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

用于合成分子、临床和流行病学数据以及翻译的模型

• 批准号: 9279143
• 负责人: SIMON CAUCHEMEZ
• 金额: $ 20.28万
• 依托单位: IMPERIAL COLLEGE OF SCIENCE, TECHNOLOGY AND MEDICINE
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-15 至 2019-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9279143
• 关键词: Address; Affect; Algorithms; Animals; Antibiotics; Antigenic Variation; Area; Biological; Biology; Cells; Clinical; Clinical Data; Communicable Diseases; Communities; Complex; Computer Simulation; Coronaviridae; Coronavirus; Coupled; Data; Data Analyses; Data Set; Development; Disease; Disease Outbreaks; Economics; Emerging Communicable Diseases; Epidemic; Epidemiology; Evolution; Face; Frequencies; Funding; Generations; Generic Drugs; Genetic; Genotype; Hospitalization; Human; Immune system; Immunological Models; Incidence; Individual; Infection; Infectious Disease Epidemiology; Influenza; Influenza A virus; Intervention; Joints; Knowledge; Location; Machine Learning; Maps; Medicine; Methodology; Methods; Middle East; Middle East Respiratory Syndrome Coronavirus; Modeling; Molecular; Monte Carlo Method; Movement; Natural History; Pattern; Persons; Phenotype; Pneumococcal Infections; Policies; Policy Maker; Population; Process; Public Health; Recording of previous events; Research; Research Methodology; Serologic tests; Serological; Shapes; Site; Spatial Distribution; Specific qualifier value; Specificity; Stream; Streptococcus pneumoniae; Syndrome; Testing; Time; Vaccination; Vaccines; Variant; Virus; Work; age group; algorithmic methodologies; base; contextual factors; data exchange; data mining; design; digital; disease natural history; disease transmission; epidemiologic data; epidemiological model; forest; genetic evolution; high dimensionality; improved; infectious disease model; innovation; insight; mathematical model; meetings; mortality; novel; novel strategies; novel virus; pandemic influenza; pathogen; predictive modeling; predictive tools; public health relevance; resistant strain; seasonal influenza; simulation; social; surveillance data; tool; transmission process; virus genetics

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)?

描述（由申请人提供）：传染病传播的数学或计算模型代表了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领域有着悠久的历史，是深入了解流行病动态和评估控制方法潜在效果的有用工具。该项目的总体目标是大幅度改进传染病传播模型根据生物和疾病监测数据进行校准的方法。这将提高模型作为分析传染病爆发数据的工具的效用（例如，为公共卫生机构提供关于新病毒的传播和致命程度的更快速和可靠的估计），并提高可靠性