New approaches to measuring and containing the spatial spread of human pathogens

测量和遏制人类病原体空间传播的新方法

• 批准号: 9381090
• 负责人: Caroline O'Flaherty Buckee
• 金额: $ 39.72万
• 依托单位: HARVARD SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-15 至 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9381090
• 关键词: Big Data; Car Phone; Cells; Communicable Diseases; Complex; Crowding; Data; Data Set; Data Sources; Dengue; Disease; Drug resistance; Ebola virus; Epidemic; Epidemiology; Genes; Genetic; Genomics; Glare; Human; Individual; Infection; Influenza; Influenza A Virus, H1N1 Subtype; Intervention; Location; Low income; Malaria; Maps; Mathematics; Measures; Mediating; Methods; Modeling; Monitor; Parasite resistance; Parasites; Pattern; Policy Maker; Population; Population Dynamics; Population Genetics; Protocols documentation; Resource Allocation; Resources; Risk; Rubella; Severe Acute Respiratory Syndrome; Source; Southeastern Asia; Technology; Text; Time; Translating; Travel; Zika Virus; analytical tool; disorder control; genomic data; insight; migration; novel strategies; pandemic disease; pathogen; programs; response; surveillance data; tool; transmission process

Project Summary In an increasingly crowded and connected world, infectious diseases can spread rapidly between regions, as highlighted by increasingly frequent global pandemics including SARS, H1N1 influenza, Ebola virus, and now Zika virus. The spatial spread of disease mediated by human mobility also impacts endemic pathogens like malaria, where control programs and elimination strategies are undermined by travel to and from high transmission regions, drug resistant parasites are spread by human mobility, and distinguishing local from imported cases is critical for planning interventions. Understanding the distribution and dynamics of human populations underlies all aspects of infectious disease control, from the interpretation of surveillance data to the allocation of resources. Until recently, however, there was a glaring lack of information about human mobility patterns that spread diseases, particularly in low-income settings. New sources of data on human mobility and the spatial spread of diseases are increasingly available. In particular, data from mobile phones provide passively collected, real-time information on the scale of millions of individuals, with operators routinely collecting data on the cell towers associated with calls/texts that – when appropriately anonymized – can be modeled to provide longitudinal maps of where people are and how they are moving. We have been developing approaches to these models into epidemiological frameworks for understanding the spatial spread of infections, showing that these approaches provide specific targets for malaria control, accurate predictions about the location and timing of dengue epidemics, and insights into seasonal peaks of rubella, for example. Sequencing technology is also producing large volumes of geocoded pathogen genomic data, which can be used to estimate gene flow between populations – a measure of the rate at which infections are spreading. We have been analyzing malaria genetic data to adapt standard population genetic methods to accommodate the complex lifecycle and high diversity of the malaria parasite, in order to estimate this internal measure of migration. This proposal brings together these sources of information about the spatial spread of infectious diseases, focusing on the spread of the malaria parasite in Southeast Asia, working with collaborators collecting parasite genomic data in the region, mobile operators, and National Malaria Control Programs, to develop practical mathematical tools for integrating mobility data and pathogen genomics into the risk mapping, drug resistance monitoring, and resource allocation protocols used by control programs when planning for elimination. The project will lead to an analytical pipeline for generating mobility models from mobile phone data that can also be applied to other infectious diseases, and in particular in response to emerging epidemics. New tools are needed to understand the interaction between human population dynamics and the spread of infectious disease threats. These data sets are now increasingly straightforward to generate, but the analytical tools available to make the most use of them are still lacking. This proposal aims to develop the approaches to translate the promise of “Big Data” into insights that can be used by policy makers to control and contain human pathogens.

项目摘要 在一个日益拥挤和相互联系的世界中，传染病可以在区域之间迅速传播，正如以下所强调的那样 日益频繁的全球大流行，包括SARS、H1N1流感、埃博拉病毒，以及现在的寨卡病毒。空间扩散 由人类流动性介导的疾病的传播也影响到疟疾等地方性病原体，在这些疾病中，控制程序和消除 策略被往返高传播区的旅行破坏，抗药性寄生虫通过人类传播 流动性以及区分本地病例和输入病例对于规划干预措施至关重要。了解分布和 从监测数据的解释来看，人口的动态是传染病控制的各个方面的基础 到资源的分配。然而，直到最近，关于人类活动模式的信息仍然明显缺乏。 这会传播疾病，特别是在低收入环境中。 关于人类流动性和疾病空间传播的新数据来源越来越多。尤其是，数据 从移动电话提供被动收集的实时信息，规模达数百万人，与运营商 定期收集与呼叫/短信相关的手机信号塔上的数据，当适当地匿名时，这些数据可以被建模 以提供人们在哪里以及他们如何移动的纵向地图。我们一直在开发解决这些问题的方法 将模型纳入流行病学框架，以了解感染的空间传播，表明这些方法 为疟疾控制提供具体目标，准确预测登革热流行的地点和时间，以及 例如，对风疹季节性高峰期的洞察。测序技术也产生了大量的地理编码 病原体基因组数据，可用于估计种群之间的基因流动-一种衡量 感染正在蔓延。我们一直在分析疟疾遗传数据，以使标准的人口遗传方法适用于 适应疟疾寄生虫的复杂生命周期和高度多样性，以便估计 迁移。 这项建议汇集了有关传染病空间传播的这些信息来源，重点是 疟疾寄生虫在东南亚的传播，与收集该地区寄生虫基因组数据的合作者合作， 移动运营商和国家疟疾控制计划，开发实用的数学工具来整合移动性 数据和病原体基因组学到风险图、耐药性监测和资源分配方案中 计划淘汰时的控制程序。该项目将产生一条生成流动性模型的分析管道 来自手机数据，也可以应用于其他传染病，特别是在应对新出现的 流行病。需要新的工具来理解人口动态和疟疾传播之间的相互作用 传染病威胁。这些数据集现在越来越容易生成，但可用于 最大限度地利用它们仍然是欠缺的。这项提案旨在开发翻译“Big”承诺的方法 数据“为政策制定者提供可用于控制和遏制人类病原体的见解。