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FREMONT, CA: Leveraging artificial intelligence (AI) methodologies in animal health (AH) helps addressing highly complicated problems, such as those encountered in quantitative and predictive epidemiology, animal or human precision-based medicine, and the investigation of host-pathogen interactions. AI may contribute to diagnosis and disease case detection, to more reliable predictions and reduced errors, to representing more realistically complex biological systems, and to making computing codes more readable for non-computer scientists. It helps accelerate decisions and enhances the accuracy of risk assessments, better-targeted interventions, and anticipated adverse effects.
Due to the peculiarities of AH systems, data, and analytical objectives, AI research may be stimulated by AH difficulties. With the emergence of several current concepts encouraging a global and multispectral viewpoint in the field of health, AI should contribute to the defracturing of the various disciplines in AH in the direction of more transversal and integrative research. The quality and availability of data at the different organizational levels of living systems and diverse geographical and temporal scales remain a focal topic of AH research. Various types of information are of interest.
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AI helps us understand animal epidemiological systems
AI-based technologies have made collecting, storing, and distributing large amounts of data more accessible, necessitating better data analysis. Computer scientists used exponential advances in computing power to develop AI approaches to meet these needs. In recent decades, statistical methods have also advanced in dimensionality reduction, variable selection, model comparison, and combination.
AI approaches often detect signals, patterns, or features (density-dependence in vector-borne transmission) that conventional statistical methods cannot. It helps pathogen transmission in complex system networks, typical of emerging illnesses in tropical, developing settings. Developing interfaces and training with under-resourced countries will enable synergistic effects and measures to predict and combat future disease risks.
The Animal Health mechanistic model provides reliability, repeatability, and flexibility
Understanding and predicting disease propagation requires explicit and comprehensive modeling of the mechanisms involved in AH system dynamics, regardless of scale along a primary production chain.
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