Abstract：
Epidemiological modeling is a key lever for infectious disease control and prevention on farms. It makes it possible to understand the spread of pathogens, but also to compare intervention scenarios even in counterfactual situations. However, the actual capability of decision makers to use mechanistic models to support timely interventions is limited. This study demonstrates how artificial intelligence (AI) techniques can make mechanistic epidemiological models more accessible to farmers and veterinarians, and how to transform such models into user-friendly decision-support tools (DST). By leveraging knowledge representation methods, such as the textual formalization of model components through a domain-specific language (DSL), the co-design of mechanistic models and DST becomes more efficient and collaborative. This facilitates the integration of explicit expert knowledge and practical insights into the modeling process. Furthermore, the utilization of AI and software engineering enables the automation of web application generation based on existing mechanistic models. This automation simplifies the development of DST, as tool designers can focus on identifying users\' needs and specifying expected features and meaningful presentations of outcomes, instead of wasting time in writing code to wrap models into web apps. To illustrate the practical application of this approach, we consider the example of Bovine Respiratory Disease (BRD), a tough challenge in fattening farms where young beef bulls often develop BRD shortly after being allocated into pens. BRD is a multi-factorial, multi-pathogen disease that is difficult to anticipate and control, often resulting in the massive use of antimicrobials to mitigate its impact on animal health, welfare, and economic losses. The DST developed from an existing mechanistic BRD model empowers users, including farmers and veterinarians, to customize scenarios based on their specific farm conditions. It enables them to anticipate the effects of various pathogens, compare the epidemiological and economic outcomes associated with different farming practices, and decide how to balance the reduction of disease impact and the reduction of antimicrobial usage (AMU). The generic method presented in this article illustrates the potential of artificial intelligence (AI) and software engineering methods to enhance the co-creation of DST based on mechanistic models in veterinary epidemiology. The corresponding pipeline is distributed as an open-source software. By leveraging these advancements, this research aims to bridge the gap between theoretical models and the practical usage of their outcomes on the field.
摘要：
流行病学模型是农场传染病控制和预防的关键杠杆。它使了解病原体的传播成为可能，而且即使在反事实的情况下也要比较干预方案。然而,决策者使用机械模型来支持及时干预的实际能力是有限的。这项研究展示了人工智能（AI）技术如何使农民和兽医更容易获得机械流行病学模型。以及如何将此类模型转换为用户友好的决策支持工具（DST）。通过利用知识表示方法，例如通过领域特定语言(DSL)对模型组件进行文本形式化，机械模型和DST的共同设计变得更加高效和协作。这有助于将明确的专家知识和实际见解集成到建模过程中。此外，利用人工智能和软件工程可以基于现有的机械模型实现Web应用程序生成的自动化。这种自动化简化了DST的开发，因为工具设计者可以专注于识别用户的需求，并指定预期的功能和有意义的结果呈现，而不是浪费时间编写代码将模型包装到Web应用程序中。为了说明这种方法的实际应用，我们考虑牛呼吸道疾病（BRD）的例子，在育肥场，年轻的牛牛经常在被分配到围栏后不久就发展出BRD。BRD是一个多因素的，难以预测和控制的多病原体疾病，经常导致大量使用抗菌剂来减轻其对动物健康的影响，福利,和经济损失。从现有的机械BRD模型开发的DST赋予用户权力，包括农民和兽医，根据其特定的场条件自定义方案。它使他们能够预测各种病原体的影响，比较与不同耕作方式相关的流行病学和经济结果，并决定如何平衡减少疾病影响和减少抗菌药物使用（AMU）。本文介绍的通用方法说明了人工智能（AI）和软件工程方法在兽医流行病学中基于机械模型增强DST共同创建的潜力。相应的管道作为开源软件分发。通过利用这些进步，这项研究旨在弥合理论模型和实际使用他们的结果在现场之间的差距。
