Smart and scalable epidemic prediction and control

智能、可扩展的疫情预测和控制

基本信息

批准号：
MR/W016834/1
负责人：
金额：
$ 165.49万
依托单位：
Imperial College London
依托单位国家：
英国
项目类别：
Fellowship
财政年份：
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=MR%2FW016834%2F1
关键词：
Smart scalable epidemic prediction control

项目摘要

Novel infectious diseases, such as SARS and COVID-19, are pre-eminent threats to public health. In the absence of vaccines or prior immunity and the presence of large uncertainties surrounding the characteristics of these diseases when they emerge, models form our first line of defence. Mathematical models are computational tools that combine our knowledge of how diseases spread with available epidemic data, for example daily counts of cases. Models can provide understanding of the key factors driving transmission, forecasts of upcoming cases or deaths and estimates of the potential impact of non-pharmaceutical interventions (NPIs), such as social distancing or lockdowns. Given this predictive power, model outputs often serve as evidence for policymaking. However, the reliability of this evidence can depend substantially and sometimes unexpectedly on the scale or level of detail of the model. Epidemics are complex phenomena, involving differences, called heterogeneities, in spread across geographic, demographic and other scales. Fine scale models simulating all of these heterogeneities may yield unreliable forecasts because we may only have scarce data on each difference and have to make more assumptions to use the model. Coarse models, which average these heterogeneities over an entire country or ignore differences due to age-based risks, may be easier to use but overconfident and only able to evidence blunt NPIs such as lockdowns. Selecting the right scale at which to model and respond to infectious diseases is a problem at the forefront of epidemiology. Getting this scale wrong could misinform policy, making pandemic response risky, costly and ineffective.Two main issues make this model selection problem fundamentally challenging. First, the most reliable scale for modelling (e.g. locally, regionally or nationally) varies with location, time and response to NPIs. Existing models rarely adapt to this fluctuation or when they do tend to become very complex. Second, noisy data, logistical constraints on policy (e.g. school closures may only occur district-wide) and the reactive behaviours of people impose largely unknown performance limits on models, restricting the horizons of forecasts or efficiency of NPIs. I will develop smart models to resolve these issues. By connecting simple epidemic models into hierarchies or groups, where each lower-level group depicts some heterogeneity of interest and each higher one averages over that heterogeneity, I aim to construct novel models that realistically describe the many interacting scales of pandemics. Information theory and decentralised control theory are engineering fields that offer unique and rigorous ways of mitigating uncertainty and managing reactive loops that are seldom used in epidemiology. By combining principles from these fields together with expert input from policy scientists, I will design new algorithms that restructure these hierarchies to expose and bypass performance limits, and to pinpoint the most reliable scales for practically combatting pandemics at any time. These smart frameworks, which intelligently balance the details of spread with the available data to reliably learn about those details, will push the boundaries of epidemic modelling. Applying them to diverse SARS and COVID-19 datasets, I will (i) derive robust early-warning indicators of transmission (e.g. signs that foretell if an epidemic might have a second wave), (ii) improve understanding of how limits to modelling translate into restrictions on how well we can predict or control outbreaks and (iii) derive new strategies for coordinating NPIs across different scales to improve the efficiency of future pandemic response (e.g. discovering when combinations of local lockdowns might be more effective than a national one). Smart models, which adapt to the changing reality of pandemics, can solidify the evidence base for reliable and better-informed public health policy.

SARS 和 COVID-19 等新型传染病是对公共健康的首要威胁。在缺乏疫苗或事先免疫的情况下，并且这些疾病出现时其特征存在很大的不确定性，模型构成了我们的第一道防线。数学模型是计算工具，它将我们对疾病如何传播的知识与可用的流行病数据（例如每日病例数）相结合。模型可以帮助您了解推动传播的关键因素、预测即将发生的病例或死亡，以及估计社交距离或封锁等非药物干预措施 (NPI) 的潜在影响。鉴于这种预测能力，模型输出通常可以作为决策的证据。然而，该证据的可靠性很大程度上取决于模型的规模或详细程度，有时甚至是意想不到的。流行病是复杂的现象，涉及在地理、人口和其他尺度上传播的差异，称为异质性。模拟所有这些异质性的精细尺度模型可能会产生不可靠的预测，因为我们可能只有关于每个差异的稀缺数据，并且必须做出更多假设才能使用该模型。粗略模型对整个国家的异质性进行平均，或忽略因年龄风险而产生的差异，可能更容易使用，但过于自信，并且只能证明诸如封锁之类的钝性 NPI。选择正确的尺度来建模和应对传染病是流行病学的前沿问题。如果这个规模错误，可能会误导政策，使流行病应对变得危险、成本高昂且无效。有两个主要问题使这个模型选择问题从根本上具有挑战性。首先，最可靠的建模尺度（例如本地、区域或国家）随地点、时间和对 NPI 的响应而变化。现有模型很少适应这种波动，或者当它们适应时往往会变得非常复杂。其次，嘈杂的数据、政策的后勤限制（例如，学校关闭可能只发生在学区范围内）以及人们的反应行为对模型施加了很大程度上未知的性能限制，限制了非营利机构的预测范围或效率。我将开发智能模型来解决这些问题。通过将简单的流行病模型连接成层次结构或组，其中每个较低级别的组描述了一些感兴趣的异质性，而每个较高级别的组对该异质性进行平均，我的目标是构建能够真实描述流行病的许多相互作用规模的新模型。信息论和分散控制理论是工程领域，提供了独特而严格的方法来减轻不确定性和管理反应循环，这些方法在流行病学中很少使用。通过将这些领域的原理与政策科学家的专家意见相结合，我将设计新的算法来重组这些层次结构，以暴露和绕过性能限制，并随时确定实际抗击流行病的最可靠的规模。这些智能框架可以智能地平衡传播细节与可用数据，以可靠地了解这些细节，将突破流行病建模的界限。将它们应用于不同的 SARS 和 COVID-19 数据集，我将 (i) 得出可靠的传播预警指标（例如，预示流行病是否可能出现第二波的迹象），(ii) 加深对建模限制如何转化的理解限制我们预测或控制疫情的能力，以及 (iii) 制定新的策略来协调不同规模的 NPI，以提高未来大流行应对的效率（例如，发现何时局部封锁的组合可能比局部封锁的组合更有效）国家级的）。适应流行病不断变化的现实的智能模型可以巩固证据基础，以制定可靠且信息更丰富的公共卫生政策。