Influenza viruses are persistently threatening public health, causing annual epidemics and sporadic pandemics due to rapid viral evolution. Vaccines are used to prevent influenza infections but the composition of the influenza vaccines have to be updated regularly to ensure its efficacy. Computational tools and analyses have become increasingly important in guiding the process of vaccine selection. By constructing time-series training samples with splittings and embeddings, we develop a computational method for predicting suitable strains as the recommendation of the influenza vaccines using recurrent neural networks (RNNs). The Encoder-decoder architecture of RNN model enables us to perform sequence-to-sequence prediction. We employ this model to predict the prevalent sequence of the H3N2 viruses sampled from 2006 to 2017. The identity between our predicted sequence and recommended vaccines is greater than 98% and the [Formula: see text] indicates their antigenic similarity. The multi-step vaccine prediction further demonstrates the robustness of our method which achieves comparable results in contrast to single step prediction. The results show significant matches of the recommended vaccine strains to the circulating strains. We believe it would facilitate the process of vaccine selection and surveillance of seasonal influenza epidemics.

中文翻译：

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使用递归神经网络对甲型 H3N2 流感疫苗进行时间序列计算预测

流感病毒持续威胁着公众健康，由于病毒的快速进化，导致年度流行病和零星大流行。疫苗用于预防流感感染，但必须定期更新流感疫苗的成分以确保其功效。计算工具和分析在指导疫苗选择过程中变得越来越重要。通过构建具有拆分和嵌入的时间序列训练样本，我们开发了一种计算方法，用于使用循环神经网络 (RNN) 预测合适的菌株作为流感疫苗的推荐。RNN 模型的编码器-解码器架构使我们能够执行序列到序列的预测。我们使用该模型来预测 2006 年至 2017 年采样的 H3N2 病毒的流行序列。我们预测的序列和推荐的疫苗之间的同一性大于 98%，[公式：见正文]表明它们的抗原相似性。多步疫苗预测进一步证明了我们方法的稳健性，与单步预测相比，该方法获得了可比较的结果。结果显示推荐的疫苗毒株与流行毒株的显着匹配。我们相信这将促进疫苗选择和季节性流感流行监测的过程。结果显示推荐的疫苗毒株与流行毒株的显着匹配。我们相信这将促进疫苗选择和季节性流感流行监测的过程。结果显示推荐的疫苗毒株与流行毒株的显着匹配。我们相信这将促进疫苗选择和季节性流感流行监测的过程。