The Flu Vaccine Is Constantly Reviewed and Reformulated Because
Hum Vaccin Immunother. 2018; 14(three): 678–683.
Models for predicting the evolution of flu to inform vaccine strain selection
Joseph Chiliad. Agor
aOperations Inquiry, North Carolina State University, Raleigh, NC, Us
Osman Y. Özaltın
bEdward P. Fitts Department of Industrial and Systems Engineering, N Carolina Land University, Raleigh, NC, USA
Received 2017 Sep 6; Revised 2017 December 6; Accepted 2017 December 28.
Abstruse
Influenza vaccine composition is reviewed before every flu flavour because influenza viruses constantly evolve through antigenic changes. To inform vaccine updates, laboratories that contribute to the World Health Organization Global Influenza Surveillance and Response System monitor the antigenic phenotypes of circulating viruses all year round. Vaccine strains are selected in apprehension of the upcoming influenza flavor to allow adequate time for production. A mismatch between vaccine strains and predominant strains in the influenza season tin can significantly reduce vaccine effectiveness. Models for predicting the development of influenza based on the relationship of genetic mutations and antigenic characteristics of circulating viruses may inform vaccine strain selection decisions. Nosotros review the literature on state-of-the-art tools and prediction methodologies utilized in modeling the development of influenza to inform vaccine strain pick. We then hash out areas that are open for comeback and demand further research.
KEYWORDS: influenza development, prediction models, influenza vaccine, strain choice, antigenic difference, flu
Introduction
Influenza (influenza) is a highly contagious, acute, respiratory viral disease. Seasonal influenza epidemics impact 5–15% of the globe's population, resulting in 3–5 million cases of severe illnesses and up to 500,000 deaths annually.1 There are three serotypes of the flu virus: flu A, B, and C. Flu A and B viruses are mainly responsible for seasonal flu epidemics, whereas flu C viruses are less common and usually cause balmy upper respiratory illnesses.ii Influenza A viruses are subtyped on the basis of their two surface proteins: hemagglutinin and neuraminidase. Flu A subtypes and influenza B viruses are farther classified into strains based on their antigenic properties.
The first line of defense force against seasonal epidemics is the flu shot, which contains ii strains of the A virus (H1N1 and H3N2) and one or two strains of the B virus. Since 1970s, influenza B viruses accept diverged into two antigenically distinct lineages.3 Therefore, in addition to the trivalent vaccine, manufacturers likewise produce the quadrivalent vaccine with ii influenza B strains to cover both lineages.4 Most individuals have some level of prior immunity. Even so, new strains with mutations in their epitopes (poly peptide regions that are recognized by human antibodies) frequently ascend. These new strains have a fettle advantage over existing dominant strains considering they tin more effectively escape from host amnesty.5 This continuous process of evolution – also known as antigenic migrate – results in rapid turnover of the viral population.
The flu shot is unique in that it is annually reformulated and prepared at least six months in advance of the upcoming flu season due to rapid emergence of new strains and the time consuming nature of vaccine product (see Fig. ane).half-dozen In current do, the flu shot compositions of the Northern and Southern Hemispheres are reviewed and updated as necessary past the World Health Organization (WHO) through a global surveillance and response system.seven Also surveillance on emerging virus variants, antigenic characterization of circulating viruses past standard ferret antisera is the main determinant in vaccine strain selection. Lesser determinants include genetic variations, prevalence rates, and geographic distributions of virus variants.8 The authentic prediction of emerging strains, yet, is a circuitous problem considering of the stochastic nature of the antigenic drift procedure. Predicting the fate of strains currently circulating in the population is also non like shooting fish in a barrel for two reasons. First, multiple strains carrying different combinations of mutations co-broadcast and to some extent compete with one another for susceptible hosts.5 2nd, antigenic label by ferret antisera is different from that past man postal service-vaccination antisera considering humans and ferrets have unlike immune systems also equally very unlike prior exposure histories to influenza virus.eight
Influenza vaccine manufacturing process and timeline.xi It takes at least six months for the first supplies of approved vaccine to go available in one case the vaccine composition is decided. This lead time is needed because the vaccine production process involves many sequential steps, and these steps are strictly controlled past government health agencies.
