Discovering topics and trends in biosecurity law research: A machine learning approach - Yang Liu (One Health)
This study employed machine learning techniques, specifically Latent Dirichlet Allocation (LDA), to analyze 559 articles on biosecurity legislation from 1996 to 2023. The LDA model identified nine key research topics, including Agricultural Management and Production, Biosafety and Environmental Impact, Biological Invasion and Regulation, Biosecurity Legislation and Prevention, Agriculture and Environmental Relations, Virus Infection and Governance, Health Risk Assessment and Detection, Disease Prevention and Biotechnology, and Policy Control and Research. The findings reveal significant trends: an increasing focus on Biosecurity Legislation and Prevention and a declining interest in Agricultural Management and Production. Geographically, Australia, Canada, and the United States lead in biosecurity research, exhibiting diverse research topics. Journal-level analysis highlights central topics such as Agricultural Management and Production, Biosecurity Legislation and Prevention, and Health Risk Assessment and Detection. This study's use of LDA reduces subjective bias, providing a more objective analysis of global biosecurity legislation literature. The research underscores the importance of expanding geographical scope, integrating advanced machine learning models, adopting interdisciplinary approaches, and assessing policy impacts to enhance biosecurity strategies globally.