Forecasting influenza epidemics using a mechanistic epidemic model

使用机械流行病模型预测流感流行

• 批准号: 9361027
• 负责人: Michael L Jackson
• 金额: $ 28.63万
• 依托单位: KAISER FOUNDATION RESEARCH INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9361027
• 关键词: Address; Blood Circulation; Cessation of life; Data; Epidemic; Epidemiology; Evolution; Genetic; Geographic state; Geography; Health; Hospital Administrators; Hospitals; Human; Immunity; Incidence; Individual; Infection; Influenza; Influenza A virus; Influenza prevention; Influenza vaccination; Knowledge; Lead; Link; Measures; Membrane Proteins; Methods; Modeling; Population; Predisposition; Prevalence; Prevention; Public Health; Research; Resource Allocation; Resources; Seasons; Severities; Target Populations; Testing; Time; Uncertainty; Vaccination; Vaccines; Variant; Viral; Virus; acquired immunity; base; data resource; flu; flu transmission; genetic strain; improved; influenza epidemic; influenza surveillance; influenza virus strain; influenza virus vaccine; influenzavirus; mathematical model; population based; prevent; prospective; seasonal influenza; surveillance data; vaccine effectiveness; virology

SUMMARY/ABSTRACT This project will fill a fundamental knowledge gap in influenza epidemiology, which is the lack of a quantified relationship between viral antigenic drift and human susceptibility to influenza. We will then apply this new knowledge to improve the accuracy and timeliness of influenza forecasts. Antigenic drift refers to gradual changes in the surface proteins of influenza viruses, which allow new virus strains to escape acquired immunity and to re-infect individuals who were previously infected with influenza. Antigenic cartography can quantify the magnitude of antigenic drift (i.e. the antigenic distance) between influenza virus strains. To date, however, the relationship between antigenic distance and susceptibility to infection has not been quantified for human influenza. We will use a mechanistic model of influenza transmission and immunity to estimate the association between increasing antigenic distance and increasing susceptibility to infection with influenza. For this, we will take advantage of a unique data resource: active influenza surveillance conducted since the 2011/12 influenza season through the US Influenza Vaccine Effectiveness Network. These data include population-based estimates of the incidence of influenza, stratified by virus subtype/lineage and with antigenic and genetic characterization of circulating influenza viruses, in three geographically distinct US states. The data also include influenza vaccine coverage for the target populations. We will apply our mechanistic influenza model to these data and quantify the drift/susceptibility association. We will then apply these findings to improve forecasting of seasonal influenza epidemics. Two different approaches are currently taken to influenza forecasting. Short-term forecasts use near-real-time surveillance data to predict the timing and intensity of the peak in influenza cases, with lead times of a few weeks. Long-term forecasts use data on the relative prevalence of different influenza strains to predict which strains will dominate the upcoming season. At present neither short- nor long-term forecasting methods make effective use of data on pre-existing immunity to influenza due to vaccination or prior circulation of influenza strains. Having quantified the drift/susceptibility association, we will test the forecasting abilities of our influenza model. We hypothesize that including data on prior circulation of influenza and on vaccine coverage will allow us to forecast the intensity and subtype/lineage distribution of upcoming influenza epidemics with lead times of 9+ months. The proposed research will benefit human health by 1) improving our understanding of the interplay between human immunity and virus antigenic drift and 2) improving the accuracy and timeliness of influenza forecasts, allowing more time for the allocation of resources for influenza prevention and treatment.

总结/摘要 该项目将填补流感流行病学的一个基本知识空白， 病毒抗原漂移和人类对流感易感性之间的定量关系。然后我们将申请 这些新知识将提高流感预测的准确性和及时性。抗原漂移是指 流感病毒表面蛋白质的逐渐变化，使新的病毒株能够逃避获得性感染， 免疫力和重新感染先前感染流感的个体。抗原制图可以 量化流感病毒株之间的抗原漂移幅度（即抗原距离）。到目前为止， 然而，抗原距离和感染易感性之间的关系尚未被量化， 人类流感。 我们将使用流感传播和免疫的机制模型来估计这种关联 抗原距离的增加和流感感染易感性的增加之间的关系。为此，我们 利用独特的数据资源：自2011/12年流感以来进行的积极流感监测 美国流感疫苗有效性网络（US Influenza Vaccine Efficiency Network）这些数据包括基于人口的估计数 的流感发病率，按病毒亚型/谱系分层，并具有抗原性和遗传特征 在美国三个地理位置不同的州，数据还包括流感 目标人群的疫苗覆盖率。我们将把我们的机械流感模型应用于这些数据， 量化漂移/敏感性关联。 然后，我们将应用这些发现来改善季节性流感流行的预测。两个不同 目前正在采取各种方法进行流感预测。短期预报使用接近实时的监测数据 预测流感病例高峰的时间和强度，提前几周。长期 预测使用不同流感毒株的相对流行率数据来预测哪种毒株将占主导地位 即将到来的赛季。目前，短期和长期预测方法都没有有效利用以下数据： 由于接种疫苗或流感病毒株之前的传播而预先存在对流感的免疫力。量化后， 漂移/易感性关联，我们将测试我们的流感模型的预测能力。我们假设 包括流感之前的传播和疫苗覆盖率的数据将使我们能够预测 即将到来的流感流行的强度和亚型/谱系分布，提前时间为9个月以上。 这项拟议中的研究将通过以下方式有益于人类健康：1）提高我们对相互作用的理解 2）提高流感诊断的准确性和及时性 因此，我们有更多时间分配资源，以预防和治疗流感。