Seasonal flu vaccine effectiveness mainly depends on how well vaccine strains represent prevalent viruses circulating in the community. The vaccine effectiveness estimates for years without antigenic mismatch between the flu shot strains and circulating strains ranges between 49% and 60%. As recently as the 2014–2015 flu season, however, a poor match between the selected A (H3N2) vaccine strain and the ones that predominantly circulated in that season reduced the vaccine effectiveness to 19% in the United states of america.nine Note that the vaccine effectiveness data simply becomes available towards the end of the flu flavor. For instance, the WHO made the decision on the 2014–2015 Northern Hemisphere vaccine composition in February 2014, while vaccine effectiveness data came out in early on 2015. Thus, vaccine effectiveness provides merely a retrospective review on seasonal vaccine performance, but never plays a role in the selection process for vaccine strains.
A serologic assay, i.east., hemagglutinin inhibition (HI), is used for antigenic characterization of the circulating strains in the flu shot design procedure.ii The Hi assay, however, does non explicate the association between antigenic difference and genetic mutations. To accomplish this and model the development of influenza, genetic data of previous viruses should exist analyzed. The WHO has accumulated a multitude of information for this purpose. Since this data tin can now exist collected rapidly and economically, antigenic label of the flu virus based on its genetic material can enable early detection of emerging strains and increase influenza surveillance efficiency, thus enhancing flu vaccine strain selection.x
Our goal in this paper is to review the current tools and methods employed in predicting the evolution of influenza which might assistance when determining the composition of the seasonal flu vaccine. In Section2, we talk over the relevant literature about tools developed for visualizing the evolution of influenza. In Section3, we discuss the models proposed for predicting the evolution of influenza. Finally, in Section 4, nosotros provide insights and place areas in modeling the evolution of influenza that are open for improvement.
Tools for visualizing the evolution of influenza
In any given year, the item choice of vaccine strain plays a major role in determining vaccine efficacy so it is of critical importance to develop tools to clarify the ongoing evolution of influenza.12 A widely used tool to visualize the evolution of influenza is called a phylogenetic tree or evolutionary tree. In full general, these diagrams show inferred evolutionary relationships among biological entities based on similarities and differences in their genetic characteristics. Every foliage node in the tree represents a species, each edge denotes a relationship between 2 neighboring species, and the length of an border indicates the evolutionary distance betwixt species.thirteen
There have been many tools adult to generate and analyze phylogenetic trees,14-19 With respect to flu, Neher and Bedford12 designed an online visualization tool entitled nextflu that displays a phylogenetic tree of the most recent influenza virus sequences (see Fig. 2). This tool allows users to visualize many genetic and epidemiological features of flu strains and aids in the electric current vaccine strain option procedure. Bedford and Neher20 demonstrated the use of nextflu to analyze seasonal flu circulation patterns and provided projections for the 2017–2018 flu season. In a similar vein, Steinbrück and McHardy21 introduced allele dynamics plots (Advertising plots) every bit a method for visualizing the evolutionary dynamics of a gene. The Advertizing plot of a population-level sequence sample combines information from phylogenetic inference and ancestral character state reconstruction to identify the alleles that might take selective reward. Using this tool, Steinbrück and McHardy21 identified emerging strains of flu A (H3N2) and 2009 A (H1N1) pdm viruses.
