Merging Viral Genetics with Climate and Population Data for Zoonotic Surveillance

将病毒遗传学与气候和人口数据相结合以进行人畜共患病监测

• 批准号: 9253452
• 负责人: MATTHEW SCOTCH
• 金额: $ 39.51万
• 依托单位: ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-04-06 至 2019-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9253452
• 关键词: Absenteeism; Address; Adoption; Animals; Area; Avian Influenza; Bacteria; Bioinformatics; Biological Models; Case Study; Climate; Communicable Diseases; Complex; Data; Data Sources; Detection; Diffusion; Disease; Disease Outbreaks; Enabling Factors; Epidemic; Epidemiologist; Evaluation; Event; Genbank; Genetic; Genetic Models; Genetic Polymorphism; Genotype; Goals; Health; Hospitals; Human; Individual; Infection; Influenza; Influenza A Virus, H1N1 Subtype; Influenza A virus; Internet; Investigation; Laboratories; Letters; Location; Measures; Modeling; Molecular Evolution; Molecular Models; Morbidity - disease rate; Mutation; Pathogenicity; Pattern; Population; Population Genetics; Population Surveillance; Rabies; Reporting; Research; Resources; Risk; Role; Running; Sales; Sampling; Schools; Seasons; System; Techniques; Testing; Time; Translating; Travel; United States; Viral; Viral Genome; Virus; Visit; West Nile virus; Zoonoses; climate impact; critical period; data resource; flu; genetic predictors; migration; mortality; pandemic influenza; pathogen; population health; public health intervention; surveillance data; transmission process; trend; trend analysis; viral transmission; virus characteristic; virus genetics; web portal

 DESCRIPTION (provided by applicant): Recent events such as pandemic influenza A (H1N1)pdm09 have demonstrated how mutations in a viral genome can greatly impact disease spread and population health risk. Thus, there is now a greater need to merge viral genetics within state health agency surveillance practice. This is particularly relevant for zoonotic viruse that are transmittable between animals and humans such as influenza, rabies, and West Nile Virus. As an added complexity, there are many potential drivers of virus transmission that need to be considered including climate, population and travel, and ultimately, genetic polymorphisms in the virus itself. Zoonotic disease surveillance at the state level is most often performed using data that originates from passive case reporting by laboratories or clinicians rather than secondary data from resources such as GenBank. While these data are sufficient for federal reporting purposes and basic trend analysis, they only measure the number of suspected or confirmed cases and not the genetic characteristics of the virus. When states and federal agencies do use genotyping, it is often limited to certain pathogens (mostly bacteria) and only for samples that are reported through passive surveillance or during outbreak investigations. The omission of secondary viral genetic data limits the types of analysis by state health agencies. For example, current reportable disease data do not enable epidemiologists to determine the origin of a particular viral strain, trace how it has spread, or identify climate, population, and genetic factrs enabling it to propagate. In this study, we will develop and evaluate an integrated bioinformatics framework to supplement current zoonotic disease surveillance approaches at state health agencies. We hypothesize that a framework that properly merges viral genetic data with climate, population, and travel data can accurately predict the timing of initial peaks of seasonal epidemics caused by zoonotic viruses. Health agencies can then use these trends to prioritize control measures and reduce morbidity and mortality. In addition, we will address the barriers to health agency utilization of bioinformatics resources and secondary data by developing an online portal for accessing and querying of complex viral genetic models. We will measure the perceived usefulness of information from our framework as part of our long-term goal of utilization and adoption by health agencies. In Aim 1, we will develop an automated bioinformatics system that models virus diffusion while testing the significance of climate, population, and genetic predictors. As part of this effort, we will provide a publically available Web portal for health agencies and other users to access our results, and run their own models. In Aim 2, we will use our platform to identify significant climate, population, and genetic predictors of diffusion across different zoonotic viruses including influenza and WNV. In Aim 3, we will evaluate the accuracy of a bioinformatics system that uses statistically significant climate, population, and genetic predictors to identify seasona trends of zoonotic virus epidemics and communicate these findings to different health agencies.

