Building an epidemiological modelling toolkit for epidemic preparedness

构建流行病学建模工具包以做好流行病防范

• 批准号: MR/Z503939/1
• 负责人: Simon Frost
• 金额: $ 67.29万
• 依托单位: London School of Hygiene & Tropical Medicine
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FZ503939%2F1
• 关键词: Building; epidemiological; modelling; toolkit; epidemic

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.

该提案旨在通过将高级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如何预测和管理传染病。认识到COVID-19大流行的当前模型中的局限性，例如延迟的响应时间和对新疾病的挑战的挑战，该项目提出了一种新的方法。它旨在为建立传播模型创建灵活的透明工具包，该工具包可以快速适应新信息，从而实现健康危机期间的实时决策。该方法将应用类别理论（ACT）与操作模型结合在一起，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方式来描述复杂系统的数学框架，例如一种用于了解系统的不同部分如何融合和交互的语言，使其对于在包括流行病学在内的各个领域建立和分析模型特别有用。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访问和适应性，从而促进一个可以快速针对特定疾病或场景量身定制的模型的环境。此外，该研究探讨了这些模型与决策过程的整合，承认预测疾病蔓延和干预措施的有效性固有的不确定性。它探讨了这种不确定性下的优化决策，旨在为公共卫生策略提供强有力的支持。这项研究的应用将通过麻疹和SARS-COV-2的模型来证明，SARS-COV-2的模型是导致Covid-19的病毒，展示了Toolkit复制现有模型的能力，并创建了可以告知政策决策的新模型。这些演示将受益于有关这些感染传输动态的大型详细数据集，并将在可信赖的研究环境中运行，在该环境中，可以在模型中以安全，安全的方式在模型中使用详细的流行病学信息。通过研讨会和基础软件的开源分配，该项目旨在授权建模者，政策制定者和研究人员，增强对未来大流行的准备。该倡议不仅可以发展流行病学建模领域，而且还为公共卫生威胁的更加知情和灵活的反应做出了贡献，从而通过实现迅速的，基于证据的行动来挽救生命和资源。