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: 9750623
• 负责人: Michael L Jackson
• 金额: $ 26.86万
• 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/9750623
• 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 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.

摘要/摘要 该项目将填补流感流行病学的基本知识空白，即缺乏 病毒抗原漂移与人类对流感易感性之间的量化关系。然后我们将申请 这一新知识提高了流感预测的准确性和及时性。抗原漂移是指 流感病毒表面蛋白的逐渐变化，使新病毒株能够逃脱获得性感染 免疫力并重新感染以前感染过流感的人。抗原制图可以 量化流感病毒株之间抗原漂移的程度（即抗原距离）。迄今为止， 然而，抗原距离与感染易感性之间的关系尚未量化 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 抗原距离增加与流感感染易感性增加之间的关系。为此，我们将 利用独特的数据资源：自 2011/2012 年流感以来进行的主动流感监测 通过美国流感疫苗有效性网络了解季节。这些数据包括基于人口的估计 流感发病率，按病毒亚型/谱系以及抗原和遗传特征分层 在美国三个地理位置不同的州传播流感病毒。该数据还包括流感 目标人群的疫苗覆盖率。我们将把我们的机制性流感模型应用到这些数据上 量化漂移/敏感性关联。 然后，我们将应用这些发现来改进对季节性流感流行的预测。两种不同的 目前正在采取流感预测方法。短期预测使用近实时监测数据 预测流感病例高峰的时间和强度，提前期为几周。长期 预测使用不同流感毒株相对流行率的数据来预测哪些毒株将占主导地位 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数据 由于疫苗接种或先前流感病毒株的传播而对流感预先存在的免疫力。量化后 通过漂移/敏感性关联，我们将测试流感模型的预测能力。我们假设 包括先前流感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，提前期为 9 个月以上。 拟议的研究将通过以下方式造福人类健康：1）提高我们对相互作用的理解 人类免疫和病毒抗原漂移之间的关系以及2）提高流感的准确性和及时性 预测，为流感预防和治疗分配资源留出更多时间。