Multi-faceted analysis and prediction for the outbreak of pediatric respiratory syncytial virus
Multi-faceted analysis and prediction for the outbreak of pediatric respiratory syncytial virus
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DOI：
10.1093/jamia/ocad212
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发表时间：
2023-11-02
影响因子：
6.4
通讯作者：
Sun，Jimeng
中科院分区：
文献类型：
--
作者：
Yang，Chaoqi;Gao，Junyi;Sun，Jimeng
目的呼吸道合胞病毒（RSV）是儿科住院的重要原因。本文的目的是利用multisbranddata和利用张量方法来揭示不同的RSV地理集群，并制定一个准确的RSV预测模型为未来seasons.Materials和MethodsThis研究利用5年的RSV数据来源，包括医疗索赔，CDC监测数据，谷歌搜索趋势。我们进行时空张量分析和预测儿科RSV在美国通过设计（i）一个非负张量因子分解模型的儿科RSV疾病和位置聚类;（ii）和一个循环神经网络张量回归模型县级趋势预测使用的疾病和位置features.ResultsWe确定一个聚类层次的儿科疾病：从独立来源中确定了RSV暴发的三个常见地理集群，显示RSV在美国不同地区的年度趋势转移，从南部和东南部地区转移到中部和东北部地区，然后转移到西部和西北部地区，降水量和温度分别是决定系数R2的相关因子。我们的回归模型准确地预测了2022-2023年县一级的RSV季节，R2平均绝对误差MAE < 0.4，Pearson相关性大于0.75，显著优于基线，P值<0.05。结论我们提出的框架提供了对美国RSV疾病的全面分析，使医疗保健提供者能够更好地为潜在的爆发做好准备，预测对服务和用品的需求增加，并通过及时干预挽救更多生命。
ObjectivesRespiratory syncytial virus (RSV) is a significant cause of pediatric hospitalizations. This article aims to utilize multisource data and leverage the tensor methods to uncover distinct RSV geographic clusters and develop an accurate RSV prediction model for future seasons.Materials and MethodsThis study utilizes 5-year RSV data from sources, including medical claims, CDC surveillance data, and Google search trends. We conduct spatiotemporal tensor analysis and prediction for pediatric RSV in the United States by designing (i) a nonnegative tensor factorization model for pediatric RSV diseases and location clustering; (ii) and a recurrent neural network tensor regression model for county-level trend prediction using the disease and location features.ResultsWe identify a clustering hierarchy of pediatric diseases: Three common geographic clusters of RSV outbreaks were identified from independent sources, showing an annual RSV trend shifting across different US regions, from the South and Southeast regions to the Central and Northeast regions and then to the West and Northwest regions, while precipitation and temperature were found as correlative factors with the coefficient of determinationR2, respectively. Our regression model accurately predicted the 2022-2023 RSV season at the county level, achievingR2mean absolute error MAE < 0.4 and a Pearson correlation greater than 0.75, which significantly outperforms the baselines withP-values <.05.ConclusionOur proposed framework provides a thorough analysis of RSV disease in the United States, which enables healthcare providers to better prepare for potential outbreaks, anticipate increased demand for services and supplies, and save more lives with timely interventions.