| Resumen |
Globally, there is an increase in the risk and threats related to high-impact and rapidly spreading animal health diseases. An outbreak in any region of the world can represent a threat to agricultural health and food security, due to its potential impact on the economy, public health and the natural environment. Therefore, one of the main goals of proactive animal health authorities in every nation, including Mexico, is to implement epidemiological intelligence strategies. These strategies include the timely detection, verification and dissemination of signals on the occurrence of diseases, based on both formal and informal sources of information available on the Internet. In this context, the development of AHTRISM (Animal Health Threat Reporting and Information System for Mexico) is introduced. This system specializes in searching, identifying, classifying, analyzing and selecting news from open sources in the web in an automated and organized manner. Its design allows an agile query of unstructured information on possible threats to animal health in Mexico. The whole process includes the use of keywords to locate documents and the application of machine learning (ML) algorithms and natural language processing (NLP) techniques to clean and classify the news, and also to extract relevant data from these. © 2025 SPIE. |
