Melia E. Bonomo, Michael W. Deem
We predict vaccine efficacy with a measure of antigenic distance between influenza A(H3N2) and candidate vaccine viruses based on amino acid substitutions in the dominant epitopes. In 2016-2017, our model predicts 19% efficacy compared to 20% observed. This tool assists candidate vaccine selection by predicting human protection against circulating strains.
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Influenza is a contagious respiratory illness that causes significant human morbidity and mortality, affecting 5-15% of the population in a typical epidemic season. Human influenza epidemics are caused by types A and B, with roughly 25% of human cases due to influenza B. Influenza B is a single-stranded RNA virus with a high mutation rate, and both prior immune history and vaccination put significant pressure on the virus to evolve. Due to the high rate of viral evolution, the influenza B vaccine component of the annual influenza vaccine is updated, roughly every other year in recent years. To…
Influenza A is a serious disease that causes significant morbidity and mortality, and vaccines against the seasonal influenza disease are of variable effectiveness. In this paper, we discuss use of the $p_{\rm epitope}$ method to predict the dominant influenza strain and the expected vaccine effectiveness in the coming flu season. We illustrate how the effectiveness of the 2014/2015 A/Texas/50/2012 [clade 3C.1] vaccine against the A/California/02/2014 [clade 3C.3a] strain that emerged in the population can be estimated via pepitope. In addition, we show by a multidimensional scaling analysis o…
We define a new parameter to quantify the antigenic distance between two H3N2 influenza strains: we use this parameter to measure antigenic distance between circulating H3N2 strains and the closest vaccine component of the influenza vaccine. For the data between 1971 and 2004, the measure of antigenic distance correlates better with efficacy in humans of the H3N2 influenza A annual vaccine than do current state of the art measures of antigenic distance such as phylogenetic sequence analysis or ferret antisera inhibition assays. We suggest that this measure of antigenic distance could be used t…
Traditional vaccines against viruses are designed to target their surface proteins, i.e., antigens, which can trigger the immune system to produce specific antibodies to capture and neutralize the viruses. However, viruses often evolve quickly, and their antigens are prone to mutations to avoid recognition by the antibodies (antigenic drift). This limitation of the antibody-mediated immunity could be addressed by the T-cell mediated immunity, which is able to recognize conserved viral HLA peptides presented on virus-infected cells. Thus, by targeting conserved regions on the genome of a virus,…
H1N1 influenza causes substantial seasonal illness and was the subtype of the 2009 influenza pandemic. Precise measures of antigenic distance between the vaccine and circulating virus strains help researchers design influenza vaccines with high vaccine effectiveness. We here introduce a sequence-based method to predict vaccine effectiveness in humans. Historical epidemiological data show that this sequence-based method is as predictive of vaccine effectiveness as hemagglutination inhibition (HI) assay data from ferret animal model studies. Interestingly, the expected vaccine effectiveness is g…
Seasonal influenza is a significant public health concern in the United States and globally. While influenza vaccines are the single most effective intervention to reduce influenza morbidity and mortality, there is considerable debate surrounding the merits and consequences of repeated seasonal vaccination. Here, we describe a two-season influenza epidemic contact network model and use it to demonstrate that increasing the level of continuity in vaccination across seasons reduces the burden on public health. We show that revaccination reduces the influenza attack rate not only because it reduc…
Influenza has been circulating in the human population and has caused three pandemics in the last century (1918 H1N1, 1957 H2N2, 1968 H3N2). The 2009 A(H1N1) was classified by the World Health Organization (WHO) as the fourth pandemic. Influenza has a high evolution rate, which makes vaccine design challenging. We here consider an approach for early detection of new dominant strains. By clustering the 2009 A(H1N1) sequence data, we found two main clusters. We then define a metric to detect the emergence of dominant strains. We show on historical H3N2 data that this method is able to identify a…
Human seasonal influenza viruses evolve rapidly, enabling the virus population to evade immunity and re-infect previously infected individuals. Antigenic properties are largely determined by the surface glycoprotein hemagglutinin (HA) and amino acid substitutions at exposed epitope sites in HA mediate loss of recognition by antibodies. Here, we show that antigenic differences measured through serological assay data are well described by a sum of antigenic changes along the path connecting viruses in a phylogenetic tree. This mapping onto the tree allows prediction of antigenicity from HA seque…
Inferring and Predicting Clade-Level Relative Transmission Fitness in Seasonal Influenza A Using Differential Population Growth Rate and Deep Learning
本文应用差异种群增长率框架和卷积神经网络，基于全球流感监测数据估计H3N2和H1N1支系的相对传播适应性，并实现从全基因组序列预测适应性，揭示了新兴支系K的适应性优势及基因组贡献位点。
