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Volume 40 Issue 2
April 2025
    Citation: Yousong Peng, Lei Yang, Weijuan Huang, Mi Liu, Xiao Ding, Xiangjun Du, Yuelong Shu, Taijiao Jiang, Dayan Wang. Evaluating the performance of the PREDAC method in flu vaccine recommendations over the past decade (2013-2023) .VIROLOGICA SINICA, 2025, 40(2) : 288-291.  http://dx.doi.org/10.1016/j.virs.2025.01.004
    shu

    Evaluating the performance of the PREDAC method in flu vaccine recommendations over the past decade (2013-2023)

    • a. Bioinformatics Center, College of Biology, Hunan Provincial Key Laboratory of Medical Virology, Hunan University, Changsha, 410082, China;

    • b. National Institute for Viral Disease Control and Prevention, China CDC, Beijing, 102206, China;

    • c. Jiangsu Institute of Clinical Immunology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China;

    • d. Suzhou Institute of Systems Medicine, Suzhou, 215123, China;

    • e. Department of Biostatistics and Systems Biology, School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, Guangdong, 510275, China;

    • f. Key Laboratory of Pathogen Infection Prevention and Control (MOE), State Key Laboratory of Respiratory Health and Multimorbidity, National Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 102629, China;

    • g. Guangzhou National Laboratory, Guangzhou, 510005, China

    • Highlights
      1. PREDAC is a semi-automated and user-friendly method for flu vaccine recommendations based on HA protein sequences.
      2. PREDAC performed comparably to the WHO in recommending vaccines of influenza A(H3N2) viruses during the period of 2013–2023.
      3. PREDAC can serve as a useful supplement to the traditional approach for flu vaccine recommendations.
      4. More intensive surveillance of antigenic variations in the influenza virus are needed to improve vaccine recommendations.
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    • Electronic Supplementary Material

      10.1016j.virs.2025.01.004-ESM.docx

      1. Du X, Dong L, Lan Y, Peng Y, Wu A, Zhang Y, Huang W, Wang D, Wang M, Guo Y, Shu Y, Jiang T. 2012. Mapping of H3N2 influenza antigenic evolution in China reveals a strategy for vaccine strain recommendation. Nat. Commun., 3: 709.

      2. Hayati M, Sobkowiak B, Stockdale JE, Colijn C. 2023. Phylogenetic identification of influenza virus candidates for seasonal vaccines. Sci. Adv., 9: eabp9185.

      3. Huddleston J, Bedford T. 2024. Timely vaccine strain selection and genomic surveillance improves evolutionary forecast accuracy of seasonal influenza A/H3N2. medRxiv. doi: 10.1101/2024.09.11.24313489.

      4. Jong SPJ, E. Nichols B, Anniek R, Parker E, Mitesser V, Happi C, Jong MD, X. Han A, Russell C. 2023. Global solidarity in genomic surveillance improves early detection of respiratory virus threats. medRxiv. 10.1101/2023.11.01.23297901.

      5. Liu M, Zhao X, Hua S, Du XJ, Peng YS, Li XY, Lan Y, Wang DY, Wu AP, Shu YL, Jiang TJ. 2015. Antigenic patterns and evolution of the human influenza a (H1N1) virus. Sci. Rep., 5: 14171.

      6. Malik S, Asghar M, Waheed Y. 2024. Outlining recent updates on influenza therapeutics and vaccines: a comprehensive review. Vaccine X, 17: 100452.

      7. Meng J, Liu J, Song W, Li H, Wang J, Zhang L, Peng Y, Wu A, Jiang T. 2024. PREDAC-CNN: predicting antigenic clusters of seasonal influenza A viruses with convolutional neural network. Briefings Bioinf., 25 :bbae033.

      8. Peng Y, Wu A, Meng J, Yang L, Wang D, Shu Y, Jiang T. 2020. Automated recommendation of the seasonal influenza vaccine strain with PREDAC. Biosafety and Health, 2: 117-119.

      9. Peng Y, Li X, Zhou H, Wu A, Dong L, Zhang Y, Gao R, Bo H, Yang L, Wang D, Lin X, Jin M, Shu Y, Jiang T. 2017. Continual antigenic diversification in China leads to global antigenic complexity of avian influenza H5N1 viruses. Sci. Rep., 7: 43566.

      10. Perofsky AC, Nelson MI. 2020. The challenges of vaccine strain selection. Elife, 9: e62955.

      11. Shah SAW, Palomar DP, Barr I, Poon LLM, Quadeer AA, McKay MR. 2024. Seasonal antigenic prediction of influenza A H3N2 using machine learning. Nat. Commun., 15: 3833.

      12. WHO. 2018. Recommended composition of influenza virus vaccines for use in the 2019 southern hemisphere influenza season. https://www.who.int/publications/m/item/recommended-composition-of-influenza-virus-vaccines-for-use-in-the-2019-southern-hemisphere-influenza-season. (Accessed 13 November 2024).

      13. WHO. 2019. Recommended composition of influenza virus vaccines for use in the 2019-2020 northern hemisphere influenza season. https://www.who.int/publications/m/item/recommended-composition-of-influenza-virus-vaccines-for-use-in-the-2019-2020-northern-hemisphere-influenza-season. (Accessed 13 November 2024).

      14. WHO. 2023. Recommended composition of influenza virus vaccines for use in the 2024 southern hemisphere influenza season. https://www.who.int/publications/m/item/recommended-composition-of-influenza-virus-vaccines-for-use-in-the-2024-southern-hemisphere-influenza-season. (Accessed 13 November 2024).

      15. Zhai K, Dong J, Zeng J, Cheng P, Wu X, Han W, Chen Y, Qiu Z, Zhou Y, Pu J, Jiang T, Du X. 2024. Global antigenic landscape and vaccine recommendation strategy for low pathogenic avian influenza A (H9N2) viruses. J. Infect., 89: 106199.

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      Evaluating the performance of the PREDAC method in flu vaccine recommendations over the past decade (2013-2023)

        Corresponding author: Yuelong Shu, yshu@cnic.org.cn
        Corresponding author: Taijiao Jiang, jiang_taijiao@gzlab.ac.cn
        Corresponding author: Dayan Wang, dayanwang@cnic.org.cn
      • a. Bioinformatics Center, College of Biology, Hunan Provincial Key Laboratory of Medical Virology, Hunan University, Changsha, 410082, China;
      • b. National Institute for Viral Disease Control and Prevention, China CDC, Beijing, 102206, China;
      • c. Jiangsu Institute of Clinical Immunology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China;
      • d. Suzhou Institute of Systems Medicine, Suzhou, 215123, China;
      • e. Department of Biostatistics and Systems Biology, School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, Guangdong, 510275, China;
      • f. Key Laboratory of Pathogen Infection Prevention and Control (MOE), State Key Laboratory of Respiratory Health and Multimorbidity, National Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 102629, China;
      • g. Guangzhou National Laboratory, Guangzhou, 510005, China
      • Received Date: 13 November 2024
      • Accepted Date: 24 January 2025

      Abstract: Highlights
      1. PREDAC is a semi-automated and user-friendly method for flu vaccine recommendations based on HA protein sequences.
      2. PREDAC performed comparably to the WHO in recommending vaccines of influenza A(H3N2) viruses during the period of 2013–2023.
      3. PREDAC can serve as a useful supplement to the traditional approach for flu vaccine recommendations.
      4. More intensive surveillance of antigenic variations in the influenza virus are needed to improve vaccine recommendations.

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