Merging Viral Genetics with Climate and Population Data for Zoonotic Surveillance

 描述(由申请人提供)：最近的事件，如甲型H1N1流感大流行pdm09，证明了病毒基因组的突变可以极大地影响疾病传播和人口健康风险。因此，现在更有必要将病毒遗传学纳入州卫生机构的监测实践。这与可在动物和人类之间传播的人畜共患病毒特别相关，如流感、狂犬病和西尼罗河病毒。更复杂的是，需要考虑许多潜在的病毒传播驱动因素，包括气候、人口和旅行，最终还包括病毒本身的基因多态。 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等资源的次要数据。虽然这些数据足以用于联邦报告和基本趋势分析，但它们只衡量疑似或确诊病例的数量，而不是病毒的基因特征。当各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)，并且仅限于通过被动监测或在疫情调查期间报告的样本。二次病毒基因数据的遗漏限制了国家卫生机构的分析类型。例如，目前可报告的疾病数据无法使流行病学家确定特定病毒株的来源，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，以补充国家卫生机构目前的人畜共患疾病监测方法。我们假设，一个适当地将病毒基因数据与气候、人口和旅行数据相结合的框架可以准确地预测由人畜共患病病毒引起的季节性流行病的初始高峰的时间。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，我们将通过开发访问和查询复杂病毒遗传模型的在线门户网站，解决卫生机构利用生物信息学资源和二级数据的障碍。我们将衡量我们框架中信息的感知有用性，作为卫生机构利用和采用信息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，该系统模拟病毒扩散，同时测试气候、人口和遗传预测因素的重要性。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共可用的Web门户，以访问我们的结果，并运行他们自己的模型。在目标2中，我们将利用我们的平台来确定重要的气候、人口和遗传预测因素，以预测包括流感和西尼罗河病毒在内的不同人畜共患病病毒的传播。在目标3中，我们将评估生物信息学系统的准确性，该系统使用具有统计意义的气候、人口和遗传预测因子来识别人畜共患病病毒流行的季节性趋势，并将这些结果传达给不同的卫生机构。

 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.