Building an epidemiological modelling toolkit for epidemic preparedness

这项提议旨在通过将先进的数学方法应用于传染病传播模型的开发和分析来改变我们预测和管理传染病的方式。认识到新冠肺炎大流行暴露的当前模型的局限性，如反应时间延迟和使模型适应新疾病的挑战，该项目提出了一种新的方法。它的核心是创建一个灵活、透明的工具包，用于建立能够快速适应新信息的传播模型，从而能够在健康危机期间做出实时决策。方法将应用范畴理论(ACT)与操作建模相结合，以简化复杂疾病模型的构建和适应。ACT是一种以结构化和关系的方式描述复杂系统的数学框架，就像一种语言，用于了解系统的不同部分如何组合在一起并相互作用，使其对于建立和分析包括流行病学在内的各个领域的模型特别有用。通过将模型分解为可重复使用的组件，该项目旨在使建模过程更容易获得和更具适应性，从而培养一种环境，使模型可以快速适应特定疾病或情景。此外，该研究还探讨了将这些模型与决策过程相结合，承认在预测疾病传播和干预措施的有效性方面固有的不确定性。它探索在这种不确定性下优化决策，旨在为公共卫生战略提供强有力的支持。这项研究的应用将通过麻疹和导致新冠肺炎的SARS-CoV-2病毒的模型来演示，展示该工具包复制现有模型并创建能够为政策决策提供信息的新模型的能力。这些演示将受益于关于这些感染传播动态的大量和详细的数据集，并将在可信的研究环境中运行，在那里详细的流行病学信息可以安全、可靠的方式在模型中使用。通过研讨会和基础软件的开源分发，该项目寻求增强建模人员、政策制定者和研究人员的能力，加强对未来大流行的准备。这一举措不仅推进了流行病学建模领域，而且有助于对公共卫生威胁作出更知情和更灵活的反应，通过采取迅速、循证的行动，有可能挽救生命和资源。

This proposal aims to change how we predict and manage infectious diseases through the application of advanced mathematical methods to the development and analysis of models of infectious disease transmission. Recognizing limitations in current models exposed by the COVID-19 pandemic, such as delayed response times and challenges in adapting models to a new disease, this project proposes a novel approach. It centres on creating a flexible, transparent toolkit for building transmission models that can rapidly adapt to new information, enabling real-time decision-making during health crises.The methodology combines applied category theory (ACT) with operational modeling to simplify the construction and adaptation of complex disease models. ACT is a mathematical framework to describe complex systems in a structured and relational way, like a language for understanding how different parts of a system can fit together and interact, making it particularly useful for building and analyzing models in various fields, including epidemiology. By decomposing models into reusable components, the project intends to make the process of modeling more accessible and adaptable, fostering an environment where models can be quickly tailored to specific diseases or scenarios. Moreover, the research addresses the integration of these models with decision-making processes, acknowledging the uncertainty inherent in predicting disease spread and the effectiveness of interventions. It explores optimizing decisions under this uncertainty, aiming to provide robust support for public health strategies.Applications of this research will be demonstrated through models of measles and SARS-CoV-2, the virus that causes COVID-19, showcasing the toolkit's ability to replicate existing models and create new ones that can inform policy decisions. These demonstrations will benefit from a large and detailed dataset on the transmission dynamics of these infections, and will be run in a trusted research environment, where detailed epidemiological information can be used in the models in a safe, secure manner. Through workshops and open-source distribution of the underlying software, the project seeks to empower modelers, policymakers, and researchers, enhancing preparedness for future pandemics. This initiative not only advances the field of epidemiological modeling but also contributes to a more informed and flexible response to public health threats, potentially saving lives and resources by enabling swift, evidence-based action.