ArticleScientific reports2020
A decision support framework for prediction of avian influenza.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Quantitative risk assessment of avian influenza: A scoping review.Infectious Disease Modelling · 2026Review
- Strategies to support implementation of infectious disease decision support systems: a scoping review.BMC medical informatics and decision making · 2026Article
- Applications and Considerations of Artificial Intelligence in Veterinary Sciences: A Narrative Review.Veterinary medicine and science · 2025Review
- Mapping of stakeholders in avian influenza surveillance in Canada.One health outlook · 2025Article
- Using an adaptive modeling framework to identify avian influenza spillover risk at the wild-domestic interface.Scientific reports · 2024Article
- Identification of Pre-Emptive Biosecurity Zone Areas for Highly Pathogenic Avian Influenza Based on Machine Learning-Driven Risk Analysis.Animals : an open access journal from MDPI · 2023Article
- Computational Method for Classification of Avian Influenza A Virus Using DNA Sequence Information and Physicochemical Properties.Frontiers in genetics · 2021Article
- Prediction of COVID-19 Waves Using Social Media and Google Search: A Case Study of the US and Canada.Frontiers in public healthArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
For years, avian influenza has influenced economies and human health around the world. The emergence and spread of avian influenza virus have been uncertain and sudden. The virus is likely to spread through several pathways such as poultry transportation and wild bird migration. The complicated and global spread of avian influenza calls for surveillance tools for timely and reliable prediction of disease events. These tools can increase situational awareness and lead to faster reaction to events. Here, we aimed to design and evaluate a decision support framework that aids decision makers by answering their questions regarding the future risk of events at various geographical scales. Risk patterns were driven from pre-built components and combined in a knowledge base. Subsequently, questions were answered by direct queries on the knowledge base or through a built-in algorithm. The evaluation of the system in detecting events resulted in average sensitivity and specificity of 69.70% and 85.50%, respectively. The presented framework here can support health care authorities by providing them with an opportunity for early control of emergency situations.
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Registered trials
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