Minimum Models for Optimal Epidemic Monitoring and Control

数学模型已成为公共卫生不可或缺的。传染病模型通过模拟和提供对疾病传播的洞察，将观察到的流行病数据转换为预测的爆发反应。现实的预测使决策者能够在实地试验之前有效和经济地评估不同干预措施的风险和收益。模型对于控制2014年非洲毁灭性的埃博拉疫情至关重要，例如，通过指导人员和资源的分配。然而，更精确地描述疾病传播的尝试导致了越来越复杂的模型。复杂的模型很难解释和验证，可能会掩盖流行病的假设。基于它们的政策可能是不可靠和过度自信的，导致资源管理不善，甚至死亡。在英国，这可能特别昂贵，因为建模直接为疫苗接种计划提供信息。事件触发控制是一个工程子领域，它推荐控制动作作为对信息事件（称为触发器）的响应。通过将监控与控制直接联系起来，它优先考虑可解释性并暴露固有的假设。最近的研究表明，将建模工作的重点重新放在疫情管理而不是疾病传播上，可以消除不必要的复杂性。事件触发控制可能正是完成这项工作的工具。埃博拉和流感是英国的两种优先疾病，其中模型的复杂性和不确定性是突出的问题。通过与英国公共卫生部和世界卫生组织的官员合作，确定重要的埃博拉和流感触发因素，我将推导出合理复杂性和可证明可靠性的模型。

Mathematical models have become indispensable to public health. Infectious-disease models convert observed epidemic data into predicted outbreak responses by simulating and providing insight into disease transmission. Realistic predictions allow policymakers to efficiently and economically evaluate the risks and benefits of different interventions before trial in the field. Models were crucial to controlling the devastating 2014 African Ebola outbreak, for example, by guiding the allocation of people and resources. However, attempts to describe disease transmission more precisely have led to increasingly complex models. Complex models are difficult to interpret and validate and may conceal epidemic assumptions. Policy based on them can be unreliable and overconfident, resulting in resource mismanagement and even mortality. This can be especially costly in the UK, where modelling directly informs vaccination programmes. Event-triggered control is an engineering subfield that recommends control actions as responses to informative events, called triggers. By directly linking monitoring to control, it prioritises interpretability and exposes innate assumptions. Recent research suggests that refocusing modelling efforts on outbreak management, instead of disease transmission, could remove unnecessary complexity. Event-triggered control may be precisely the tool for this job. Ebola and influenza are two priority diseases in the UK where model complexity and uncertainty are salient concerns. By working with officials at Public Health England and the World Health Organisation to identify important Ebola and influenza triggers, I will derive models of justifiable complexity and provable reliability.