Models for synthesising molecular, clinical and epidemiological data, and transla

描述(由申请人提供)：传染病传播的数学或计算模型表示感染如何从一个人传播到另一个人的过程。这种模型在传染病流行病学中有很长的历史，是深入了解流行病动态和评估控制方法的潜在效果的有用工具。该项目的总体目标是大幅改进根据生物和疾病监测数据校准传染病传播模型的方法。这既将提高模型作为分析传染病暴发数据的工具的效用(例如，为新病毒对公共卫生机构的传播和致命性提供更快速、更可靠的估计)，也将提高可靠性 将模型作为预测不同干预措施(如疫苗或 病例隔离)，以帮助政策制定者就控制政策做出更明智的决定。与生物学和医学的许多领域一样，传染病建模的数据格局正在迅速变化。正在出现更大、更复杂的数据集，涵盖病原体与人类群体之间相互作用的许多不同方面：临床发病数据、关于快速进化的病原体的遗传数据、动物模型传播数据和基于社区的代表性血清学数据。我们项目的具体目标是：(A)开发基于机器学习的新方法，以在与传染病传播有关的复杂数据集中发现有趣的模式，从而更好地说明后续的机械数学或计算模型；(B)得出同时使用多种类型的数据来校准传播模型的新方法；以及(C)为模拟感染的空间传播或模拟病原体的传播和遗传进化而得出新的参数估计方法。我们将在三种关键感染的应用研究背景下实现这些目标：新出现的传染病(如中东目前传播的新型冠状病毒MERS-CoV)、流感和肺炎链球菌(一种主要细菌病原体)。我们将解决的用现有方法无法回答的科学问题的例子有：(I)迄今发生了多少未观察到的MERS-CoV病例(将使用病例群数据、病例的空间分布和病毒基因序列的数据来回答)？(2)每年有多少不同年龄段的人感染流感，他们的免疫系统对感染有何反应(使用人口的病例发病率和血清学测试的数据来回答)？(3)接种疫苗和处方做法在多大程度上影响耐药肺炎球菌菌株的出现(将通过抗生素和疫苗使用、病例发生率和细菌菌株频率的数据加以说明)？

DESCRIPTION (provided by applicant): A mathematical or computational model of infectious disease transmission represents the process of how an infection spreads from one person to another. Such models have a long history within infectious disease epidemiology, and are useful tools for giving insight into the dynamics of epidemics and for evaluating the potential effect of control methods. The overall objective of this project is to substantially improve the methods by which models of infectious diseases transmission are calibrated against biological and disease surveillance data. This will both improve the utility of models as tools for analyzing data on infectious disease outbreaks (for instance to provide more rapid and reliable estimates of how transmissible and lethal a new virus is to public health agencies) and also improve the reliability of models as tools for predicting the likely effect of different interventions (such as vaccines or case isolation) to help policy makers make more informed decisions about control policies. As with many areas of biology and medicine, the data landscape for infectious diseases modeling is changing rapidly. Larger and more complex datasets are becoming available that cover many different aspects of the interaction between a pathogen and the human population: clinical episode data, genetic data about fast-evolving pathogens; animal-model transmission data and community-based representative serological data. The specific aims of our project are to: (a) develop new machine-learning based methods to discover interesting patterns in complex datasets related to the transmission of infectious disease, so as to better specify subsequent mechanistic mathematical or computational models; (b) derive new approaches for using more than one type of data simultaneously to calibrate transmission models and (c) derive new methods of parameter estimation for simulations which model the spatial spread of infection or model both the transmission and genetic evolution of a pathogen. We will achieve these aims in the applied context of research on three key infections: emerging infectious diseases (such as MERS-CoV - the novel coronavirus currently spreading in the Middle East), influenza and Streptococcus pneumonia (a major bacterial pathogen). Examples of the scientific questions we will address that cannot be answered with current methods are: (i) how many unobserved cases of MERS-CoV have occurred so far (to be answered using data on case clusters data, the spatial distribution of cases and viral genetic sequences)? (ii) how many people in different age groups are infected with influenza each year and how does their immune system respond to infection (to be answered using data on case incidence and serological testing of the population)? (iii) how much is vaccination coupled with prescribing practices influencing the emergence of resistant strains of pneumococcus (to be addressed with data on antibiotic and vaccine use, case incidence and bacterial strain frequency)?