nextFlu display of influenza phylogenetic tree.12
Some other method to visualize the development of flu is through antigenic cartography or antigenic maps.22 Antigenic cartography is currently used to clarify the global data from the WHO flu surveillance network as part of the flu vaccine strain option process (see Fig. three). Antigenic maps differ from genetic copse in that they are based on serology data that reflect the antigenic properties of pathogens (in this example as revealed by How-do-you-do assay based on ferret antisera). These maps can reveal long-term trends in the antigenic space leading to improved understanding of genetic and antigenic evolution.22
Antigenic cartography of circulating A (H3N2) viruses considered by the WHO in the 2017–18 Northern hemisphere influenza vaccine strain selection coming together.24 Virus clades are color-coded. There is no pregnant antigenic deviation betwixt the 3C2a clade in the brighter red color and the 3C2a1 clade in the darker ruddy colour as they cluster. The Hong Kong/4801/2014-egg virus is on the edge of that cluster, and the cluster is becoming more distinguishable from the earlier 3C3a viruses represented by A/Switzerland/9715293/2013 strain, which was a erstwhile vaccine strain. The WHO recommended the Hong Kong/4801/2014-like virus for the 2017–18 flu vaccine. (No modify from 2016–17 recommendation).
Antigenic characterization based on the Howdy analysis is a routine procedure for influenza vaccine strain selection. Even so, the HI assay but reveals the cantankerous reaction amid exam strains (antigens) and reference antisera (antibodies). Furthermore, antigenic characterization is usually based on multiple Hullo assays performed past different WHO collaborating centers. The combination of these datasets results in an incomplete HI matrix with many unobserved entries. Cai et al.23 developed a computational tool entitled AntigenMap for antigenic cartography construction from this incomplete matrix. Their approach commencement reconstructs the HI tables using matrix completion techniques, so generates the 2-dimensional antigenic cartography using multidimensional scaling. By applying this method to Hullo datasets containing influenza A (H3N2) viruses isolated between 1968 and 2003, Cai et al.23 identified xi clusters of antigenic variants, representing all major antigenic drift events during these 36 years.
Predicting the evolution of flu
Through the use of tools mapping the evolution of influenza, such equally phylogenetic trees and antigenic cartographs, statistical learning models have been proposed to predict the next evolutionary step of influenza. These models aim to combine serology data and genetic mutation information to explain antigenic difference. We review some of the well-nigh contempo studies in the literature that propose methods to inform strain selection for the seasonal influenza vaccine.
He and Deem25 synthetic poly peptide distance maps for the HA1 surface glycoprotein of the influenza 2009 A (H1N1) pdm virus. In detail, they applied multi-dimensional scaling26 to projection the 329-residue long amino acrid sequence of the HA1 poly peptide onto two dimensions. This mapping technique was as well used by Lapedes and Farber27 to project HI assay information onto lower dimensions. He and Deem25 then used kernel density estimation to detect the incipient clusters on the protein distance map.
Steinbrück and McHardy28 used nonnegative least-squares optimization to map pairwise antigenic distances onto the branches of a phylogenetic tree. This resulted in the inference of antigenic weights for the individual branches of the tree and allowed antigenic weights to exist determined for sets of coding changes on the surface glycoprotein hemagglutinin (HA). These weights contribute to identifying the antigenic touch on of HA alleles. Steinbrück et al.29 combined phylogenetic trees and AD plots to identify the HA alleles that are most likely to get predominant in the future seasons. They demonstrated how to predict the evolution of the influenza A (H3N2) virus using genetic and antigenic information from isolates sampled between 2002 and 2007. In this retrospective study, they identified the most suitable vaccine strain for the A (H3N2) virus past detecting antigenically novel HA alleles.
Neher et al.30 predicted the fitness of a virus from its genetic information. In their method, first, a genealogical tree for the virus population is constructed. The side by side step is based on the intuition that as long as differences in fitness values ascend from the accumulation of multiple mutations (i.e., antigenic drifts), the branching structure of the genealogical tree should showroom an observable imprint of the natural choice process as it unfolds. Using this insight and methods borrowed from statistical physics, Neher et al.30 analyzed the shape and branching pattern of the tree to identify the fettle of different strains relative to each other. They tested the proposed method using historical influenza A virus information. In xvi of the 19 years studied, the genealogical tree approach made meaningful predictions most which viruses were most likely to give rising to futurity epidemics.
