An Integration of Eco-Health One-Health Transdisciplinary Approach and Bayesian Belief Network

Abstract

Animal health economics is becoming increasingly important as the assistance for decision making on animal health intervention at all levels in attempting to optimize animal health management. Economic analysis of the optimal control of zoonoses associated with livestock production is complex as it depends on the nature of occurrence, transmission, and circulation of the diseases. Recent studies show that the emphasis of most of the veterinary economists is usually on the practical field of the economic evaluation of animal diseases based on a detailed knowledge of the production system. However, the field had not yet begun to address the more complex and real-world problems such as cause of emerging diseases. This empirical research employs a more holistic approach such as that advocated by the Eco-Health One-Health approach, together with the transdisciplinary analytical framework and Bayesian Belief Network analysis that integrates uncertainties into consideration to explain Trichinellosis risk. This fundamental research found that the Bayesian Belief Network modeling for the analysis of zoonoses risk and a combined human and animal health framework can be used to guide decision making for interventions to solve the Eco-Health One-Health problem of Trichinellosis risk. However, the scoring rule results from Netica, an easy to use software for working with Bayesian Belief Network, provide only symmetric loss values based on the assumption that the loss from misestimating is the same in any direction. Nonetheless, this assumption may not be valid in some practical situations such as what we are interested in this research, Trichinellosis risk. The research suggests an approach that takes the idea of decision theory combining the cost of collecting a sample to minimize the pre-posterior expected cost. If the sampling cost of collecting data is very high, or if there is strong prior information about the risk, it is not worth sampling. Also, if the loss of illness is very high, a thorough protection strategy would be more efficient.KeywordsLoss FunctionAnimal HealthBayesian Belief NetworkQuadratic Loss FunctionAmerican Veterinary Medical AssociationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.