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.

新型的传染病，例如SARS和Covid-19，是对公共卫生的杰出威胁。在没有疫苗或先前的免疫力以及这些疾病出现时围绕这些疾病特征的大型不确定性的情况下，模型构成了我们的第一条防御线。数学模型是计算工具，可以结合我们对疾病如何传播与可用流行数据（例如每日病例计数）的了解。模型可以提供对驱动传播的关键因素的了解，对即将发生的案件或死亡的预测以及对非药物干预措施（NPI）的潜在影响（例如社会疏远或锁定）的潜在影响的估计。鉴于这种预测能力，模型输出通常是决策的证据。但是，该证据的可靠性可能会在很大程度上，有时出乎意料地取决于模型的细节规模或水平。流行病是复杂的现象，涉及差异，称为异质性，分布在地理，人口统计和其他量表中。模拟所有这些异质性的精细规模模型可能会产生不可靠的预测，因为我们可能只有在每个差异上都有稀缺的数据，并且必须做出更多的假设来使用该模型。粗糙的模型平均在整个国家中平均这些异质性或由于基于年龄的风险而忽略差异，可能更易于使用，但过度自信，只能证明诸如锁定的NPI。选择适当的对传染病进行建模和反应的规模是流行病学的最前沿的问题。弄错了这个规模可能会误导政策，使大流行的反应风险，代价高昂和无效。两个主要问题使该模型选择问题从根本上具有挑战性。首先，建模最可靠的量表（例如，本地，区域或全国范围内）随着位置，时间和对NPI的响应而异。现有模型很少适合这种波动，或者当它们确实变得非常复杂。其次，嘈杂的数据，政策的后勤限制（例如，封闭可能仅发生在范围内），以及人们的反应性行为对模型施加了很大的绩效限制，从而限制了NPI的预测或效率的视野。我将开发智能模型来解决这些问题。通过将简单的流行模型连接到层次结构或群体中，每个较低级别的群体都描绘了某些关注的异质性，并且每个较高的平均值都比该异质性，我旨在构建新型模型，这些新模型实际描述了许多大流传学的相互作用量表。信息理论和分散控制理论是工程领域，它们提供了减轻不确定性和管理反应性循环的独特而严格的方法，这些循环很少在流行病学中使用。通过将这些领域的原则与政策科学家的专家投入结合在一起，我将设计新的算法来重组这些层次结构以揭示和绕过绩效限制，并确定最可靠的量表，以便任何时间打击Pandemics。这些智能框架可以智能地平衡点差的细节和可靠的数据，以可靠地了解这些细节，这将推动流行性建模的界限。将它们应用于不同的SARS和COVID-19数据集中，我将（i）得出强大的早期表演指标（例如，如果流行病可能会有第二波），（ii）提高对建模的限制如何转化为限制的限制，以预测或控制新的策略（iii II II II III），请提高对建模如何限制的限制，以提高对建模的限制的理解（III）的限制。反应（例如，发现当地锁定的组合何时可能比民族锁定更有效）。适应大流行病现实的智能模型可以巩固证据基础，以获得可靠且消息灵通的公共卫生政策。