用环境数据+污水监测，提前预测儿童呼吸道合胞病毒住院高峰。
Prediction of Respiratory Syncytial Virus-Associated Hospitalizations Using Machine Learning Models Based on Environmental Data
- 融合污水病毒量、气象与空气质量数据，构建预测模型。
- 污水中病毒浓度是最高预测因子，高原地区住院率更高。
- 提供交互式仪表板，支持各州实时风险预警与决策。
呼吸道合胞病毒（RSV）是婴幼儿住院的主要病因，其流行受环境因素显著影响。本研究构建了机器学习框架，整合污水监测、气象与空气污染数据，预测美国每周的RSV相关住院率。数据包括周度住院率、污水中RSV水平、每日气象指标及污染物浓度。采用分类模型（如CART、随机森林、梯度提升）对住院风险等级（低风险、警报、疫情）进行预测。结果显示，污水中RSV水平为最强预测因子，其次为温度、臭氧和特定湿度等气象与空气质量变量。分析还发现，美洲原住民和阿拉斯加原住民群体的RSV相关住院率显著更高；高海拔地区因表面气压较低，住院率持续偏高。研究强调结合环境与社区监测数据在预测RSV暴发中的价值，可实现更及时的公共卫生干预与资源调配。为提升实用性，研究开发了交互式R Shiny仪表板（https://f6yxlu-eric-guo.shinyapps.io/rsv_app/），支持用户按州查看风险等级、可视化关键变量影响并生成疫情预报。
原文摘要 · Abstract (English)
Respiratory syncytial virus (RSV) is a leading cause of hospitalization among young children, with outbreaks strongly influenced by environmental conditions. This study developed a machine learning framework to predict RSV-associated hospitalizations in the United States (U.S.) by integrating wastewater surveillance, meteorological, and air quality data. The dataset combined weekly hospitalization rates, wastewater RSV levels, daily meteorological measurements, and air pollutant concentrations. Classification models, including CART, Random Forest, and Boosting, were trained to predict weekly RSV-associated hospitalization rates classified as \textit{Low risk}, \textit{Alert}, and \textit{Epidemic} levels. The wastewater RSV level was identified as the strongest predictor, followed by meteorological and air quality variables such as temperature, ozone levels, and specific humidity. Notably, the analysis also revealed significantly higher RSV-associated hospitalization rates among Native Americans and Alaska Natives. Further research is needed to better understand the drivers of RSV disparity in these communities to improve prevention strategies. Furthermore, states at high altitudes, characterized by lower surface pressure, showed consistently higher RSV-associated hospitalization rates. These findings highlight the value of combining environmental and community surveillance data to forecast RSV outbreaks, enabling more timely public health interventions and resource allocation. In order to provide accessibility and practical use of the models, we have developed an interactive R Shiny dashboard (https://f6yxlu-eric-guo.shinyapps.io/rsv_app/), which allows users to explore RSV-associated hospitalization risk levels across different states, visualize the impact of key predictors, and interactively generate RSV outbreak forecasts.
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