Objectives matter: A mathematical modelling framework to identify optimal control strategies for future infectious disease outbreaks

目标很重要：数学模型框架可确定未来传染病爆发的最佳控制策略

• 批准号: 2737654
• 金额: --
• 依托单位: University of Warwick
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2737654
• 关键词: Objectives; matter; mathematical; modelling; framework

When deciding upon the optimal control intervention during an epidemic, a policy maker might seek to select the strategy that minimises a defined epidemic metric such as cost, duration or the number of severe disease cases. It is most likely, however, that the policy maker in question is required to consider multiple competing objectives which complicates the identification of the optimal strategy. The decision is further complicated by uncertainty, especially if the pathogen is novel and disease-specific parameters or the efficacy of interventions are unknown. The policy maker could therefore benefit from a framework which can recommend control strategies for future infectious disease outbreaks, subject to a cost function which includes multiple objectives. This project aims to develop a mathematical framework which uses a suitable objective function to explore optimisation of control interventions for infectious disease outbreaks. The project will use the framework to investigate how optimal intervention policies are dependent upon the spatiotemporal state of the outbreak, the characteristics of the disease and, crucially, the objective of policy makers when implementing such policies. This framework will be initially applied to models of respiratory virus outbreaks in humans of global significance, such as seasonal influenza. Time permitting, the framework will be extended to consider animal or plant diseases which may require an alternate modelling approach and a different class of control interventions. The project will rely upon the development and simulation of various models ofinfectious disease outbreaks whose projections will be used to measure the objective function against. The models must be developed to allow for flexible implementation of feasible control interventions and will be fitted to data (either provided by the external partner or publicly available through previous publications and/or online public health dashboards) using statistical inference, or incorporate plausible uncertainty in disease parameters such as the transmission rate to investigate changes in the objective function. We will develop a suite of models that can be used in a range of different futureinfectious disease outbreaks, including considering stochastic models and spatially explicit models. In the case of respiratory diseases, considering a spatially explicit model adds a layer of realism to the modelling as restrictions during the recent SARS-CoV-2 (COVID-19) pandemic were often localised. This can assist the policy maker in identifying vulnerable administrative divisions and taking targeted action. In the context of plant and animal diseases, the spatial heterogeneities are essential for model accuracy as the population can no longer be considered to be well-mixed.

当决定在流行病期间的最佳控制干预时，政策制定者可能会寻求选择使定义的流行病度量（如成本，持续时间或严重疾病病例的数量）最小化的策略。然而，最有可能的情况是，决策者需要考虑多个相互竞争的目标，这使得确定最优战略变得复杂。不确定性使决策更加复杂，特别是如果病原体是新的，疾病特异性参数或干预措施的效力未知。因此，政策制定者可以受益于一个框架，可以建议控制战略，为未来的传染病爆发，成本函数，其中包括多个目标。本项目旨在开发一个数学框架，使用合适的目标函数来探索传染病爆发控制干预的优化。该项目将利用该框架调查最佳干预政策如何取决于疫情的时空状态、疾病的特点，以及至关重要的是，决策者在执行此类政策时的目标。这一框架将首先应用于具有全球意义的人类呼吸道病毒暴发模型，如季节性流感。如果时间允许，将扩大该框架，以考虑可能需要另一种建模方法和不同类别的控制干预措施的动物或植物疾病。该项目将依赖于各种传染病爆发模型的开发和模拟，其预测将用于衡量目标函数。开发的模型必须允许灵活实施可行的控制干预措施，并将使用统计推断来拟合数据（由外部合作伙伴提供或通过以前的出版物和/或在线公共卫生仪表板公开提供），或纳入疾病参数（如传播率）中的合理不确定性，以调查目标函数的变化。我们将开发一套模型，可用于一系列不同的未来传染病爆发，包括考虑随机模型和空间显式模型。在呼吸道疾病的情况下，考虑空间显式模型为建模增加了一层现实感，因为最近SARS-CoV-2（COVID-19）大流行期间的限制通常是局部的。这可以帮助决策者确定脆弱的行政区划，并采取有针对性的行动。在植物和动物疾病的背景下，空间异质性对于模型的准确性至关重要，因为人口不再被认为是混合良好的。