Smart and scalable epidemic prediction and control

SARS和COVID-19等新型传染病是对公共卫生的重大威胁。在没有疫苗或事先免疫以及这些疾病出现时的特征存在很大不确定性的情况下，模型构成了我们的第一道防线。数学模型是一种计算工具，它将我们关于疾病如何传播的知识与现有的流行病数据（例如每日病例数）结合起来。模型可以提供对推动传播的关键因素的理解，对即将发生的病例或死亡的预测，以及对非药物干预措施（npi）的潜在影响的估计，如保持社交距离或封锁。鉴于这种预测能力，模型的输出通常可以作为决策的依据。然而，这种证据的可靠性在很大程度上，有时甚至出乎意料地取决于模型的尺度或细节水平。流行病是一种复杂的现象，涉及在地理、人口和其他尺度上传播的差异，即异质性。模拟所有这些异质性的精细模型可能会产生不可靠的预测，因为我们可能只有很少的每个差异的数据，并且必须做出更多的假设来使用模型。将这些异质性平均到整个国家或忽略基于年龄的风险差异的粗糙模型可能更容易使用，但过于自信，只能证明封锁等生硬的npi。选择正确的规模来模拟和应对传染病是流行病学的一个前沿问题。弄错这个规模可能会误导政策，使大流行应对变得危险、昂贵和无效。两个主要问题使得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（如地方、区域或国家）因地点、时间和对国家行动指标的反应而异。现有的模型很少能适应这种波动，或者当它们适应时往往会变得非常复杂。其次，嘈杂的数据、政策上的后勤限制（例如，学校关闭可能只发生在整个地区）以及人们的反应性行为给模型施加了很大程度上未知的性能限制，限制了npi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到层次或组中，其中每个较低级别的组描述了一些感兴趣的异质性，每个较高级别的组在这种异质性上取平均值，我的目标是构建能够真实地描述流行病的许多相互作用尺度的新模型。信息论和分散控制理论是工程学领域，它们提供了独特而严谨的方法来减轻不确定性和管理反应性循环，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，重构这些层次结构，以暴露和绕过性能限制，并确定在任何时候实际抗击流行病的最可靠的尺度。这些智能框架智能地平衡了传播细节和可用数据，以可靠地了解这些细节，将推动流行病建模的边界。将它们应用于不同的SARS和COVID-19数据集，我将(I)获得强大的传播预警指标（例如，预测流行病是否可能出现第二波的迹象）；（二）进一步了解建模的限制如何转化为对我们预测或控制疫情的能力的限制；（三）制定协调不同规模的国家行动方案的新战略，以提高未来大流行病应对的效率（例如，发现何时地方封锁的组合可能比国家封锁更有效）。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，从而制定可靠和更明智的公共卫生政策。

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.