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

基本信息

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
项目类别：
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

项目摘要

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（由申请人提供）：新兴病原体的爆发是对健康和个人福利的最不可预测的威胁之一。病原体出现的原因是可变的，看似特质。尽管预测传染病的出现（和重新出现）似乎是棘手的，但流行病建模的新发展表明，出现事件与跨倾斜点的动力学系统中常见的模式结合在一起。特别是，最近已经显示出传输中的倾斜点感染疾病以留下其接近性的方式影响流行病学模式。例如，一种称为关键减速的统计现象会影响数据流的方差和特征性方式。这项研究的长期目标是发展出一种预测统计的理论，即在近距离统计中逐渐宣传，并逐渐降低了统计的态度，并逐渐降低了态度，而这些统计的态度是其他的，并且是其他统计的态度，并且是其他统计的态度。（1）基于该理论的统计方法，开发了新出现的感染性疾病的理论；在经验驱动的反馈回路中，具有数据分析的理论研究。研究设计首先需要开发中间复杂性的数学模型，从而使数学解决方案和动态现实主义都代表当代的新兴和重新出现传染病。这些模型的解决方案为检测流行病学数据中的预警模式的统计算法提供了基础。 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最终，将为随后的模型开发，模拟和验证提供信息，这些研究的结果将在一系列软件应用程序中汇总在一起，以供使用I研究，政策制定和教育。