Minimum Models for Optimal Epidemic Monitoring and Control

最佳流行病监测和控制的最小模型

• 批准号: MR/S019693/1
• 金额: $ 36.75万
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FS019693%2F1
• 关键词: Minimum; Models; Optimal; Epidemic; Monitoring

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.

数学模型已经成为公共健康不可或缺的一部分。传染病模型通过模拟和洞察疾病传播，将观察到的疫情数据转换为预测的暴发反应。现实的预测使政策制定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好处。模型对于控制2014年毁灭性的非洲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，试图更准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，并可能隐藏流行病的假设。基于它们的政策可能是不可靠和过于自信的，导致资源管理不善，甚至死亡。在英国，这可能特别昂贵，在那里，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字段，它建议控制操作作为对信息性事件的响应，称为触发器。通过直接将监测与控制联系起来，它优先考虑可解释性，并暴露了与生俱来的假设。最近的研究表明，将建模工作的重点重新放在疫情管理上，而不是疾病传播上，可能会消除不必要的复杂性。事件触发控件可能正是这项工作的工具。埃博拉和流感是英国的两种重点疾病，在英国，模型的复杂性和不确定性是突出的担忧。通过与英国公共卫生组织(Public Health England)和世界卫生组织(WHO)的官员合作，确定重要的埃博拉和流感诱因，我将得出合理的复杂性和可证明的可靠性模型。

项目成果

Tracking the emergence of disparities in the subnational spread of COVID-19 in Brazil using an online application for real-time data visualisation: A longitudinal analysis.

• DOI: 10.1016/j.lana.2021.100119
• 发表时间: 2022-01
• 期刊: Lancet regional health. Americas
• 作者: Mee P;Alexander N;Mayaud P;González FJC;Abbott S;Santos AAS;Acosta AL;Parag KV;Pereira RHM;Prete CA Jr;Sabino EC;Faria NR;LSHTM Centre for Mathematical Modelling of Infectious Disease COVID-19 working group;Brady OJ
• 通讯作者: Brady OJ

Are skyline plot-based demographic estimates overly dependent on smoothing prior assumptions?

基于天际线图的人口统计估计是否过度依赖于平滑先前的假设？

• DOI: 10.1101/2020.01.27.920215
• 发表时间: 2020
• 作者: Parag K

On Signalling and Estimation Limits for Molecular Birth-Processes

关于分子诞生过程的信号传导和估计极限

• DOI: 10.1101/319889
• 发表时间: 2018
• 作者: Parag K