Process Review for Development of Quantitative Risk Analyses for Transboundary Animal Disease to Pathogen-Free Territories.

Abstract

Outbreaks of transboundary animal diseases (TADs) have the potential to cause significant detriment to animal, human, and environmental health; severe economic implications; and national security. Challenges concerning data sharing, model development, decision support, and disease emergence science have recently been promoted. These challenges and recommendations have been recognized and advocated in the disciplines intersecting with outbreak prediction and forecast modeling regarding infectious diseases. To advance the effective application of computation and risk communication, analytical products ought to follow a collaboratively agreed common plan for implementation. Research articles should seek to inform and assist prioritization of national and international strategies in developing established criteria to identify and follow best practice standards to assess risk model attributes and performance. A well-defined framework to help eliminate gaps in policy, process, and planning knowledge areas would help alleviate the intense need for the formation of a comprehensive strategy for countering TAD outbreak risks. A quantitative assessment that accurately captures the risk of introduction of a TAD through various pathways can be a powerful tool in guiding where government, academic, and industry resources ought to be allocated, whether implementation of additional risk management solutions is merited, and where research efforts should be directed to minimize risk. This review outlines a part of a process for the development of quantitative risk analysis to collect, analyze, and communicate this knowledge. A more comprehensive and unabridged manual was also developed. The framework used in supporting the application of aligning computational tools for readiness continues our approach to apply a preparedness mindset to challenges concerning threats to global biosecurity, secure food systems, and risk-mitigated agricultural economies.