Neher et al.31 proposed a phylogenetic tree-based model and a exchange model to explicate antigenic difference. The tree based model describes the How-do-you-do titer betwixt a test and a reference influenza strain as a sum of antigenic changes along the path connecting them in a phylogenetic tree. The commutation model explains Howdy titers as a sum of contributions associated with amino acrid substitutions between the reference and test viruses. Through numerical experiments using data from isolates sampled between 2002 and 2015, Neher et al.31 demonstrated that both the tree based model and the substitution model perform similarly in terms of prediction accuracy.
Łuksza and Lässig32 developed a fettle model to predict the evolution of flu by identifying changes in the frequencies of strain groups referred to as clades. They considered two major groups of mutations at the epitope and not-epitope regions of the virus' surface protein. Mutations at epitopes are likely to be benign to the virus, considering they modify the structural features targeted by host antibodies. Thus, a strain can accept better fitness than its competitors past being antigenically distinct. In contrast, mutations exterior epitope regions are often deleterious because they reduce protein stability or upset evolutionarily conserved viral functions Łuksza and Lässig32 used their model to predict frequencies of clades i year in the futurity with considerable accuracy. Their observations of how loftier and depression fettle clades evolve provide new perspectives on the evolution of influenza and can potentially contribute to the vaccine selection process.
Wilson and Cox33 suggested that a drift variant of epidemiologic importance usually contains at least four amino acid substitutions located at more than two of the epitope regions on the HA1 polypeptide. Lee and Chen34 showed that the number of amino acid changes in the 131 amino acid positions around the epitope sites had the highest correlation with the antigenic altitude and the all-time performance for predicting antigenic difference. Presumably non all 131 amino acrid positions around the epitope regions play a critical function in determining antigenicity, and thus immunodominant positions (i.eastward., major antibody-binding sites) should be identified using bioinformatics models.35 Liao et al.36 explored the apply of scoring methods, such as the construction of similarity classes and substitution matrices, to explain the antigenic differences of viruses' using genetic information. These methods considered polarity, accuse and molecular structure of the amino acids. Liao et al.36 employed statistical machine learning methods including iterative filtering, multiple and logistic regression, and support vector machines to quantify the antigenic issue of amino acid substitutions and identify immunodominant positions on the HA1 polypeptide. Similarly, Sun et al.10 utilized bootstrapped ridge regression and antigenic mapping to quantify antigenic difference between influenza strains based on the HA1 sequence information. Recently, Lee et al.37 adult a general purpose computational framework called DAMIP to discover cistron signatures that can predict vaccine immunity and efficacy.
The literature on predicting the evolution of influenza is rich and fast-growing. Seasons characterized by low vaccine effectiveness due to a mismatch betwixt the vaccine strains and the circulating strains, such as the 2014–15 influenza season,vi highlight the demand to utilize advanced tools and innovative approaches to improve the accuracy of the strain selection decisions. There is room for evolution in this field as we discuss in the next section.
Discussion
We have presented a detailed review of the near contempo and influential studies developing innovative methods for predicting the long-term evolution of influenza. The overarching goal of these studies is to inform and ameliorate strain selection for the seasonal flu vaccine. While much progress has been made, in that location are several areas that are still open for comeback.
