time-series models using weather data with and without Event-Based Surveillance (EBS) information, i.e., news media reports and internet search trends, to predict monthly KFD cases in humans. We fitted Extreme Gradient Boosting (XGB) and Long Short Term Memory models at the national and regional levels. We utilized the rich epidemiological data from endemic regions by applying Transfer Learning (TL) techniques to predict KFD cases in new outbreak regions where disease surveillance information was scarce. Overall, the inclusion of EBS data, in addition to the weather data, substantially increased the prediction performance across all models. The XGB method produced the best predictions at the national and regional levels. The TL techniques outperformed baseline models in predicting KFD in new outbreak regions. Novel sources of data and advanced machine-learning approaches, e.g., EBS and TL, show great potential towards increasing disease prediction capabilities in data-scarce scenarios and/or resource-limited settings, for better-informed decisions in the face of emerging zoonotic threats.
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.