Establishing a Surveillance, Early Warning, and Forecasting System for Avian Influenza Outbreaks скачать в хорошем качестве

Establishing a Surveillance, Early Warning, and Forecasting System for Avian Influenza Outbreaks

Beginning in November 2021, Canada has repeatedly contended with outbreaks of the H5N1 strain of avian influenza, impacting both domestic poultry and wild bird populations. These outbreaks have not only posed public health risks but also caused widespread disruption and significant economic damage within the bird farming industry. While existing surveillance techniques, such as environmental sampling from water, soil, air, and bird enclosures, are standard, they are often labor-intensive and expensive, limiting timely response. To overcome these challenges, this study introduces a new digital-based surveillance approach and Early Warning System (EWS) that harnesses publicly available web data to track and anticipate avian influenza outbreaks with greater speed and efficiency. The system analyzes multiple online data sources, including weather and air quality reports, media coverage, social media trends, and Google search patterns, that show strong associations with outbreak events. By applying deep learning methods such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models, this framework predicts potential outbreaks across Canada, both nationally and regionally. This data-driven strategy is intended to aid regulatory agencies, poultry producers, and public health officials in taking pre-emptive measures, ultimately aiming to minimize the spread of the virus and reduce the risk of it crossing over to humans or other animals.