When building statistical models for predicting the evolution of flu, out-of-sample performance is assessed to prevent overfitting. Notwithstanding, the number of candidate model features is usually large. For example, in an amino acid commutation model,31 , 34 there are 329 amino acrid residues on the HA1 polypeptide, and mutation in any one of these residues can potentially cause antigenic deviation. Selecting a minimal set of model features (e.g., amino acid residues) to ensure acceptable out-of-sample performance is a formidable chore that tin be addressed via feature choice methods developed in the machine learning literature.38–40
Many of the models in the literature assume rather simple relationships between genetic differences of flu strains (east.m., the number of amino acid substitutions around the epitope regions) and the extent of cantankerous-amnesty that they tin can induce in a host.5 However, antigenic assay has shown that only certain amino-acid changes near the epitope regions crusade antigenic difference.22 Current efforts to predict the development of influenza endeavor to map the serology data onto genetic sequences and phylogenetic trees.31 , 32 , 34 , 37 Occasionally, new strains, which are antigenically different from the circulating dominant strains, still fail to spread in the population although they have a higher probability of fixation.31 Therefore, incorporating serology data to consider empirically informed associations between viral genotypes and their antigenic characteristics in addition to genetic mutations may improve the predictive power of statistical models in detecting emerging strains.
Some other direction that is open for improvement pertains to the generation of serology data. Currently, the ferret postal service-infection antisera are used in antigenic characterization for influenza surveillance considering ferrets accept homo-like respiratory tracts.41 , 42 Despite this similarity, nevertheless, the process of ferret immunity is different from that of human being.8 In particular, the human and ferret antibodies elicited via the same process may target dissimilar epitopes of the influenza HA.43 Furthermore, ferrets with no prior influenza infections are used to produce reference antisera. In dissimilarity, human immunity is largely shaped by previous influenza infection and/or vaccination history.44 For instance, many recent studies have shown that people with repeated almanac vaccination had lower vaccine effectiveness than those who were not vaccinated in the prior season, suggesting that immunological education and pre-existing immunity affects seasonal vaccine effectiveness.45-48 Xie et al.8 have clearly demonstrated that human post-vaccination antisera responded differently than ferret post-infection antisera to H3 viruses. Therefore, more emphasis should be placed on human serologic testing in assessing vaccination-induced cross reactivity to improve influenza vaccine strain choice.
In summary, the seasonal influenza is a serious public health concern, causing significant human being suffering and economic burden. Due to dynamic evolution of the virus, the flu vaccine composition is reviewed annually past the WHO to ensure vaccine effectiveness. The overarching goal of this review procedure is to predict which strains will provide a thorough coverage as vaccine strains against prevalent circulating strains during the upcoming flu flavour. Predicting virus development is an essential interim step towards achieving this goal because one time new variants are predicted to emerge, their representative strains will be considered as candidate vaccine strains. If an antigenic mismatch happens to occur between the vaccine strains and the ones dominantly circulating in the population during the flu flavor, then a vaccine with significantly reduced effectiveness is even so distributed considering the vaccine production is extremely time consuming. We have discussed tools and methods used in the development of prediction models to help influenza vaccine strain selection. There are many opportunities for new innovative directions or for improvement of current methodologies. Such developments can potentially lead to more than effective prevention of flu worldwide saving lives, time and coin.
Disclosure of potential conflicts of interest
No potential conflicts of involvement were disclosed.
