Smart and scalable epidemic prediction and control

新型传染病，如SARS和COVID-19，是对公共卫生的突出威胁。在缺乏疫苗或先前免疫力的情况下，以及在这些疾病出现时围绕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。数学模型是一种计算工具，它将我们对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）相结合。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，如社交距离或封锁。鉴于这种预测能力，模型输出往往作为决策的证据。然而，这一证据的可靠性可能在很大程度上取决于模型的详细程度，有时甚至出乎意料。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，称为异质性。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，因为我们可能只有关于每个差异的稀缺数据，并且必须做出更多的假设才能使用模型。粗略的模型，平均这些异质性在整个国家或忽略差异，由于年龄为基础的风险，可能更容易使用，但过于自信，只能证明钝NPI，如封锁。选择正确的规模来模拟和应对传染病是流行病学的前沿问题。如果这个尺度错了，可能会误导政策，使大流行应对变得危险、昂贵和无效。第一，最可靠的建模尺度（如地方、区域或国家）因地点、时间和对国家绩效指标的反应而异。现有的模型很少适应这种波动，或者当它们变得非常复杂时。其次，噪声数据、政策的后勤限制（例如，学校关闭可能只发生在整个地区）以及人们的反应行为对模型施加了很大程度上未知的性能限制，限制了预测的范围或NPI的效率。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，每个较高级别的组对该异质性进行平均，我的目标是构建新颖的模型，真实地描述流行病的许多相互作用的尺度。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流行病学中很少使用的反应回路。通过将这些领域的原则与政策科学家的专家意见相结合，我将设计新的算法，重新构建这些层次结构，以暴露和绕过性能限制，并确定最可靠的规模，以便在任何时候实际对抗流行病。这些智能框架智能地平衡了传播的细节与可用数据，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）得出强有力的传播预警指标（例如，预示一种流行病是否可能有第二波的迹象），（ii）更好地理解建模的局限性如何转化为我们预测或控制疫情的限制，以及（iii）制定新的战略，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可靠和更知情的公共卫生政策的证据基础。

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.