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中的基本知识差距，这是缺乏 病毒抗原漂移与人类对流感的敏感性之间的量化关系。然后我们将申请 这种新知识是为了提高流感预测的准确性和及时性。抗原漂移是指 流感病毒的表面蛋白的逐渐变化，允许新的病毒菌株逃脱 免疫力和重新感染以前感染流感的人。抗原制图可以 量化流感病毒菌株之间抗原漂移（即抗原距离）的大小。迄今为止， 但是，抗原距离和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力的机械模型来估计关联 在增加抗原距离和增加流感感染的敏感性之间。为此，我们将 利用独特的数据资源：自2011/12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调味。这些数据包括基于人群的估计 流感率的发生率，由病毒亚型/谱系和抗原和遗传表征分层 在三个地理上不同的美国国家中流传的流感病毒。数据还包括流感 目标种群的疫苗覆盖范围。我们将把机械性流感模型应用于这些数据，并 量化漂移/敏感性关联。 然后，我们将应用这些发现来改善对季节性流感流行病的预测。两个不同 目前采用方法来进行流感预测。短期预测使用近实时监视数据 为了预测流感病例中峰值的时间和强度，交货时间为几周。长期 预测使用有关不同流感菌株相对患病率的数据来预测哪些菌株将占主导地位 即将到来的季节。目前，短期或长期预测方法都没有有效地使用数据 由于疫苗接种或先前的流感菌株循环，对流感的免疫力预先存在。量化 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 其中包括有关流感和疫苗覆盖范围的先前流通数据的数据，将使我们能够预测 即将到来的流感流行病的强度和亚型/谱系分布，含量为9个月以上。 拟议的研究将通过1）提高我们对相互作用的理解来使人类健康受益 在人类免疫力和病毒抗原漂移之间以及2）提高流感的准确性和及时性 预测，为预防流感和治疗的资源分配更多时间。