References
ane. Carrat F, Flahault A. Influenza vaccine: The challenge of antigenic drift. Vaccine. 2007;25:6852–62. doi:ten.1016/j.vaccine.2007.07.027 [PubMed] [CrossRef] [Google Scholar]
3. Beire B, Bauer B, Schweiger B. Differentiation of influenza B virus lineages Yamagata and Victoria past real-time PCR. J Clin Microbiol. 2010;48(4):1425–27. doi:10.1128/JCM.02116-09 [PMC free commodity] [PubMed] [CrossRef] [Google Scholar]
4. Grohskopf LA, Olsen SJ, Sokolow LZ, Bresee JS, Cox NJ, Broder KR, Karron RA, Walter EB. Prevention and control of seasonal flu with vaccines: Recommendations of the advisory committee on immunization practices (ACIP) – United States, 2014–15 influenza season. MMWR Morb Mortal Wkly Rep. 2014;63(32):691–vii. [PMC costless article] [PubMed] [Google Scholar]
5. Koelle K, Rasmussen DA. Influenza: Prediction is worth a shot. Nature. 2014;506(7490):47–49. doi:x.1038/nature13054 [PubMed] [CrossRef] [Google Scholar]
6. Dos Santos G, Neumeier E, Bekkat-Berkani R. Influenza: Can we cope better with the unpredictable?. Hum Vaccin Immunother. 2016;12(3):699–708. doi:10.1080/21645515.2015.1086047 [PMC complimentary article] [PubMed] [CrossRef] [Google Scholar]
8. Xie H, Wan X-F, Ye Z, Plant EP, Zhao Y, Xu Y, Li Ten, Finch C, Zhao North, Kawano T, et al.. H3N2 mismatch of 2014–15 Northern Hemisphere flu vaccines and head-to-head comparison between homo and ferret antisera derived antigenic maps. Sci Rep. 2015;five:15279. doi:x.1038/srep15279 [PMC free article] [PubMed] [CrossRef] [Google Scholar]
ten. Sun H, Yang J, Zhang T, Long P, Jia K, Yang G, Webby R, Wan X. Using sequence data to infer the antigenicity of flu virus. mBio. 2013;4(4):e00230–13. doi:x.1128/mBio.00230-13 [PMC free article] [PubMed] [CrossRef] [Google Scholar]
eleven. Özaltın OY, Prokopyev OA, Schaefer AJ. Optimal design of the seasonal influenza vaccine with manufacturing autonomy. Forthcoming at INFORMS Journal on Computing. 2017. [Google Scholar]
12. Neher R, Bedford T. nextflu: Real-time tracking of seasonal influenza virus evolution in humans. Bioinformatics. 2015;31(21):3546–48. doi:ten.1093/bioinformatics/btv381 [PMC free commodity] [PubMed] [CrossRef] [Google Scholar]
13. Choi JH, Jung HY, Kim HS, Cho HG. PhyloDraw: A phylogenetic tree drawing system. Bioinformatics. 2000;16(eleven):1056–58. [PubMed] [Google Scholar]
xiv. Gouy Thou, Guidon Due south, Gascuel O. SeaView version four: A multiplatform graphical user interface for sequence alignment and phylogenetic tree building. Mol Biol Evol. 2010;27(2):221–4. [PubMed] [Google Scholar]
15. Letunic I, Bork P. Interactive tree of life (iTOL): An online tool for phylogenetic tree display and annotation. Bioinformatics. 2006;23(ane):127–8. [PubMed] [Google Scholar]
16. Price MN, Dehal PS, Arkin AP. FastTree: Computing large minimum development trees with profiles instead of a distance matrix. Mol Biol Evol. 2009;26(7):1641–l. doi:10.1093/molbev/msp077 [PMC free commodity] [PubMed] [CrossRef] [Google Scholar]
17. Shimodaira H, Hasegawa One thousand. CONSEL: For assessing the confidence of phylogenetic tree option. Bioinformatics. 2001;17(12):1246–7. [PubMed] [Google Scholar]
18. Thorley JL, Page RD. RadCon: Phylogenetic tree comparison and consensus. Bioinformatics. 2000;16(5):486–7. [PubMed] [Google Scholar]