Determining phenotype from genetic data is a fundamental challenge. Influenza A viruses undergo rapid antigenic drift and identification of emerging antigenic variants is critical to the vaccine selection process. Using former seasonal influenza A(H1N1) viruses, hemagglutinin sequence and corresponding antigenic data were analyzed in combination with 3-D structural information. We attributed variation in hemagglutination inhibition to individual amino acid substitutions and quantified their antigenic impact, validating a subset experimentally using reverse genetics. Substitutions identified as…
Many viruses evolve rapidly. For example, hemagglutinin of the H3N2 influenza A virus evolves to escape antibody binding. This evolution of the H3N2 virus means that people who have previously been exposed to an influenza strain may be infected by a newly emerged virus. In this paper, we use Shannon entropy and relative entropy to measure the diversity and selection pressure by antibody in each amino acid site of H3 hemagglutinin between the 1992-1993 season and the 2009-2010 season. Shannon entropy and relative entropy are two independent state variables that we use to characterize H3N2 evolu…
Individual vaccination as Nash equilibrium in a SIR model with application to the 2009-10 Influenza A(H1N1) epidemic in France
本文在SIR模型中运用平均场博弈理论证明个体自利疫苗接种策略存在纳什均衡，并与社会最优策略比较，进而应用于2009-2010年法国甲型H1N1流感疫苗接种运动，揭示部分个体因对疫苗风险的悲观感知而停止接种。
The unfolding of pandemic influenza A(H1N1) for Fall 2009 in the Northern Hemisphere is still uncertain. Plans for vaccination campaigns and vaccine trials are underway, with the first batches expected to be available early October. Several studies point to the possibility of an anticipated pandemic peak that could undermine the effectiveness of vaccination strategies. Here we use a structured global epidemic and mobility metapopulation model to assess the effectiveness of massive vaccination campaigns for the Fall/Winter 2009. Mitigation effects are explored depending on the interplay between…
Background: Influenza A/H3N2 has been circulating in humans since 1968, causing considerable morbidity and mortality. Although H3N2 incidence is highly seasonal, how such seasonality contributes to global phylogeographic migration dynamics has not yet been established. Results: Incorporating seasonally varying migration rates improves the modeling of migration. In our global model, windows of increased immigration map to the seasonal timing of epidemic spread, while windows of increased emigration map to epidemic decline. Seasonal patterns also correlate with the probability that local lineage…
Concepts and methods for predicting viral evolution
针对季节性人流感病毒快速进化问题，提出整合序列、流行病学、抗原特征及病毒表型数据的预测流程，可估计毒株相对适应性、预测分支频率并评估候选疫苗株保护力，相关预测可通过指定网站获取。
In this work we analyze the evolution of voluntary vaccination in networked populations by entangling the spreading dynamics of an influenza-like disease with an evolutionary framework taking place at the end of each influenza season so that individuals take or not the vaccine upon their previous experience. Our framework thus put in competition two well-known dynamical properties of scale-free networks: the fast propagation of diseases and the promotion of cooperative behaviors. Our results show that when vaccine is perfect scale-free networks enhance the vaccination behavior with respect to …
The evolution of the hemagglutinin amino acids sequences of Influenza A virus is studied by a method based on an informational metrics, originally introduced by Rohlin for partitions in abstract probability spaces. This metrics does not require any previous functional or syntactic knowledge about the sequences and it is sensitive to the correlated variations in the characters disposition. Its efficiency is improved by algorithmic tools, designed to enhance the detection of the novelty and to reduce the noise of useless mutations. We focus on the USA data from 1993/94 to 2010/2011 for A/H3N2 an…
Emergenet: A Digital Twin of Sequence Evolution for Scalable Emergence Risk Assessment of Animal Influenza A Strains
本研究针对动物甲型流感病毒株出现潜力评估能力有限的问题，提出Emergenet工具，通过序列进化数字孪生预测变异株出现路径，其性能优于现有方法且速度大幅提升，为大流行预防提供新途径。
Seasonal influenza presents an ongoing challenge to public health. The rapid evolution of the flu virus necessitates annual vaccination campaigns, but the decision to get vaccinated or not in a given year is largely voluntary, at least in the United States, and many people decide against it. In early attempts to model these yearly flu vaccine decisions, it was often assumed that individuals behave rationally, and do so with perfect information -- assumptions that allowed the techniques of classical economics and game theory to be applied. However, the usual assumptions are contradicted by the …
Understanding which phenotypic traits are consistently correlated throughout evolution is a highly pertinent problem in modern evolutionary biology. Here, we propose a multivariate phylogenetic latent liability model for assessing the correlation between multiple types of data, while simultaneously controlling for their unknown shared evolutionary history informed through molecular sequences. The latent formulation enables us to consider in a single model combinations of continuous traits, discrete binary traits and discrete traits with multiple ordered and unordered states. Previous approache…
Within-host genetic sequencing from samples collected over time provides a dynamic view of how viruses evade host immunity. Immune-driven mutations might stimulate neutralization breadth by selecting antibodies adapted to cycles of immune escape that generate within-subject epitope diversity. Comprehensive identification of immune-escape mutations is experimentally and computationally challenging. With current technology, many more viral sequences can readily be obtained than can be tested for binding and neutralization, making down-selection necessary. Typically, this is done manually, by pic…
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