 描述（由申请人提供）：最近发生的大流行甲型流感 (H1N1)pdm09 等事件已经证明，病毒基因组中的突变如何极大地影响疾病传播和人口健康风险。因此，现在更需要将病毒遗传学纳入州卫生机构的监测实践中。这对于可在动物和人类之间传播的人畜共患病毒尤其相关，例如流感、狂犬病和西尼罗河病毒。更复杂的是，病毒传播有许多潜在的驱动因素需要考虑，包括气候、人口和旅行，以及最终的病毒本身的基因多态性。 州一级的人畜共患疾病监测通常使用来自实验室或临床医生被动病例报告的数据，而不是来自 GenBank 等资源的二手数据。虽然这些数据足以用于联邦报告目的和基本趋势分析，但它们仅衡量疑似或确诊病例的数量，而不是病毒的遗传特征。当州和联邦机构确实使用基因分型时，通常仅限于某些病原体（主要是细菌），并且仅适用于通过被动监测或疫情调查期间报告的样本。二级病毒遗传数据的遗漏限制了州卫生机构的分析类型。例如，当前的可报告疾病数据无法使流行病学家确定特定病毒株的起源，追踪其传播方式，或识别使其传播的气候、人口和遗传因素。 在这项研究中，我们将开发和评估一个综合的生物信息学框架，以补充州卫生机构当前的人畜共患疾病监测方法。我们假设，一个将病毒遗传数据与气候、人口和旅行数据正确融合的框架可以准确预测人畜共患病毒引起的季节性流行病的初始高峰时间。然后，卫生机构可以利用这些趋势来确定控制措施的优先顺序并降低发病率和死亡率。此外，我们将通过开发用于访问和查询复杂病毒遗传模型的在线门户来解决卫生机构利用生物信息学资源和二手数据的障碍。我们将衡量框架中信息的感知有用性，作为卫生机构利用和采用的长期目标的一部分。 在目标 1 中，我们将开发一个自动化生物信息学系统，该系统可以模拟病毒扩散，同时测试气候、人口和遗传预测因子的重要性。作为这项努力的一部分，我们 将为卫生机构和其他用户提供一个公开的门户网站，以访问我们的结果并运行他们自己的模型。在目标 2 中，我们将使用我们的平台来识别不同人畜共患病毒（包括流感和西尼罗河病毒）传播的重要气候、人口和遗传预测因子。在目标 3 中，我们将评估生物信息学系统的准确性，该系统使用统计上显着的气候、人口和遗传预测因子来识别人畜共患病毒流行的季节趋势，并将这些发现传达给不同的卫生机构。

项目成果

Phylodynamic assessment of intervention strategies for the West African Ebola virus outbreak.

• DOI: 10.1038/s41467-018-03763-2
• 发表时间: 2018-06-08
• 期刊: Nature communications
• 影响因子: 16.6
• 作者: Dellicour S;Baele G;Dudas G;Faria NR;Pybus OG;Suchard MA;Rambaut A;Lemey P
• 通讯作者: Lemey P

Bayesian phylogeography of influenza A/H3N2 for the 2014-15 season in the United States using three frameworks of ancestral state reconstruction.

• DOI: 10.1371/journal.pcbi.1005389
• 发表时间: 2017-02
• 期刊: PLoS computational biology
• 影响因子: 4.3
• 作者: Magee D;Suchard MA;Scotch M
• 通讯作者: Scotch M

The Effects of Sampling Location and Predictor Point Estimate Certainty on Posterior Support in Bayesian Phylogeographic Generalized Linear Models.

• DOI: 10.1038/s41598-018-24264-8
• 发表时间: 2018-04-12
• 期刊: Scientific reports
• 影响因子: 4.6
• 作者: Magee D;Taylor JE;Scotch M
• 通讯作者: Scotch M

The Molecular Epidemiology and Clinical Phylogenetics of Rhinoviruses Among Paediatric Cases in Sydney, Australia.

• DOI: 10.1016/j.ijid.2021.06.046
• 发表时间: 2021-09
• 期刊: INTERNATIONAL JOURNAL OF INFECTIOUS DISEASES
• 影响因子: 8.4
• 作者: Adam, Dillon Charles;Chen, Xin;Scotch, Matthew;MacIntyre, Chandini Raina;Dwyer, Dominic;Kok, Jen
• 通讯作者: Kok, Jen

GeoBoost2: a natural languageprocessing pipeline for GenBank metadata enrichment for virus phylogeography.

• DOI: 10.1093/bioinformatics/btaa647
• 发表时间: 2020-12-22
• 期刊: Bioinformatics (Oxford, England)
• 作者: Magge A;Weissenbacher D;O'Connor K;Tahsin T;Gonzalez-Hernandez G;Scotch M
• 通讯作者: Scotch M