xix. Yang Z. PAML: A program package for phylogenetic analysis by maximum likelihood. Bioinformatics. 1997;13(5):555–6. [PubMed] [Google Scholar]
21. Steinbrück L, McHardy Air-conditioning. Allele dynamics plots for the report of evolutionary dynamics in viral populations. Nucleic Acids Res. 2011;39(one):e4. doi:x.1093/nar/gkq909 [PMC gratis article] [PubMed] [CrossRef] [Google Scholar]
22. Smith DJ, Lapedes Equally, Jong J, Bestebroer TM, Rimmelzwaan GF, Osterhaus A, Fouchier R. Mapping the antigenic and genetic evolution of influenza virus. Science. 2004;305(5682):371–6. [PubMed] [Google Scholar]
23. Cai Z, Zhang T, Wan X-F. A computational framework for flu antigenic cartography. PLoS Comput Biol. 2010;6(10):e1000949. doi:10.1371/journal.pcbi.1000949 [PMC free article] [PubMed] [CrossRef] [Google Scholar]
25. He J, Deem MW. Low-dimensional clustering detects incipient ascendant influenza strain clusters. Protein Eng Des Sel. 2010;23(12):935–46. doi:10.1093/protein/gzq078 [PMC free commodity] [PubMed] [CrossRef] [Google Scholar]
26. Everitt BS, Landau South, Leese M. Cluster Assay, fifth ed. London: John Wiley & Sons, Ltd; 2011. [Google Scholar]
27. Lapedes A, Farber R. The geometry of shape space: Application to influenza. J Theor Biol. 2001;212(i):57–69. doi:10.1006/jtbi.2001.2347 [PubMed] [CrossRef] [Google Scholar]
28. Steinbrück L, McHardy AC. Inference of genotype-phenotype relationships in the antigenic evolution of human being flu A (H3N2) viruses. PLoS Comput Biol. 2012;8(4):e1002492. doi:10.1371/periodical.pcbi.1002492 [PMC complimentary article] [PubMed] [CrossRef] [Google Scholar]
29. Steinbrück Fifty, Klingen TR, McHardy Air conditioning. Computational prediction of vaccine strains for human being influenza A (H3N2) viruses. J Virol. 2014;88(20):12123–32. doi:10.1128/JVI.01861-14 [PMC free article] [PubMed] [CrossRef] [Google Scholar]
30. Neher R, Russell C, Shraiman B. Predicting development from the shape of genealogical trees. eLife. 2014;iii:e03568. doi:10.7554/eLife.03568 [PMC costless commodity] [PubMed] [CrossRef] [Google Scholar]
31. Neher R, Bedford T, Daniels R, Russell C, Shraiman B. Prediction, dynamics, and visualization of antigenic phenotypes of seasonal flu viruses. Proc Natl Acad Sci. 2016;113(12):E1701–E1709. doi:10.1073/pnas.1525578113 [PMC free article] [PubMed] [CrossRef] [Google Scholar]
32. Łuksza M, Lässig 1000. A predictive fitness model for influenza. Nature. 2014;507(7490):57–61. doi:10.1038/nature13087 [PubMed] [CrossRef] [Google Scholar]
33. Wilson IA, Cox NJ. Structural basis of immune recognition of influenza virus hemagglutinin. Annu Rev Immunol. 1990;eight(1):737–87. doi:10.1146/annurev.iy.08.040190.003513 [PubMed] [CrossRef] [Google Scholar]
34. Lee Yard-S, Chen J. Predicting antigenic variants of influenza A/H3N2 viruses. Emerg Infect Dis. 2004;ten(8):1385. doi:10.3201/eid1008.040107 [PMC free commodity] [PubMed] [CrossRef] [Google Scholar]
35. Lee One thousand-S, Chen M-C, Liao Y-C, Hsiung CA. Identifying potential immunodominant positions and predicting antigenic variants of influenza A/H3N2 viruses. Vaccine. 2007;25(48):8133–39. doi:10.1016/j.vaccine.2007.09.039 [PubMed] [CrossRef] [Google Scholar]
36. Liao Y-C, Lee M-South, Ko C-Y, Hsiung CA. Bioinformatics models for predicting antigenic variants of influenza A/ H3N2 virus. Bioinformatics. 2008;24(4):505–12. [PubMed] [Google Scholar]
