Forecasting tipping points in emerging and re-emerging infectious diseases

预测新出现和重新出现的传染病的临界点

• 批准号: 9069488
• 负责人: John M Drake
• 金额: $ 62.94万
• 依托单位: UNIVERSITY OF GEORGIA
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-12 至 2019-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9069488
• 关键词: Acute; Adopted; Adoption; Agriculture; Algorithmic Software; Algorithms; Antibiotics; Anus; Biology; Businesses; Categories; Characteristics; Chemical Vaccines; Communicable Diseases; Complex; Computer Simulation; Computer software; Data; Data Analyses; Demography; Detection; Development; Disease; Disease Outbreaks; Education; Education and Outreach; Emerging Communicable Diseases; Epidemic; Epidemiology; Event; Excess Mortality; Exhibits; Family suidae; Feedback; Frequencies; Growth; Health; Incidence; Individual; Infection; Influenza A Virus, H1N1 Subtype; Influenza A virus; Language; Lead; Learning; Measles; Medical; Methodology; Methods; Middle East Respiratory Syndrome Coronavirus; Minor; Mission; Modeling; Mumps; Pattern; Pertussis; Policy Making; Population; Public Health; Reporting; Research; Research Design; Research Personnel; Rubella; Savings; Science; Series; Severe Acute Respiratory Syndrome; Signal Transduction; Social Welfare; Speed; Statistical Algorithm; Statistical Methods; Stream; Stress Tests; System; Theoretical Studies; Theoretical model; Time; Vaccinated; Vaccines; Validation; arm; base; cost; critical period; disease transmission; dynamic system; flu; human disease; mathematical model; mathematical theory; model development; novel; pandemic disease; pathogen; simulation; theories

DESCRIPTION (provided by applicant): Outbreaks of emerging pathogens are among the most unpredictable threats to health and personal welfare. The causes of pathogen emergence are variable and seemingly idiosyncratic. Although forecasting infectious disease emergence (and re-emergence) therefore seems intractable, new developments in epidemic modeling show that emergence events are united by patterns common among dynamical systems that cross tipping points. Particularly, it has recently been shown that tipping points in the transmission of infectious diseases influence epidemiological patterns in a way that leaves \signatures" of their proximity. For instance, a statistical phenomenon called critical slowing down influences the variance and autocorrelation of data streams in characteristic ways. The long term objective of this study is to develop a theory for forecasting epidemic transitions before they occur, focusing on critical slowing down and other near-critical statistical patterns. Its specific aims are: (1) T develop theories of forecastability for emerging and eliminable infectious diseases; (2) to develop model-independent statistical methods for forecasting disease emergence based on this theory; (3) to document critical slowing down and other near-critical phenomena by comparing data on re-emerging and non-emerging pathogens; and (4) to produce software packages implementing these methods. The research strategy adopted by this project combines theoretical studies with data analysis in an empirically- driven feedback loop. The research design requires first the development of mathematical models of intermediate complexity allowing both mathematical solution and dynamic realism to represent contemporary emerging and re-emerging infectious diseases. Solutions of these models provide the basis for statistical algorithms for detecting early warning patterns in epidemiological data. These algorithms will be \stress-tested" using computer simulations of emergence events, including both simple and complex scenarios for the conditions under which emergence occurs and the imperfect surveillance and reporting systems that generate data. The algorithms will then be applied to data on recent emergence and re-emergence of measles, pertussis, and rubella in populations from which these pathogens had been nearly eliminated; lessons learned from this empirical study will inform subsequent rounds of model development, simulation and validation. Finally, the results of these studies will be brought together in a range of software applications for use i research, policy making, and education.

描述(申请人提供)：新出现的病原体的暴发是对健康和个人福利最不可预测的威胁之一。病原体出现的原因是多种多样的，而且看起来是特殊的。因此，尽管预测传染病的出现(和再次出现)似乎很难，但流行病建模的新进展表明，突发事件由跨越临界点的动力系统中的共同模式统一在一起。特别是，最近有研究表明，病毒传播中的临界点 传染病影响流行病模式的方式留下了它们接近的“特征”。例如，一种称为临界减速的统计现象以特有的方式影响数据流的方差和自相关性。这项研究的长期目标是开发一种理论，在疫情发生之前对其进行预测，重点是关键的减缓和其他接近关键的统计模式。它的具体目标是：(1)发展新出现和可消除的传染病的可预测性理论；(2)根据这一理论发展预测疾病出现的独立于模型的统计方法；(3)通过比较新出现的和非出现的病原体的数据，记录关键的减速和其他接近关键的现象；以及(4)制作实施这些方法的软件包。本项目所采用的研究策略将理论研究与数据分析结合在一个经验驱动的反馈循环中。研究设计要求首先开发中等复杂性的数学模型，允许数学解和动态现实主义代表当代新出现的和重新出现的传染病。这些模型的解决方案为检测流行病学数据中的早期预警模式的统计算法提供了基础。这些算法将使用计算机模拟紧急事件进行压力测试，包括紧急事件发生条件的简单和复杂情景，以及生成数据的不完善的监测和报告系统。然后，这些算法将应用于麻疹、百日咳和风疹在这些病原体几乎已被消除的人群中最近出现和重新出现的数据；从这项实证研究中吸取的经验教训将为随后几轮的模型开发、模拟和验证提供信息。最后，这些研究的结果将被汇集在一系列软件应用程序中，用于研究、政策制定和教育。