37. Lee EK, Nakaya How-do-you-do, Yuan F, Querec TD, Burel M, Pietz FH, Benecke BA, Pulendran B. Machine learning for predicting vaccine immunogenicity. Interfaces. 2016;46(five):368–ninety. [Google Scholar]
38. Guyon I, Elisseeff A. An introduction to variable and characteristic selection. J Mach Acquire Res. 2003;iii(Mar):1157–82. [Google Scholar]
39. Huang C-L, Wang C-J. A GA-based feature option and parameters optimization for support vector machines. Expert Syst Appl. 2006;31(2):231–40. [Google Scholar]
forty. Siedlecki Westward, Sklansky J. A note on genetic algorithms for large-scale feature selection. Pattern Recognit Lett. 1989;10(5):335–47. [Google Scholar]
41. Belser JA, Katz JM, Tumpey TM. The ferret as a model organism to written report influenza A virus infection. Dis Model Mech. 2011;4(v):575–9. doi:10.1242/dmm.007823 [PMC free commodity] [PubMed] [CrossRef] [Google Scholar]
42. Russell CA, Jones TC, Barr IG, Cox NJ, Garten RJ, Gregory V, Gust ID, Hampson AW, Hay AJ, Hurt AC, et al.. Flu vaccine strain selection and recent studies on the global migration of seasonal influenza viruses. Vaccine. 2008;26:D31–D34. [PubMed] [Google Scholar]
43. Lee M-Southward, Yang C-F. Cantankerous-reactive H1N1 antibody responses to a live attenuated influenza vaccine in children: implication for selection of vaccine strains. J Infect Dis. 2003;188(9):1362–6. doi:ten.1086/379045 [PubMed] [CrossRef] [Google Scholar]
44. Li Y, Myers JL, Bostick DL, Sullivan CB, Madara J, Linderman SL, Liu Q, Carter DM, Wrammert J, Esposito S, et al.. Immune history shapes specificity of pandemic H1N1 flu antibody responses. J Exp Med. 2013;210(8):1493–1500. doi:10.1084/jem.20130212 [PMC free commodity] [PubMed] [CrossRef] [Google Scholar]
45. Xie H, Li Fifty, Ye Z, Li X, Found EP, Zoueva O, Zhao Y, Jing X, Lin Z, Kawano T, et al.. Differential effects of prior influenza exposures on H3N2 cross-reactivity of homo postvaccination sera. Clin Infect Dis. 2017;65(two):259–67. doi:10.1093/cid/cix269 [PMC free article] [PubMed] [CrossRef] [Google Scholar]
46. McLean HQ, Thompson MG, Sundaram ME, Meece JK, McClure DL, Friedrich TC, Belongia EA. Affect of repeated vaccination on vaccine effectiveness against influenza A (H3N2) and B during eight seasons. Clin Infect Dis. 2014;59(10):1375–85. doi:10.1093/cid/ciu680 [PMC free article] [PubMed] [CrossRef] [Google Scholar]
47. Ohmit SE, Petrie JG, Malosh RE, Fry AM, Thompson MG, Monto AS. Influenza vaccine effectiveness in households with children during the 2012–2013 flavour: assessments of prior vaccination and serologic susceptibility. J Infect Dis. 2014;211(10):1519–28. doi:10.1093/infdis/jiu650 [PMC complimentary article] [PubMed] [CrossRef] [Google Scholar]
48. Saito N, Komori 1000, Suzuki Yard, Morimoto K, Kishikawa T, Yasaka T, Ariyoshi Yard. Negative affect of prior flu vaccination on current influenza vaccination among people infected and not infected in prior season: A test-negative example-control study in Nihon. Vaccine. 2017;35(4):687–93. [PubMed] [Google Scholar]
Manufactures from Man Vaccines & Immunotherapeutics are provided here courtesy of Taylor & Francis
Source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5861780/
0 Response to "The Flu Vaccine Is Constantly Reviewed and Reformulated Because"
Post a Comment